Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

The field of Cooperative Intelligent Transport Systems and more specifically Pedestrians to Vehicles (P2V) could be characterized as quite challenging, since there is broad research area to be studied, with direct positive results to society.P2V is a type of Cooperative Intelligent Transport System, within the group of Early Warning Collision/Safety System. In this article, we examine the research and applications carried out so far within the field of Pedestrians to Vehicles Cooperative Transport Systems by leveraging the information coming from VRU’s smartphones. Moreover, extensive literature review has been carried out in the fields of VRUs Outdoor Localisation via smartphones and VRUs Next Step/Movement Prediction, that are closely related to P2V applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address the future research objectives and methodologies that could support the improvement/development of those gaps. We also propose a P2V security framework that combines prediction and communication capabilities in order to avoid accidents between pedestrians and vehicles.

This paper develops the mathematical modeling and deflection control of a textile-reinforced composite integrated with shape memory actuators. The model of the system is derived using identification method and unstructured uncertainty approach. Based on this model and robust stability analysis a robust proportional-integral controller is designed for controlling the deflection of the composite. The performance of the proposed controller is compared with a classical one through experimental analysis.

Directional Modulation (DM) technique, as an emerging promising approach to secure wireless communications, has been attracting increasing attention for its unique characteristic in the past decade. The baseband signal is synthesized at the antenna level for maintaining the standard constellation format along the prescribed direction(s) while simultaneously distorting the signal constellations in other undesired directions. In this paper, we present a novel DM signal synthesis method based on phased array using a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, which takes advantages of both GA and PSO algorithm. Then, the bit error rate of the proposed method is analyzed and simulated. Simulation results are presented to verify that the proposed DM signal have a narrower information beam-width. Therefore, it can offer a better physical layer security (PLS) performance compared with the conventional directional modulation signal.

Permanent magnet synchronous motor (PMSM) AC servo system shows the characteristics of uncertainty, time-varying and non-linearity, which makes it difficult for traditional PID control to achieve ideal control effect. Fuzzy control has strong adaptability to the problems of parameter variation, non-linearity and model inaccuracy of the controlled object. Vector control strategy is used to study its control principle and the realization of SVPWM. Because the motor object has certain fractional order characteristics, the fuzzy parameter self-tuning PIλ control is chosen as the position regulator of servo motor. It combines the accuracy of fractional order PI control and the adaptability of fuzzy control. A simulation model of PMSM three-closed-loop system is built in MATLAB/Simulink environment. The results show that the control method is effective and can satisfy the trajectory tracking of servo control.

Objects recognition is a necessary task in smart city environments. This recognition can be used in processes such as the reconstruction of the environment map or the intelligent navigation of vehicles. This paper proposes an architecture that integrates heterogeneous distributed information to recognize objects in intelligent environments. The architecture is based on the IoT / Industry 4.0 model to interconnect the devices, called Smart Resources. Smart Resources can process local sensor data and send information to other devices. These other devices can be located in the same operating range, the Edge, in the same intranet, the Fog, or on the Internet, the Cloud. Smart Resources must have an intelligent layer in order to be able to process the information. A system with two Smart Resources equipped with different image sensors has been implemented to validate the architecture. Experiments show that the integration of information increases the certainty in the recognition of objects between 2\% and 4\%. Consequently, in the field of intelligent environments, it seems appropriate to provide the devices with intelligence, but also capabilities to collaborate closely with other devices.

Directional protective relaying based on amplitude comparison of traveling wave on continuous co-phase auto-transformer(AT) power transmission system was proposed. Phase-model transformation is used for decoupling transient fault signals and get aerial mode component. The forward and backward traveling wave are obtained by calculating aerial mode component, then the intrinsic mode function(IMF) components can be obtained through Variational Mode Decomposition(VMD), the module maxima are obtained through calculating the IMF components. The fault direction is determined by the ratio of fault components. If two faults relays at the ends of electric traction networks detect a fault to be in the forward direction, the fault occurred in the internal area, the protection would operate properly. The simulation tests indicate that the protection scheme is feasible, and the proposed protection method can discriminate internal faults from external faults under various fault types, and its performances are immune to fault initial angle, ground resistance, etc.

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust adaptive and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a Function-as-a-Service based Fog Robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.

A variety of techniques have been utilized in metal additive manufacturing (AM) for melt pool size management, including modeling and feed-forward approaches. In a few cases, closed-loop control has been demonstrated. In this research, closed-loop melt pool size control for large-scale, laser-wire based Directed Energy Deposition is demonstrated with a novel modification: site-specific changes to the controller set-point were commanded at trigger points, the locations of which were generated by the projection of a secondary geometry onto the primary 3D-printed component geometry. The present work shows that, through this technique, it is possible to print a specific geometry that occurs beyond the actual toolpath of the print head. This is denoted as an extra-toolpath geometry and is fundamentally different from other methods of generating component features in metal AM. A proof-of-principle experiment is presented in which a complex oak leaf geometry was embossed on an otherwise ordinary double-bead wall made from Ti-6Al-4V. The process is introduced and characterized primarily from a controls perspective with reports on the performance of the control system, the melt pool size response, and the resulting geometry. The implications of this capability, which extend beyond localized control of bead geometry to the potential mitigations of defects and functional grading of component properties, are discussed.

