It is widely discussed that growth of GDP has a vague impact on environmental pollution due to the emissions of carbon dioxide from consumed fossil fuels in production, transportation and power generation, and creation of required instruments to control environmental pollution as well. Due to the fact that environmental pollution can be a reason of mortality increase in a society, the main purpose of this study is to investigate the relationship between economic growth, fossil fuel consumption, mortality (from cardiovascular disease, cancer, diabetes and chronic respiratory disease) and environmental pollution using Generalized Method of Moments (GMM) estimation technique in the case of CIS member states over the period of 1993-2018. The major results revealed CO2 emissions in in the region of CIS have a positive effect on increase of mortality from cardiovascular disease, cancer, diabetes and chronic respiratory disease. In regards to fossil fuel consumption, the estimation proved that this variable affects mortality from cardiovascular disease, cancer, diabetes and chronic respiratory disease positively. In addition, we found effect of economic growth, population growth and inflation rate positively, while any improvement in human development index (HDI) has a negative effect on increase of mortality from cardiovascular disease, cancer, diabetes and chronic respiratory disease in the CIS region. It is recommended that the CIS member states carry out different policies to improve energy transition indicating movement from fossil fuel energy sources to renewable ones. Moreover, we recommend the CIS member states to enhance various policies for easy access to the electricity from green sources and increase of renewable supply through improved technologies, sustainable economic growth, and increase of using green source in daily social life.

It is common knowledge that estimating the height component of GNSS stations in general is much more problematic than estimating the horizontal position. Many different effects, such as tectonic signals, non-tectonic signals, atmospheric delay, noise, etc., are known to affect the height component of GNSS stations more than the horizontal component. However, the height component of GNSS stations is still poorly estimated. In this study, the height time series of 37 continuous GNSS stations covering the 2014–2019 date range is used from the Turkish National Permanent GNSS Network-Active (TUSAGA-Active). Since it is easier to interpret the effects of the height component due to its topographic features and seasonal changes being more effective than in the rest of the country, stations were chosen in the Eastern Anatolia region of Turkey. The daily coordinates of the GNSS stations were obtained as a result of the GAMIT/GLOBK software solution. By applying time series analysis to the daily coordinate values of the stations, statistically significant trends, periodic and stochastic components of the stations were determined. As a result of the analysis, the vertical velocities of the GNSS stations and the standard deviations of the vertical velocities were determined. Furthermore, when the height components of continuous GNSS stations were examined, it was seen that there were seasonal effects, and it was investigated whether the height components were related to meteorological parameters. For that, simple linear regression analysis was performed to determine how dependent the height components of the continuous GNSS stations were on meteorological parameters. As a result of the analysis, the height components of the continuous GNSS stations are dependent on meteorological parameters such as temperature, pressure, relative humidity, wind speed, and precipitation. In addition, height component time series analysis of continuous GNSS stations was performed by using Autoregressive Moving Average (ARMA) models from linear time series methods. As a result of the study, the performance of the ARMA modeling results again indicated the dependence of the height component of the continuous GNSS stations on the meteorological parameters.

This paper proposes a high-performance and low-cost in-flight collision avoidance system based on background subtraction for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) dynamic background subtraction to remove the background and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) Euclidean clustering to cluster the moving object and to remove noise blobs, (iv) distinguish independent objects and track the movement using Kalman filter, and (v) collision avoidance using proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace high vision system such as event camera. The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via 2-D transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the 3-D trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a quadrotor UAV test vehicle, and appropriate algorithm parameters for various requirements are deduced.

This paper presents a methodology for indirectly estimating the scale of seabird bycatch using existing data, eliminating the need for regular monitoring. The study focuses on five species of ducks that winter in the Polish waters of the Baltic Sea and are highly susceptible to bycatch: Long-tailed Duck, Velvet Scoter, Common Scoter, Greater Scaup, and Tufted Duck. The evaluation units used are divided into the Bornholm and Gotland Groups within the Polish Exclusive Economic Zone (PEEZ). The analysis acknowledges the lack of data for certain areas known to have high concentrations of diving birds and fishing activity. The population sizes of waterbirds were assessed using ship-based surveys and a distance sampling approach. Fishing effort data from the winter seasons between 2015/2016 and 2019/2020 were analyzed. Bycatch rates were estimated based on surveys conducted in previous seasons, and threshold values for bycatch were established using the concept of "small numbers" defined in EU directives. The results show that within the Gotland subdivision of the PEEZ, the estimated average abundance of all diving waterbirds was 207,114 individuals, with an average bycatch of 7,921 birds (3.8% of the total). The Velvet Scoter was the most abundant species, followed by the Long-tailed Duck. Benthivorous ducks constituted the majority of the bird population in the area. In the Bornholm subdivision, the estimated average abundance of diving waterbirds was 600,845 individuals, with an average bycatch of 5,056 birds (0.8% of the total). The Long-tailed Duck was the most numerous species, followed by the Velvet Scoter. Benthivorous ducks also dominated in this area. The findings highlight the need for effective management and conservation measures to address the high mortality of seabirds due to bycatch. The methodology presented in this study offers a valuable approach for estimating bycatch scale and can support efforts to minimize the impact on seabird populations in the Baltic Sea.

The European pear is a climacteric fruit species characterized by high storability. 'Conference', a pear cultivar very popular on the market, can be stored in a CA room without major losses for half a year. However, for ‘Conference’ storage to be successful, the fruit must be harvested fully developed even before it enters the climatic maturation phase. Harvested too early or too late, pears will taste bad, have physiological disorders and will be poorly storable. Among the various methods used to determine the optimal harvest date, the degree-days method, which relies of the sum of active temperatures, seems to be relatively accurate and easy to apply. During 11 years of study, ‘Conference’ pears were harvested on 4 dates (every 4-5 days) chosen based on measurements and observations, and then, after a six-month storage period, the optimum harvest date was determined. During the growing season, temperature was measured using an automatic weather station to calculate the sum of active temperatures from full bloom to the optimal harvest date. Measurements made after storage were analysed and the sum of active temperatures needed for the proper development of ‘Conference’ pear was calculated to be 2469 degree days. The calculations are very accurate because the dispersion calculated using the standard deviation was only 20 °. The base temperature with the smallest error was 0 °. On the basis of the study results, this method can be recommended as useful for determining the optimal harvest date.

In systems biology, biological phenomena are often modeled by ODE and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulations, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the SBSCL, and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code available from https://github.com/TakahiroYamada/SBMLWebApp. The program runs online at http://simulate-biology.org.

Peroxiredoxins play central roles in the detoxification of reactive oxygen species and have been modelled across multiple organisms using a variety of kinetic methods. However, the peroxiredoxin dimer-to-decamer transition has been underappreciated in these studies despite the 100-fold difference in activity between these forms. This is due to the lack of available kinetics and theoretical framework for modelling this process. Using published isothermal titration calorimetry data, we obtained association and dissociation rate constants of 93.0 µM-4·s-1 and 102 s-1, respectively, for the dimer-decamer transition of human PRDX1. We developed an approach that greatly reduces the number of reactions and species needed to model the peroxiredoxin decamer oxidation cycle. Using these data, we simulated horse radish peroxidase competition and NADPH-oxidation linked assays and found that the dimer-decamer transition had an inhibition-like effect on peroxidase activity. Further, we incorporated this dimer-decamer topology and kinetics into a published and validated in vivo model of PRDX2 in the erythrocyte and found that it almost perfectly reconciled experimental and simulated responses of PRDX2 oxidation to hydrogen peroxide insult. By accounting for the dimer-decamer transition of peroxiredoxins, we were able to resolve several discrepancies between experimental data and available kinetic models.

This manuscript presents a novel approach to estimating the potential within Schrödinger equations, with a particular focus on applications to the Navier-Stokes problem in fluid dynamics. By establishing new theoretical estimates, we delve into the intricate dynamics of fluid flow, aiming to unveil previously obscured aspects of the Navier-Stokes equations. Through a rigorous mathematical framework, we explore how these new potential estimates can provide a fresh perspective on fluid mechanics, contributing to the ongoing quest to solve some of its most persistent challenges.

The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution Convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.

In this work, in spite of Milne’s recommendation using the three-point Newton–Cotes open formula (Milne’s rule) as a predictor rule and three-point Newton–Cotes closed formula (Simpson’s rule) as a corrector rule for 4-th differentiable functions with bounded derivatives. There is still a great need to introduce such formulas in other Lp spaces. Often, we need to approximate real integrals under the assumptions of the function involved. Because of that, the aim of this work is to introduce several Lp error estimates for the proposed perturbed Milne’s quadrature rule. Numerical experiments showing that our proposed quadrature rule is better than the classical Milne rule for certain types of functions are provided as well.

Regression models are mostly used in all fields of sciences for modelling the relationship between a dependent variable and independent variable(s). The least square method is often used to estimate the parameters in a linear model because it is the best linear unbiased estimator. These estimates can only be reliable if the assumption of normality is satisfied. In some cases the dependent variable might be bimodal and shows a non-linear relationship with the independent variable(s). In this case, a non-linear model should be used. In non-linear model, the standard errors are often obtained by linearizing the nonlinear function around the parameter, assuming central limit theorem. After the linearization, the least square parameter estimates are obtained. It should be noted that the error of the non-linear model is different from that of the transformed linear model. Thus, there is a need to transform back to the original non-linear model. In this note, a novel non-linear function was developed into a non-linear regression model, called Normal-Power model. The least square method was used to estimate the parameter of the transformed model. Its usefulness in regression model was demonstrated using real data of Nigeria Economy-Tourism model.

To validate the accuracy and reliability of onboard sensors for object detection and localization in driver assistance, as well as autonomous driving applications under realistic conditions (indoors and outdoors), a novel tracking system is presented. This tracking system is developed to determine the position and orientation of a slow-moving vehicle (e.g. car during parking maneuvers), independent of the onboard sensors, during test maneuvers within a reference environment. One requirement is a 6 degree of freedom (DoF) pose with a position uncertainty below 5 mm (3σ), an orientation uncertainty below 0.3° (3σ) at a frequency higher than 20 Hz, and a latency smaller than 500 ms. To compare the results from the reference system with the vehicle’s onboard system, a synchronization via Precision Time Protocol (PTP) and a system interoperability to Robot Operating System (ROS) is implemented. The developed system combines motion capture cameras mounted in a 360° panorama view set-up on the vehicle with robotic total stations. A point cloud of the test site serves as a digital twin of the environment, in which the movement of the vehicle is simulated. Results have shown that the fused measurements of these sensors complement each other, so that the accuracy requirements for the 6 DoF pose can be met, while allowing a flexible installation in different environments.

This paper reviews the management of the COVID-19 crisis and the difficulty of cost estimation model, comparing centralized management or bureaucratic government coaction and the agile market alternative or spontaneous social coordination. This is a study of Political Economy and Health Economics from the perspective of Austrian Economics. We describe and compare the al-ternative models, which are adapted to the current crisis. The analysis is based on the theorem of the impossibility of the economic calculation under coactive systems, and other principles of economy. In this context we pay also attention to collateral problems of the centralized and coac-tive management. Finally we propose a solution based on dynamic efficiency and the constitutions of wellbeing economics

Since December 2019, the coronavirus disease (COVID-19) has rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, the authors use statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national level in Mexico. Two types of models are proposed: first, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model is used to estimate the middle and the end of the outbreak. Model selection will be performed using Vuong’s test. The proposed models show overall fit similar to predictive models (e.g. time series, and machine learning); however, the interpretation of parameters is less complex for decision-makers and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue.

In recent years, since the propagation channel characteristics have been effectively used for applications such as motion sensing, position detection, etc. A great deal of attention is attracted to channel sounding methods easy to utilize using low-cost devices. This paper presents a device-free indoor location estimation method using spatio-temporal features of radio propagation channels using the 2.4-GHz band 3-by-3 MIMO channel sounder developed using commodity wireless LANs. The measurement results demonstrated a reasonable performance of the proposed method with small number of antennas.

Owing to the time-varying characteristics and nonlinearities of industrial processes, control has higher difficulties and results in challenges for advanced technology. In this paper, a self-tuning controller that includes a nonlinear proportional-integral-derivative (NPID) control function as well as a self-tuning function is proposed for first-order plus time delay (FOPTD) process control. The NPID control function is implemented using the nonlinear PID controller whose optimum parameters are adapted by a neural network (NN). The self-tuning function is able to identify the process dynamics using a short period of process behavior and tune NPID parameters based on the identified parameters. The advantage of the proposed method is validated with a set of simulation works on three processes and the comparison results are presented.

We propose a Gauss model (GM), a map from time to the bell-shaped Gauss function to model the deaths per day and country, as a quick and simple model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e. initial exponential spread to eventual saturation, we apply the GM to existing data, as of April 2, 2020, from 25 countries during first corona pandemic wave and study the model's predictions. We find that logarithmic daily fatalities caused by Covid-19 are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical chi2-fit with 95\% confidence, we are able to obtain the characteristic parameters of the GM, i.e. a width, peak height and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced.

Structural displacement is an important metric for assessing structural conditions because it has a direct relationship with the structural stiffness. Many bridge displacement measurement techniques have been developed, but most methods require fixed reference points in the vicinity of the target structure which limits field implementations. A promising alternative is to use reference-free measurement techniques that indirectly estimate the displacement by using measurements such as acceleration, and strain. This paper proposes novel reference-free bridge displacement estimation by the fusion of single acceleration with pseudo-static displacement derived from co-located strain measurements. First, we propose a conversion of the strain at the center of a beam into displacement based on the geometric relationship between strain and deflection curves with reference-free calibration. Second, an adaptive Kalman filter is proposed to fuse the displacement generated by strain with acceleration by recursively estimate the noise covariance of displacement from strain measurements which is vulnerable to measurement condition. Both numerical and experimental validations are presented to demonstrate the efficiency and robustness of the proposed approach.

Few switching transitions in high power and medium voltage application of Power converters are desirable. The selective harmonics elimination (SHE) pulse width modulation offers a better quality waveform with lower switching transitions and hence lower switching losses. The SHE is a pre-programmed modulation technique where certain amounts of lower order harmonics are removed and fundamental voltage is controlled. After Fourier analysis of output waveform, a set of nonlinear transcendental equations is obtained which exhibits, multiple, unique or no solution in different range of modulation index (MI). In this paper, an iterative method based on the Jacobian estimate is proposed to solve a highly non-linear set of SHE equations. The proposed technique is easy in implementation and can solve a large number of such equations as computation of the Jacobian matrix in the subsequent iteration is estimated from the previous values. Moreover, the proposed method also removes the singularity problem, especially for large SHE equations. High accuracy in the initial guess is also not essential for this method and can converge to the solution with any random initial guess. The computational and simulation results are given to validate the concept. The hardware result is provided to confirm the computational and simulation results.

The COVID-19 infections in Pakistan are spreading at an exponential rate and a point may soon be reached where rigorous prevention measures would need to be adopted. Mathematical models can help define the scale of an epidemic and the rate at which an infection can spread in a community. I used ARIMA Model, Diffusion Model, SIRD Model and Prophet Model to forecast the magnitude of the COVID-19 pandemic in Pakistan and compared the numbers with the reported cases on the national database. Results depicts that Pakistan could hit peak number of infectious cases between June 2020 and July, 2020.