In addition to provide an extremely clean environment for primary loop of high-temperature gas-cooled reactor (HTR), the primary helium circulator (PHC) using electromagnetic levitation technology also provides an effective means for vibration control. Besides synchronous vibration produced by mass imbalance and sensor runout, double-frequency vibration produced by unbalanced magnetic pull (UMP) is serious in PHC engineering prototype (PHC-EP). In this paper, we firstly analyzed the mechanism of UMP and the multi-frequency vibration characteristics in combination with the PHC-EP. Then we put forward a distributed iterative learning control (ILC) algorithm and a parallel control scheme to suppress the periodic vibrations. Finally, we verified the methods by carrying out experimental researches on the active magnetic bearing (AMB) bench of PHC-EP. The results show that the methods put forward in this paper have significant control effect on the double-frequency vibration generated by UMP of the PHC-EP, and provide theoretical and practical references for the PHC safe operation in HTR.

With the development of computer science and software engineering, software becomes more and more complex. Traditional software reliability assurance techniques including software testing and evaluation can't ensure software reliable execution after being deployed. Software failure prediction techniques based on failure indicators can predict software failures according to abnormal indicator values. The latter can be collected using runtime monitoring techniques. An essential part of this method is finding proper indicators which have strong correlation with software failures. We propose a novel type of indicators in this work named software runtime entropy, which takes both software module execution time and call times into consideration. Three common open source software, grep, flex and gzip are used as study cases for finding the relationships between the indicators and software failures. Firstly, a series of fault injection experiments are conducted on those three software respectively. The decision tree algorithm is used to train those data to build the correlation models between software runtime entropy and software failures. Several common measures in machine learning domains such as accuracy, recall rates, and F-measure are used to evaluate the models. The decision tree models can be used as failure mechanisms to assist the failure prediction work. One can examine the value of runtime entropy and make a warning report when it ranges from the normal interval to abnormal one.

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

The purpose of this paper is to provide a structural review of the progress made on detection and localization of leaks in pipelines by using approaches based on the Kalman filter. This is, to the best of our knowledge, the first review on the t opic. In particular, it is the first to try to draw the attention of the leak detection community to the important contributions that use the Kalman filter as the core of a computational pipeline monitoring system. Without being exhaustive, we try to gather the results from different research groups and present them in a unified fashion. For this reason, we propose a classification of the current approaches based on the Kalman filter. For each of the existing approaches within this classification, the basic concepts, fundamental results, and relations with the other approaches are discussed in detail. The review starts from a short summary of basic concepts about state observers. Then, a brief history of the use of the Kalman filter for diagnosing leaks is described by mentioning the most outstanding approaches. At last, we briefly discuss some emerging research problems, such as the leak detection in pipelines transporting heavy oils, and we discuss the main challenges and some open problems.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

The study concentrates on researching possibilities of using computer image analysis and neural modeling in order to assess selected quality discriminants of spray-dried chokeberry powder. The aim of the paper is quality identification of chokeberry powders on account of their highest dying power, the highest bioactivity as well as technologically satisfying looseness of powder. The article presents neural models with vision technique backed up by devices such as digital camera as well as electron microscope. Reduction in size of input variables with PCA has influence on improving the processes of learning data sets, thus increasing effectiveness of identifying chokeberry fruit powders included in digital pictures, which is shown in the results of the conducted research. The effectiveness of image recognition are presented by classifying abilities as well as low Root Mean Square Error (RMSE), for which the best results are achieved with typology of network type Multi-Layer Perceptron (MLP). The selected networks type MLP are characterized by the highest degree of classification at 0.99 and RMSE at 0.11 at most at the same time.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

Roaming complexity in terrains and unexpected environments poses significant difficulties in robotic exploration of an area. In a broader sense, robots have to face two common tasks during exploration namely walking on the drylands and swimming through the water. This research aims to design and develop an amphibious robot, which incorporates webbed duck feet design to walk on different terrains, swim in water and tackle obstructions on its way. The designed robot is compact and easy-to-use and also has the abilities to work autonomously. Such a mechanism is implemented by designing a novel robotic webbed foot consisting of two hinged plates. Due to the design, the webbed feet are able to open and close with the help of water pressure. The Klann linkage has been used to convert rotational motion to walking and swimming as an animal’s gait. Because of its amphibian nature, the designed robot can be used for exploring the tight caves, closed spaces and moving on uneven challenging terrains such as sand, mud or water. The presented model and design of the amphibious robot has been inspired by the working principle of duck feet. The propulsion generated through this feet system has been observed to be better and more controlled then the initial design and prototype of designed duck feet robot reported in [1]. The direction of movement can be managed, and the speed can be controlled using a larger contact area with water. Since duck is able to roam on both land and water, their feet movement during walking and swimming is adopted in designing the robot’s four legs. Klann linkage is used to mimic such duck feet motion and the control mechanism of the feet is driven by DC motors and DC servo motors which are governed by an Arduino microcontroller. The robot is capable of sensing the presence of water through conductive sensors and detects obstacles using ultrasonic sensor while walking. Due to its amphibian nature and other features in movement, the robot is capable of traversing diversified terrains. It is envisaged that the proposed design will be appreciated in the industry to design amphibious robots in near future.