Based on collected power generation data associated with solar irradiance, PV system conversion efficiency and cell temperature, an hourly solar PV power estimation model by a parallel artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. Weight matrices related to different seasons and geographic areas for estimation power generations have been trained by real measured operation data. The parallel PSO algorithm with heuristic global optimization technique assists the ANN training process to get near optimal solutions precisely. The accuracy and reliability of estimation is audited by actual details of photovoltaic power stations in various regions and scales. The results of estimation model can not only assist the electricity dispatcher to explicitly monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet load demand more accurately. The presented model will bring benefits to power dispatching for larger scales of intermittent and unstable solar power generation.

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player’s training and ludology experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need of generating comprehensible visual representations from different sets of data, in this paper we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

Lysine is the first limiting essential amino acid in rice because it is present in the lowest quantity compared to all the other amino acids. Amino acids are the building block of proteins and play an essential role in maintaining the human body’s healthy functioning. Rice is a staple food for large proportion of the global population, thus increasing the lysine content in rice will improve its nutritional value. In this paper, we studied the lysine biosynthesis pathway in rice (Oryza Sativa) to identify the regulators of the lysine reporter gene LYSA (LOC_Os02g24354). Genetically intervening at the regulators has the potential to increase the overall lysine content in rice. We modeled the lysine biosynthesis pathway in rice seedlings under normal and saline (NaCl) stress conditions using Bayesian networks. We estimated the model parameters using experimental data and identified the gene DAPF(LOC_Os12g37960) as a positive regulator of the lysine reporter gene LYSA under both normal and saline stress conditions. Based on this analysis, we conclude that the gene DAPF is a potent candidate for genetic intervention. Upregulating DAPF using methods such as CRISPR-Cas9 has the potential to upregulate the lysine reporter gene LYSA and increase the overall lysine content in rice.

Classically the walk in biped robotics was obtained controlling balance during the whole step, i.e. guaranteeing that the pressure point under the soles always stayed in the polygon of the supporting feet. However, this assumed that the feet were able to transfer torque to the ground during the whole gait cycle. In spite of the fact that the amount of transferrable torque in the feet-ground contact is limited, it is possible only during some phases of the step, and the overall process is energetically inefficient. On the other side, starting from the passive motion of the rimless wheel falling on an inclined surface, and ending to the inverted pendulum with a compass, balance in the whole was proven in spite of dynamical instability inside each step. Along this line results of Foot Placement Estimation (FPE) in 2-D and 3-D showed how energy efficient walk was possible, emulating the human walk with a free fall on the swing foot and energy restitution at the foot collision with the ground for the next step. This model assumes pointy feet, so without torque transfer to the ground. In the realm of FPE, in previous papers the present author adopted the 3-D inverted pendulum in polar coordinates (Spherical Inverted Pendulum - SIP) to introduce omnidirectional walks with arbitrarily changing characteristics. No torque control was used during the step, i.e the pendulum was always in free fall at each step, the only control actions were at the beginning of the next step. These actions are: the change of angular velocities at the start of a new step, with respect to those given after the collision (emulating the torque action in the brief double stance period), to recover for the losses, and the preparation of the position in the frontal and sagittal planes of the swing foot for the next collision. The present paper improves this paradigm, proposing a general model to account for all characteristics of the biped and of the gait, with adding a minimum of dynamical complexity with respect to the SIP. This model allows, not only to walk omnidirectionally on a flat surface, but also to go up and down staircases.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in multipath fading time-varying channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

Estimating the parameters of statistical distributions generally involves solving a system of nonlinear equations or a nonlinear equation, being a technical difficulty in their usual application in hydrology. This article presents improved approximations and, in some cases, new approximations for the estimation with the method of ordinary moments and the method of linear moments, which are useful for the direct calculation of the parameters, because the errors in the approximate estimation are similar to the use of iterative numerical methods. Thirteen statistical distributions of two and three parameters frequently used in hydrology are presented, for which parameter estimation was laborious. Thus, the approximate estimation of the parameters by the two methods is simple but also precise and easily applicable by hydrology researchers. The approximate forms characterized by very small errors are the result of the research conducted within the Faculty of Hydrotechnics to update the romanian normative standards in the hydrotechnical field.

Calibrating ecological models or making decisions with them is an optimisation problem with challenging methodological issues. Depending on the optimisation formulation, there may be a large variety of optimisation configurations (e.g. multiple objectives, constraints, stochastic criteria) and finding a single acceptable solution may be difficult. The challenges are exacerbated by the high computational cost and the non linear or elusive mathematical properties that increased with the complexity of numerical models. From the feedbacks of practitioners, the need for a guideline for conducting optimisation of complex models has emerged. In this context, we propose a practical guide for the complex model optimisation process, covering both calibration and decision-making. The guide sets out the workflow with recommendations for each step based on existing tools and methods usually scattered throughout the literature. This guide is accompanied with an ODDO template (Overview, Design, Details of Optimisation) to standardise the published description of model-based optimisation and suggests research directions.

This paper compares the finite sample performance of three non-parametric threshold estimators via Monte Carlo method. Our results show that the finite sample performance of the three estimators is not robust to the relative position of the threshold level along the distribution of threshold variable, especially when a structural change occurs at the tail part of the distribution.

This paper addresses the issues of how we can quantify structural information for nonparametric distributions and how we can detect its changes. Structural information refers to an index for a global understanding of a data distribution. When we consider the problem of clustering using a parametric model such as a Gaussian mixture model, the number of mixture components (clusters) can be thought of as structural information in the model. However, there does not exist any notion of structural information for nonparametric modeling of data. In this paper we introduce a novel notion of {\em kernel complexity} (KC) as structural information in the nonparametric setting. The key idea of KC is to combine the information bias inspired by the Gini index with the information quantity measured in terms of the normalized maximum likelihood (NML) code length. We empirically show that KC has a property similar to the number of clusters in a parametric model. We further propose a framework for structural change detection with KC in nonparametric distributions. With synthetic and real data sets we empirically demonstrate that our framework enables us to detect structural changes underlying the data and their early warning signals.

In the design of multi-loop Networked Control Systems (NCSs) wherein each control system is characterized by heterogeneous dynamics and associated with certain set of timing specifications and constraints, appropriate metrics need to be employed for the synthesis of control and networking policies to efficiently respond to the requirements of each control loop. Majority of the design approaches for sampling, scheduling and control policies include either time-based or event-based metrics to perform pertinent actions in response to the changes of the parameters of interest. We specifically focus in this article on Age-of-Information (AoI) as a recently-developed time-based metric and threshold-based triggering function as a generic event-based metric. As the NCS model, we consider multiple heterogeneous stochastic linear control systems that close their feedback loops over a shared-resource communication network. We investigate the co-design across the NCS, and discuss the pros and cons with AoI and ET approaches in terms of asymptotic control performance measured by linear-quadratic Gaussian (LQG) cost functions. In particular, sampling and scheduling policies combining AoI and stochastic event-triggered metrics are proposed. It is argued that pure AoI functions that generate decision variables solely upon minimizing the average age irrespective of control systems dynamics may not be able to improve the overall NCS performance even compared with pure randomized policies. Our theoretical analyses are successfully validated through several simulation scenarios.

Relying on Random Utility Theory (RUT) as the guiding mechanism for the Data Generating Process (DGP), this paper uses households consumption choices on cooking fuel, drinking water, and sanitation from the 2014 United States Agency for International Development's (USAID) Demographic and Health Survey (DHS) data on Burkina Faso, to characterize and investigate the inter-linkages between health consciousness and environmental consciousness, and their relationship with wealth in a low income country context. We achieve this by specifying sequentially three econometric modeling frameworks: the first one being independent binary probit (IBP) models to describe each choice process, followed by a fully parametric trivariate probit (FPTP) model to account for choice dependency, and finally by a semi-parametric trivariate probit (SPTP) model to further relax the linearity assumption. Based on the Akaike Information criteria (AIC) and the estimated Trivariate model correlation coefficients, the SPTP framework is found to be the best specification for describing the observed consumption behaviors. The results show that increased wealth level raises households health and environmental consciousness, while leaving the relative preference ordering over the elements in the household consumption basket unchanged.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as Oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

This study presents the numerical investigation of bioconvective nanofluid (NF) flow compris-ing gyrotactic microorganisms through convective boundary conditions (CBC) enclosing heat and mass transmission analysis above an inclined extending cylinder. Via appropriate transfor-mation, the set of Partial Differential Equations is transformed into a scheme of nonlinear ODEs. A new set of variables are presented in the directive to get the first-order convectional equations and, then solved numerically by using bvp4c. The influence of several factors on velocity, ener-gy, concentration, and the density of motile microorganisms (DMM) is investigated and exam-ined. The analysis describes and addresses all physical measures of concentration such as Skin Friction (SF), Sherwood number (SN), DMM, and Nusselt number (NN). The present conse-quences display a validation with published. For confirmation of bvp4c, the ND-solve approach is also applied. Error analysis is also examined for the confirmation of the mathematical model.

The Direction-of-Arrival (DoA) estimation methods are highly versatile and find extensive applications in satellite communication. The DoA methods are employed across a range of orbits, from Low Earth Orbits (LEO) to Geostationary Earth Orbits (GEO). They serve multiple applications, including altitude determination, geolocation and estimation accuracy, target localization, and relative and collaborative positioning. This paper presents a novel approach for modeling the DoA angle using a closed-form expression, incorporating the boresight angle and satellite and Earth station position data. The method uses the geographic coordinate system in the satellite communication system, precisely the latitude and longitude of the Earth station and altitude parameters of the satellite stations, to calculate the Earth station’s elevation angle and accurately model the DoA angle. Furthermore, this paper performs a comprehensive comparative analysis of various DoA methods to gain deeper insights into the performance of DoA estimation in multi-antenna systems operating under spatially correlated channels. Accordingly, this paper evaluates DoA estimation performance using root-mean-square-error (RMSE) statistics for uplink and downlink conditions through extensive Monte Carlo simulations. The simulation’s effectiveness is validated against the Cramer-Rao Bound (CRB) performance metric for the Additive white Gaussian noise (AWGN) case. (i.e., thermal noise). The simulation results demonstrate improved RMSE performance in satellite systems by incorporating spatial correlation into the system model.

We present a finite element model updating technique for soil-structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil-structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring and diagnosis of civil structures.

We examine how observations can be used to evaluate an air quality analysis by verifying against passive observations (i.e. cross-validation) that are not used to create the analysis and we compare these verifications to those made against the same set of (active) observations that were used to generate the analysis. The results show that both active and passive observations can be used to evaluate of first moment metrics (e.g. bias) but only passive observations are useful to evaluate second moment metrics such as variance of observed-minus-analysis and correlation between observations and analysis. We derive a set of diagnostics based on passive observation–minus-analysis residuals and we show that the true analysis error variance can be estimated, without relying on any statistical optimality assumption. This diagnostic is used to obtain near optimal analyses that are then used to evaluate the analysis error using several different methods. We compare the estimates according to the method of Hollingsworth Lonnberg, Desroziers, a diagnostic we introduce, and the perceived analysis error computed from the analysis scheme, to conclude that as long as the analysis is optimal, all estimates agrees within a certain error margin. The analysis error variance at passive observation sites is also obtained.

The Stepped Frequency Waveform (SFW) is commonly employed in radar technology to synthesize wideband signals through the aggregation of narrow-band pulses, thereby achieving high-resolution range profiles without the need to increase the radar's instantaneous bandwidth. However, the inherent large time-bandwidth product of SFW introduces substantial ranging errors and energy dispersion, which significantly hinders its efficacy in detecting high-speed objects. This paper introduces a pioneering velocity estimation technique utilizing the fractional Fourier transform (FrFT) to address these limitations. Leveraging the characteristic of the Doppler signal from a moving target, which manifests as a chirp signal with a rate proportional to the target's velocity, the FrFT is utilized for precise velocity estimation. Subsequent to this, velocity compensation is applied using the deduced metrics, followed by the application of the inverse fast Fourier transform (iFFT) to pinpoint the target's exact location. To optimize the computational efficiency of determining the FrFT's optimal order, we propose an iterative algorithm founded on the golden section search method. The effectiveness of the proposed approach is verified by simulation data, and the results demonstrate that the proposed approach can accurately estimate the velocity and the range of the high-speed targets with a relatively low computational complexity.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper better communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as a helpful tool to enhance UAV communication networks. However, due to the high mobility of UAV, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. Simulation results showed that the proposed GRU model effectively and accurately estimates the link quality of the ground users in the RIS-assisted UAV-enabled wireless communication network.

Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.

Deformation monitoring is beneficial to ensure the service safety of long tunnel. An three-direction (vertical, transverse and axial) deformation estimation method using long-gauge fiber Bragg grating (FBG) sensing technology is proposed and applied to a long tunnel by integrating mechanical analysis and field monitoring during the service state. The key issues of the proposed method for identifying the three-direction deformation are discussed, such as establishing the separation model of coupling strain on cross section, deriving the theory of deformation identification in three directions, and determining the sensor layout of the long tunnel. A major advantage of the proposed method is that the three-direction deformation of the tunnel can be monitored in real time using distributed long-gauge strain sensors. The numerical analysis and field test results verify the feasibility of the proposed method.

Unlike aerial or terrestrial navigation, the Global Navigation satellite system (GNSS) is not available underwater. This is a big challenge for underwater navigation. The inertial navigation system (INS) aided by the single-beacon acoustic positioning system (APS) provides one solution, but the long-range case is limited by low SNR conditions. Inspired by passive synthetic aperture detection, we proposed a new tightly coupled navigation algorithm based on spatial synthesis and one-way-travel-time (OWTT) range measurement. We design two estimators: the DOA/Range estimator using the model-based method and the tightly coupled INS/APS navigation estimator. Based on the improved UKF, all information is combined. Simulation is carried out in MATLAB. Compared with range-only tightly coupled INS/APS navigation, synthetic long baseline (SLBL) algorithm and Doppler velocity logger (DVL) aided centralized extended Kalman filter (CEKF) based single beacon INS/OWTT navigation, the proposed method's performance is proven. The main contributions of this work are: 1. Propose a new architecture of underwater integrated navigation; 2. Apply the passive acoustic detecting method in the navigation to improve accuracy. 3. Apply the tightly coupled method to improve availability.

Monitoring volatile organic compounds (VOCs) places a crucial role in environmental pollutants control and indoor air quality. In this study, a metal-oxide (MOx) sensor detector (used in a commercially available monitor) was employed to delineate the composition of air containing three common VOCs (ethanol, acetone and hexane) under various concentrations. Experiments with a single component and double components were conducted to investigate how the solvents interact with the metal oxide sensor. The experimental results revealed that the affinity between VOC and sensor was in the following order: acetone > ethanol > n-hexane. A mathematical model was developed, based on the experimental findings and data analysis, to convert the output resistance value of the sensor into concentration values, which in turn can be used to calculate a VOC-based air quality index. Empirical equations were established based on inferences of vapor composition versus resistance trends, and on an approach of using original and diluted air samples to generate two sets of resistance data per sample. The calibration of numerous model parameters allowed matching simulated curves to measured data. As such, the predictive mathematical model enabled quantifying not only the total concentration of sensed VOCs, but also estimating the VOC composition. This first attempt to obtain semi-quantitative data from a single MOx sensor, despite remaining selectivity challenges, is aimed at expanding the capability of mobile air pollutants monitoring devices.

In this paper we present an understanding of cyber risks in the Internet of Things (IoT), we explain why it is important to understand what IoT cyber risks are and how we can use risk assessment and risk management approaches to deal with these challenges. We introduce the most effective ways of doing Risk assessment and Risk Management of IoT risk. As part of our research, we also developed methodologies to assess and manage risk in this emerging environment. This paper will take you through our research and we will explain: what we mean by the IoT; what we mean by risk and risk in the IoT; why risk assessment and risk management are important; the IoT risk management for incident response and recovery; what open questions on IoT risk assessment and risk management remain.

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

For Interferometry Synthetic Aperture Radar (InSAR), the normal baseline is one of the main factors that affect the accuracy of the ground elevation. For Gaofen-3 (GF-3) InSAR processing, the poor accuracy of the real-time orbit determination resulting in a large baseline error, leads to the modulation error in azimuth and the slope error in range for timely Digital Elevation Model (DEM) generation. In order to address this problem, a baseline estimation method based on external DEM is proposed in this paper. Firstly, according to the characteristic of the real-time orbit of GF-3 images, orbit fitting is executed to remove the non-linear error factor. Secondly, the height errors are obtained in slant-range plane between Shuttle Radar Topography Mission (SRTM) DEM and the GF-3 generated DEM after orbit fitting. At the same time, the height errors are used to estimate the baseline error which has a linear variation. In this way, the orbit error can be calibrated by the estimated baseline error. Finally, DEM generation is performed by using the modified baseline and orbit. This procedure is implemented iteratively to achieve a higher accuracy DEM. Based on the results of GF-3 interferometric SAR data for Hebei, the effectiveness of the proposed algorithm is verified and the accuracy of GF-3 real-time DEM products can be improved extensively.

The chemical industry is an integral part of the world economy and a substantial income source for developing countries. However, existing regulations or the enforcement of these regulations, on controlling atmospheric pollutants sometimes may be insufficient, leading to the deterioration of surrounding ecosystems and to a quality decrease of the atmospheric environment. Previous works in this domain fail to generate executable solutions for inspection agencies due to practical challenges. In addressing these challenges, we introduce a so-called Chemical Plant Environment Protection Game (CPEP) to generate reasonable schedules of high-accuracy air quality monitoring stations for inspection agencies. First, Stackelberg Security Games (SSGs) are incorporated together with source estimation methods into this research. Second, high-accuracy air quality monitoring stations as well as gas sensors are modeled into the CPEP. Third, simplified data analysis on the regularly discharging of chemical plants is utilized to construct the CPEP. Finally, an illustrative case study is used to investigate the effectiveness of the CPEP Game, and a realistic case study is conducted to illustrate how the models and algorithms being proposed in this paper, work. Results show that playing a CPEP Game can reduce operational costs of high-accuracy air quality monitoring stations; moreover, playing the game leads to more compliance from the chemical plants towards the inspection agencies.

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSMT) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which was used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

This paper presents the methods of estimating the mean square error of hydrological forecasts, allowing for assessment of their practical applicability. Depending upon the amount and composition of available hydrometeorological data, an appropriate method for forecast error estimation is chosen. A system of statistical tests for comparison of different forecasting methods for the same hydrologic characteristic with the same lead time is presented. These tests allow for choosing an optimal and most accurate forecasting method. Hydrological forecasting method efficiency estimation is based on comparing the forecast error with climatology or inertial (persistence) forecast error using presented tests.

In recent years, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns, policy decision support, etc. because the information on alighting stops of users is missing. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. Various studies to overcome this limitation have been conducted in South Korea and other countries to develop es-timation methodologies of alighting stops. Even existing studies introduce an advanced method, we found the margin for better accuracy by combining various estimation methodologies for estimating alighting stops. This study reviewed previously conducted studies to classify data with missing alighting stop information into trip types and then applied an appropriate alighting stop estimation methodology for the characteristics of each trip type by stage. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checked the accuracy for each standard of allowable error for sensitivity analysis. Furthermore, the number of trips, accuracy, and valid tag rate were checked for each type to examine the need for classifying the trip types. Finally, our evaluation also examines the impact of classifying trip types on estimation accuracy. The evaluation criteria are accuracy of the number of trips and valid tag rate. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can es-timate users’ destinations more accurately than previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

Miscanthus holds a great potential in the frame of the bioeconomy and yield prediction can help improving Miscanthus logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. Random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass and standing biomass) using VIs time series and predicted yield using peak descriptor derived from VIs time series with 2.3 Mg DM ha-1 of RMSE. The study demonstrates the potential of UAVs multispectral in HTP applications and in yield prediction for providing important information needed to increase sustainable biomass production.

We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular we use the variance of passive observation–minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin.

This article analyzes 6 probability distributions from the Generalized Pareto family, with 3, 4 and 5 parameters, with main purpose to identify other distributions from this family with applicability in flood frequency analysis compared to the distribution already used in the literature from this family such as Generalized Pareto Type II and Wakeby. This analysis is part of a larger and more complex research carried out in the Faculty of Hydrotechnics regarding the elaboration of a norm for flood frequency analysis using the linear moments method. In Romania, the standard method of parameter estimation is the method of ordinary moments, thus the transition from this method to the method of linear moments is desired. All the necessary elements for the distributions use are presented like, the probability density functions, the complementary cumulative distribution functions, the quantile functions, the exact and approximate relations for estimating parameters, for both methods of parameters estimation. All these elements are necessary for a proper transition between the two methods, especially since the use of the method of ordinary moments is done by choosing the skewness of the observed data depending on the origin of the maximum flows. A flood frequency analysis case study, using annual maximum and annual exceedance series, was carried out for the Prigor river, to numerically present the analyzed distributions. The performance of this distributions is evaluated using relative mean error, relative absolute error and linear moments diagram.

This paper deals with a non-standard finite difference scheme defined on a quasi-uniform mesh for approximate solutions of the Magneto-Hydro Dynamics (MHD) boundary layer flow of an incompressible fluid past a flat plate for a wide range of the magnetic parameter. We show how to improve the obtained numerical results via a mesh refinement and a Richardson extrapolation. The obtained numerical results are favourably compared with those available in the literature.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in video transmission across networks. Traditional adaptive bitrate (ABR) algorithms adjust the quality of video segments based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules within a complex environment, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel Deep Reinforcement Learning-based approach for streaming DASH, focusing on maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes the Quality of Experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate this approach through a simulation model that encompasses diverse wireless network environments and various video sequences. Additionally, we compare our proposed approach with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.

The robust fusion estimation problem of a multi-sensor system with communication constraints, random packet drops, d-step state delays, and deterministic control inputs is investigated. The state augmentation method is used to transform the time delay system into a non-time delay system, and then a robust fusion estimation algorithm based on sensitivity penalty is proposed and the pseudo-cross-covariance matrix is updated. An event-triggered robust fusion estimator with state delay and deterministic control inputs is considered to make the model closer to reality while effectively reducing network congestion and lowering communication costs. Conditions are given to ensure that the estimation errors of the robust fusion estimator are uniform boundedness. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, adaptive and learning methods are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a method in which the controller is designed to adapt the system with parameters which vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model & measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. Deterministic artificial intelligence, a learning-based approach that uses the process dynamics defined by physics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive & learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output & the reference trajectory and the standard deviation of the error. The first order-hold method of discretization and surprisingly large sample time of seven tenths of a second yields over sixty percent improvement over the results presented in the prequel literature.

Conditional heteroskedastic financial time series are commonly modelled by (G)ARCH processes. ARCH(1) and GARCH were recently established in C[0,1] and L^2[0,1]. This article provides sufficient conditions for the existence of strictly stationary solutions, weak dependence and finite moments of (G)ARCH processes for any order in C[0,1] and L^p[0,1]. It deduces explicit asymptotic upper bounds of estimation errors for the shift term, the complete (G)ARCH operators and the projections of ARCH operators on finite-dimensional subspaces. The operator estimaton is based on Yule-Walker equations, and estimating the GARCH operators also involves a result estimating operators in invertible linear processes being valid beyond the scope of (G)ARCH. Moreover, our results regarding (G)ARCH can be transferred to functional AR(MA).

In the context of video background-foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method can process per video frame from a small set of measurements, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-l₁ minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background-foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.

Reliability analysis techniques are customary standard tools that are used for evaluating the performance of different equipment and devices in order to minimize their downtime. To predict the reliability, life data from a sample that is satisfactorily representative of the equipment should be fitted to the suitable statistical distribution. The parameterized distribution may be used to estimate essential characteristics such as failure rate; and probability at a precise time, as well as system reliability. In the current study, Weibull++/ALTA software package is used as a novel tool to fit the available data set to estimate the best fitted probability density function (PDF) using Maximum Likelihood (MLE) for parameter estimation. The determined distributions are then assessed using goodness-of-fit test to define how well it fits the available data set. There are multiple methods for determining goodness-of-fit. Weibull distributions and their special cases’ parameters have an effect on life times.

Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.

The current study applied a new approach for the interpolation and regionalization of observed precipitation series to a smaller spatial scale (0.125° by 0.125° grid) across the Upper Indus Basin (UIB), with appropriate adjustments for the orographic effect and changes in glacier storage. The approach is evaluated and validated through reverse hydrology, guided by observed flows and available knowledge base. More specifically, the generated corrected precipitation data is validated by means of SWAT-modelled responses of the observed flows to the different input precipitation series (original and corrected ones). The results show that the SWAT- simulated flows using the corrected, regionalized precipitation series as input are much more in line with the observed flows than those using the uncorrected observed precipitation input for which significant underestimations are obtained.

The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore the damaged central nervous system function in patients. The patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot assisted torque which focuses on the evaluation only of the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer, which deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limbs. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner loop position control module for the pneumatic swing cylinder in the joint space. An upper limb endpoint stiffness estimation model is constructed and a parameter identification algorithm is designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements is obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then the motor needs of the affected limb when completing the same movement are quantified based on the performance of the healthy limb. A stiffness mapping algorithm is designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method can effectively estimate the patient's movement needs and achieve progressive rehabilitation training. The rehabilitation training robot that simulates the movement characteristics of the patient's healthy limbs drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb use habits, and also beneficial for the consistency of bilateral limb movements.

To address the trajectory tracking issue for quadrotors working under the influence of external disturbances, a control scheme is designed for position and attitude trajectory tracking. Firstly, the quadrotor dynamic model is derived for control design. Secondly, adaptive integral backstepping control (AIBS) is employed in the position loop in which adaptive estimation is used to estimate the upper bounds of disturbances. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) is proposed to follow desired Euler angles. The intro-duction of fast nonsingular terminal sliding mode and integral element is to realize fast convergence and accurate tracking. Dead zone technique is applied to minimize estimation errors. Besides, a saturation function is utilized to eliminate chattering phenomenon. Finally, simulation experiments are conducted under the Simulink environment. The proposed control is verified by comparing it with traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS).

Earthquakes often entail alterations in the groundwater flow regime, in the phreatic level, in the surge and loss of springs, and discharge in brooks and springs. As for the latter, there are diverse feasible mechanisms, so there is a variety of theoretical approaches to elucidate the post-earthquake effects on spring discharge. This study adopts a conceptual approach, primarily presenting various methods for estimating water released by earthquakes by calculating discharge surpluses in springs. This study delves into refined techniques rooted in groundwater hydraulics, displaying applications of analytical and simulation methodologies to quantify seismic-induced groundwater discharge in springs. Investigating springs as natural indicators, this research applies mathematical precipitation-runoff models, specifically the CREC model, for simulating hydrographs in post-earthquake scenarios. We apply analytical procedures or mathematical simulation techniques employed in groundwater hydraulics for natural aquifer recharge calculations. Firstly, we briefly describe the methods based on the analysis of depletion curves of hydrographs in spring discharge. Additionally, certain mathematical rainfall-runoff models utilized in simulating hydrographs of karstic springs, along with derived analytical approximations, are adapted for this scenario. These hydraulic calculations involve the depletion coefficient and hydrodynamic volumes of aquifers, parameters that reveal certain aspects of the relation between groundwater and earthquakes, such as: a) Acknowledging faults as the primary geological structures in transmitting pore pressures due to earthquakes. b) Clarifying mechanisms of permeability enhancement during earthquakes. c) Highlighting the substantial volumes in motion within the Earth's interior, which, for instance, could constitute a significant source for the origin of mineral deposits. The findings highlight diverse features of the complex interplay between hydrogeological and tectonic processes at challenging spatial and temporal scales.

This paper presents preliminary research that investigates the relationship between the flow of a group of jazz musicians, quantified through multi-person pose synchronization, and their collective emotions. Building upon previous studies that measured the physical synchronicity of team members by tracking their body movements and measuring the difference in arm, leg, and head movements, we introduce a novel metric termed "team entanglement". We employ facial expression recognition to evaluate the musicians’ collective emotions. Through correlation and regression analysis, we establish that higher levels of synchronized body and head movements correspond to lower levels of disgust, anger, sadness and higher levels of joy among the musicians. Furthermore, we utilize a Convolutional Neural Network (CNN) based deep learning model to predict the collective emotions of the musicians. This model leverages 17 body synchrony keypoint vectors as features, resulting in a training accuracy of 61.47% and a test accuracy of 66.17%.

This paper aims at trying to answer the question of whether a low cost drone equipped with a regular camera can serve as a simple device to estimate the size of objects. Ad-ditionally, it assesses if the accuracy of measurement is good enough for using a drone for measurements, relying solely on GPS position and camera. A review of the literature pertaining to ground object measurements was conducted. The availability of such in-struments was evaluated in terms of purchase and rental costs, the number of instru-ments on the market, and the time to obtain them. The most common measurement methods were considered and their unit costs were reviewed. The cost of professional surveying and measuring equipment was compared to the possibility of using low-cost, non-commercial drones to measure objects and surfaces. The quality of the images ob-tained was compared with high-resolution satellite systems. Is an ordinary drone with a standard camera suitable for length measurement without professional measuring equipment? Should the results be considered only in the category of size estimation?

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating positons of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determine the signal arrival time and then locate the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transfers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user’s description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

Deep Learning (DL) and Machine Learning (ML) are widely used in many fields, but rarely used in Frequency Estimation (FE) and Slope Estimation (SE) of signals. Frequency and slope estimation for Frequency-Modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sonar, and radar measurements. In this work, artificial neural network (ANN) and convolutional neural network (CNN) are used in frequency and slope estimation for FM signals under Additive White Gaussian Noise (AWGN) and Additive Symmetric alpha Stable Noise (SαSN). SαS distributions are impulsive noise disturbances found in many communication environments like marine systems; their distribution lacks a closed-form Probability Density Function (PDF), except for specific cases, and infinite second-order statistic, hence Geometric SNR (GSNR) is used in this work to determine the impulsiveness of noise in a mixture of Gaussian and SαS noise processes. ANN is a machine learning classifier, designed with few layers for reducing FE and SE complexity while getting higher accuracy as compared with classical techniques. CNN is a deep learning classifier, designed with many layers for FE and SE, and proved to be more accurate than ANN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful for FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption, which is important in many systems such as some Internet of Things (IoT) sensor applications. After training DCNN for frequency and slope estimation of LFM signals, the performance of DCNN (in terms of accuracy) can give acceptable results at very low signal-to-noise ratios where TFD fails, giving more than 20dB difference in the GSNR working range.

This paper focuses on the so-called proportional intensity-based software reliability models (PI-SRMs), which are extensions of the common non homogeneous Poisson process (NHPP)-based SRMs, and describe the probabilistic behavior of software fault-detection process by incorporating the time-dependent software metrics data observed in the development process. Especially we generalize the seminal PI-SRM in Rinsaka, Shibata and Dohi (2006) by introducing eleven well-known fault-detection time distributions, and investigate their goodness-of-fit and predictive performances. In numerical illustrations with four data sets collected in real software development projects, we utilize the maximum likelihood estimation to estimate model parameters with three time-dependent covariates; test execution time, failure identification work and computer time-failure identification, and examine the performances of our PI SRMs in comparison with the existing NHPP-based SRMs without covariates. It is shown that our PI-STMs could give better goodness-of-fit and predictive performances in many cases.

Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

The timely diagnosis of defects at their incipient stage of formation is crucial to extend the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damages, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of Acoustic Emission (AE)-based inspection techniques through the computation of the Time of Arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor Signal-to-Noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative Deep Learning methods are proposed for ToA retrieval, namely a Dilated Convolutional Neural Network (DilCNN) and a Capsule Neural Network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two novel methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations.

Many papers in recent years have been devoted to estimating the per pixel proportions of three broad classes of materials (e.g. photosynthetic vegetation, non-photosynthetic vegetation and bare soil) using data from multispectral sensors. Many of these papers use estimation methods based on the linear mixture model. Very few of these papers assess the accuracy of their estimators. I show how to produce confidence intervals (CIs) and joint confidence regions (JCRs) for the proportions associated with various linear mixture models. There are two main models, both of which assume that the coefficients in the model are non-negative. The first model assumes that the coefficients sum to 1. The second does not, but uses rescaling of the estimated coefficients to produce estimated proportions. Three variants of these two models are also analysed. JCRs are shown to be particularly informative, because they are typically better at localising the information than CIs are. The methodology is illustrated using examples from Landsat Thematic Mapper data at 1169 locations across Australia, each of which has associated field observations. There is also discussion about the extent to which the methodology can be extended to hyperspectral data.

This paper introduces an approach that chooses the fault detection by calculating probabilities using probability mass function (pmf) and cumulative distribution function (CDF). This work used a method for multiple stuck-at faults by producing a new test pattern in combinational circuits. We assumed that existence of all multiple faults is only because of one single component that is faulty. A complete test set can be created by all possible single stuck-at faults in a combinational circuit using some combination of gates. The test set generation fault detection method is applied on two different 3-bit input variable and 4-bit input variable circuits. The probability of error occurrence is calculated at both 3-bit and 4-bit input variable circuits. The resulting feature is used to obtain maximum error occurrence probability to detect faults by the logic used that the complexity of the circuit is inversely proportional to the fault occurrence probability. Then again, undetectability is directly proportional to the complexity of the circuit. Therefore, finest feasible circuit should have large input variable components with less complexity to reduce the fault occurrence probability.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_c such as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

The article analyzes the problems connected with ensuring the coordinated operation of slipway drives that arise during the launch of a ship. The dynamic model of load of the electric drive of the ship's cart is obtained taking into account the peculiarities of the construction of the ship-lifting complex, which allows to analyze the influence of external factors and random influences during the entire process of launching the ship. A linearized mathematical model of the dynamics of a complex vessel movement in the process of descent in the space of states is developed, which allows to identify the mode of operation of the multi-drive system, taking into account its structure. The analysis of efficiency of application of recurrent methods of identification (stochastic approximation and least squares) of the parameters of the linearized model in the space of states is carried out. A decision support system has been developed in the automated system of operational control by the module for estimating the situation and the control synthesis to ensure a coherent motion of a complex ship-carts object in a two-phase environment.

Background. Pose estimation based on deep learning has been expected to be a breakthrough method to increase the accuracy of clinical photographic evaluation and to decrease interobserver errors. The purpose is to quantify pose estimation from photography of patients with adolescent idiopathic scoliosis (AIS) using open-source software packages and determine correlations between parameters obtained by radiography and photography. Methods. We included 12 consecutive patients with AIS treated with spinal correction surgery. Photographs were taken preoperatively using a tripod-mounted camera (iPhone 13Pro) on an X-ray tube head. To assess photographic parameters obtained by photography, we defined 17 points to analyze posture and define parameters. Results. In the sagittal plane, there was a significant correlation between the radiographic trunk tilt angle and the photographic sagittal trunk tilt angle of the shoulder–hip and ear–hip. In the coronal plane, there was a significant correlation between the radiographic clavicle angle and the photographic shoulder height angle, and the radiographic C7–CSVL and the photographic coronal trunk tilt angle. Conclusions. Posture analysis by photography using popular mobile devices has clinical utility for improving and promoting the screening and early detection of AIS because it is simple, without patient exposure to X-ray radiation.

Newly characterising 245 Italian and Albanian HCV-2 NS5B sequences collected between 2001 and 2016 was used to reconstruct the origin and dispersion pathways of HCV-2c. The tree of a subset of these sequences aligned with 247 publicly available sequences was reconstructed in spatio-temporal scale using the Bayesian approach, and the effective replication number (Re) was estimated using the birth-death model. Our findings show that HCV-2c was the most prevalent subtype in Italy and Albania, and that GT2 originated in Guinea Bissau in the XVI century and spread to Europe in the XX century. The HCV-2c subtype had two internal nodes respectively dating back to the 1930s and 1950s having as most probable locations Ghana and Italy, respectively. Phylodynamic analysis revealed an exponential increase in the effective number of infections and Re in both Italy between the 1950s and 1980s, and Albania between the 1990s and the early 2000s. It seems very likely that HCV-2c reached Italy from Africa at the time of the second Italian colonisation (1936-1941), but did not reach Albania until the period of dramatic migration to Italy in the 1990s.

This paper considers the direction of arrival (DOA) estimation problem of phase ambiguity for unfolded coprime linear array (UCLA). The existing most common stacking subarray-based method can tackle the phase ambiguity problem. However, the method is not always true. For a given DOA, it has its corresponding steering vector, but if there are two DOAs which have the same steering vectors with the given DOA for different subarrays, the phase ambiguity problem still arises. A modified method, which defines a decision variable and uses the classic beamforming technique to estimate the DOAs is proposed. However, this method needs additional algorithm to distinguish the true DOAs besides multiple signal classification (MUSIC) spectrum. And sometimes this method is not reliable if the decision variable is not chosen appropriately. Therefore, based on the UCLA, we reconstruct the array to design an improved UCLA called IUCLA. With moving the reference element, the linear relationship among the directional vectors can be broken. In this way, we can directly utilize the MUSIC algorithm to estimate with no additional algorithm. The proposed method can solve the phase ambiguity problem and it does not demand other technique to decrease the complexity. Moreover, the Cramer-Rao Bound (CRB), as the lower bound of the unbiased estimation, is provided and numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method.

Foundational and state-of-the-art anomaly detection methods through power system state estimation are reviewed. The traditional components for bad data detection such as chi-square testing, residual-based methods, and hypothesis testing are discussed to explain the motivations for recent anomaly detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest change detection and artificial intelligence are discussed and directions for research are suggested with particular emphasis on considerations of the future smart grid.

Bipolar disorder is a severe mood disorder and is one of the top 20 reasons of disability in the world. It causes a huge burden on society. In this study, the prediction models of bipolar disorder were constructed based on the concept of knowledge distillation. The input data consisted of patients of bipolar disorder and matched controls, all of which were selected from the open database MIMIC. The method of kernel density estimation (KDE) was exploited to generate probability density functions (PDF) which identify distributions of input data. The PDF values referred to as the soft labels were combined with the input data to construct the prediction models of bipolar disorder using decision tree and artificial neural network respectively. According to the evaluation results, indicators for identifying positive samples of bipolar disorder were improved. Meanwhile, the indicators for identifying negative samples have also been advanced. In addition, the branching attributes selected by the decision trees can be mapped back to specific disease diagnoses, which are all associated with bipolar disorder. In conclusion, using KDE to generate the soft label information of the input data can make knowledge distillation work and has improved the performances of prediction models for bipolar disorder.

Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems. We present a system in which an autonomous vehicle — our ACTor 1 platform — can follow a human leader who controls the vehicle through gestures. We successfully developed a modular pipeline that uses artificial intelligence/deep learning to recognize hand-and-body gestures from a user in view of the vehicle’s camera and translate those gestures into physical action by the vehicle. We demonstrate our work using our ACTor 1 platform, a modified Polaris Gem 2 electric vehicle. This work has numerous applications, such as material transport in industrial contexts.

The aerospace industry develops prognosis and health management algorithms to ensure better safety on board. Particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, we developed a clustering algorithm using a deep neural network core. We encoded the time series into pictures to be feed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihood without prior knowledge. We compared it to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As state-of-the-art indexes were not producing relevant results, we built a new indicator to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework.

Imaging systems mounted to ground vehicles are used to image fruit tree canopies for estimation of fruit load, but frequently need correction for fruit occluded by branches, foliage or other fruits. This can be achieved using an orchard ‘occlusion factor’, estimated from a manual count of fruit load on a sample of trees (referred to as the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruit. Five approaches to correct for occluded fruit based on canopy images were compared using data of three mango orchards in two seasons. However, no attribute correlates to the number of hidden fruit were identified. Several image features obtained through segmentation of fruit and canopy areas, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest fruit count on trees. The supervised machine learning methods for direct estimation of fruit load per tree delivered an improved prediction outcome over the reference method for data of the season/orchard from which training data was acquired. For a set of 2017 season tree images (n=98 trees), a R2 of 0.98 was achieved for the correlation of the number of fruits predicted by a Random forest model and the ground truth fruit count on the trees, compared to a R2 of 0.68 for the reference method. The best prediction of whole orchard (n = 880 trees) fruit load, in the season of the training data, was achieved by the MLP model, with an error to packhouse count of 1.6% compared to the reference method error of 13.6%. However, the performance of these models on new season data (test set images) was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model. This outcome was attributed to variability in tree architecture and foliage density between seasons and between orchards, such that the characters of the canopy visible from the interrow that relate to the proportion of hidden fruit are not consistent. Training of these models across several seasons and orchards is recommended.

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a Deep Neural Network-based Model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed DNN model along with an inverse perspective mapping technique leads to a very accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online infection risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20 x 20 m convex and non-convex environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment and at lower computational complexity per action. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.

Rapid developments in the field of underwater photogrammetry have given scientists1the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of 3D reconstruction and navigation fusion strategies, as well as associated error evaluation of models produced at larger scales in GPS-denied environments using a monocular camera (often in deep-sea scenarios). Based on our prior work on automatic scale estimation of Structure from Motion (SfM)-based 3D models using laser scalers, an automatic scale accuracy framework is presented. The confidence level for each of the scale error estimates is independently assessed through the propagation of the uncertainties associated with image features and laser spot detections using a Monte Carlo simulation. The number of iterations used in the simulation was validated through the analysis of the final estimate behaviour. To facilitate the detection and uncertainty estimation of even greatly attenuated laser beams, an automatic laser spot detection method, mitigating the effects of scene texture, was developed, with the main novelty of estimating the uncertainties based on the recovered characteristic shapes of laser spots with radially decreasing1 intensities. The effects of four different reconstruction strategies resulting from the combinations of Incremental/GlobalSfM, and thea priori/a posterioriuse of navigation data were analyzed using two distinct survey scenarios captured during the SUBSAINTES 2017 cruise (doi: 10.17600/17001000). The study demonstrates that surveys with multiple overlaps of non-sequential images result in a nearly identical solution regardless of the strategy (SfM or navigation fusion), while surveys with weakly connected sequentially acquired images are prone to produce broad-scale deformation (doming effect) when navigation is not included in the optimization. Thus the scenarios with complex survey patterns substantially benefit from using multi-objective BA navigation fusion. In all cases, the errors in the models are inferior to 5%, with errors often being around 1%. The effects of combining data from multiple surveys were also evaluated. The introduction of additional vectors in the optimization of multi-survey problems successfully accounted for offset changes present in the underwater USBL-based navigation data and thus minimize the effect of contradicting navigation priors. Our results also illustrate the importance of collecting a multitude of evaluation data at different locations and moments during the survey.

One of the most rapidly advancing areas of deep learning research aims at creating models that learn to disentangle the latent factors of variation from a data distribution. However, modeling joint probability mass functions is usually prohibitive, which motivates the use of conditional models assuming that some information is given as input. In the domain of numerical cognition, deep learning architectures have successfully demonstrated that approximate numerosity representations can emerge in multi-layer networks that build latent representations of a set of images with a varying number of items. However, existing models have focused on tasks requiring to conditionally estimate numerosity information from a given image. Here we focus on a set of much more challenging tasks, which require to conditionally generate synthetic images containing a given number of items. We show that attention-based architectures operating at the pixel level can learn to produce well-formed images approximately containing a specific number of items, even when the target numerosity was not present in the training distribution.

Healthcare professionals and resource planners can use healthcare delivery process mining to ensure the optimal utilisation of scarce healthcare resources when developing policies. Within hospitals, patients' Length of Stay (LOS) and volume of admitted patients, in terms of number and characteristics (age, gender, and social deter-minants), are significant factors determining daily resource requirements. In this study, we used Coxian phase-type Distribution (C-PHD) based Phase-Type Survival (PTS) trees for analysing how covariates such as admission date, gender, age, district, and admissions source influence the admission rate and LOS distribution. PTS trees. This study used a two-year data set (2011-2012) of patients admitted to the Emergency Department at Mater Dei Hospital to generate models and an independent one-year data set (2013) of patients admitted to the Emergency Department at Mater Dei Hospital to evaluate. The PTS tree effectively clusters patients based on their LOS, considering the prognostic significance of different covariates related to patients' characteristics. Charac-terising these covariates provided meaningful results about LOS. Similarly, the PTS tree was used to effectively cluster patients based on the admission rate, considering the prognostic significance of these covariates.

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

Conclusions: Our findings suggest that the NMS-E represents a valuable preoperative diagnostic tool for endometriosis, effectively correlating with the disease's severity and surgical outcomes. Incorporating the NMS-E into clinical practice could significantly enhance the management of endometriosis by addressing current diagnostic limitations and guiding surgical planning.

Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.