This paper combines the Good Regulator Theorem with the Law of Requisite Variety and seven other requisites that are necessary and sufficient for a cybernetic regulator to be effective and ethical. The resulting Ethical Regulator Theorem provides a basis for systematically evaluating and improving the adequacy of existing or proposed designs for systems that make decisions that can have ethical consequences; regardless of whether the regulators are human, machines, cyberanthropic hybrids, organizations, corporations, or government institutions. The theorem is then used to define an ethical design process that has potentially far-reaching implications for society. A six-level framework is proposed for classifying cybernetic and superintelligent systems, which highlights the existence of a possibility-space bifurcation in our future time-line. The implementation of “super-ethical” systems is identified as an urgent imperative for humanity to avoid the danger that superintelligent machines might lead to a technological dystopia. Third-order cybernetics is defined as the cybernetics of ethical systems. Concrete actions, a grand challenge, and a vision of a super-ethical society are proposed to help steer the future of the human race and our wonderful planet towards a realistically achievable minimum viable cyberanthropic utopia.

Natural calamities observation, study and simulation has always been a prime concern for disaster management agencies. Billions of dollars are spent annually to explore geo-seismic movements especially earthquakes but it has always been a unique accident. The real-time study of seismic waves, ground motions, and earthquakes always needed a programmable mechanical structure capable of physically producing the identical geo-seismic motions with seismology domain definitions. A programmable multi-parametric five degrees of freedom electromechanical seismic wave events simulation platform to study and experiment seismic waves and earthquakes realization in the form of geo-mechanic ground motions is exhibited in this work. The proposed platform was programmed and interfaced through an IoT cloud-based Web application. The geo-mechanics was tested in the range of i) frequencies of extreme seismic waves from 0.1Hz to 178Hz; ii) terrestrial inclinations from -10.000° to 10.000°; iii) velocities of 1km/s to 25km/s iv) variable arrival times 1us to 3000ms; v) magnitudes M1.0 to M10.0 earthquake; vi) epi-central and hypo-central distances of 290+ and 350+ kilometers. Wadati and triangulation methods have been used for entire platform dynamics design and implementation as one of key contributions in this work. This platform is as an enabler for a variety of applications such as training self-balancing and calibrating seismic-resistant designs and structures in addition to studying and testing seismic detection devices as well as motion detection sensors. Nevertheless, it serves as an adequate training colossus for machine learning algorithms and event management expert systems.

This paper presents a symmetrical topology for the design of solid-state transformer, made up of power switching converters, to replace conventional bulky transformers. The proposed circuitry not only reduces the overall size but also provides power flow control with the ability to be interfaced with renewable energy resources (RESs) to fulfill the future grid requirements at consumer end. Solid state transformer provides bidirectional power flow with variable voltage and frequency operation and has the ability to maintain unity power factor, and current total harmonic distortion (THD) for any type of load within defined limits of IEEE standard. Solid State Transformer offers much smaller size as compared to that of the conventional iron core transformer. MATLAB/Simulink platform is adopted to test the validity of the proposed circuit for different scenarios by providing the simulation results evaluated at 25 kHz switching frequency.

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. Firstly, the two key steps, Guided Filtering and improved anti-noise morphology navigation line extraction, were addressed in detail. Then the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption, 0.094 s, compared with HSV, HIS and 2R-G-B color spaces. The Guided Filtering method can enhance the new & old soil boundary effectively than any other methods such as Tarel, Multi-scale Retinex, Wavelet-based Retinex and Homomorphic Filtering, meanwhile, has the fastest processing speed of 0.113 s. The extracted soil boundary line of the improved anti-noise morphology algorithm has best precision and speed compared with other operators such as Sobel, Roberts, Prewitt and Log. After comparing different size of image template, the optimal template with the size of 140×260 pixels can meet high precision vision navigation while the course deviation angle is not more than 7.5°. The maximum tractor speed of the optimal template and global template are 51.41 km/h and 27.47 km/h respectively which can meet real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the new & old soil boundary line extracted by the proposed improved anti-noise morphology algorithm which has broad application prospect.

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one is designed using a Jetson TX or AGX Xavier, and the other is based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-art deep-learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep-learning-based association metric approach (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep-learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

The problem of real-time estimation of occupancy of buildings (number of people in various zones at every time instant) is relevant to a number of emerging applications that achieve high energy efficiency through feedback control. The measurement of CO2 concentration can be considered an important indicator that allows to define the occupation of closed and crowded spaces. Interesting cases can be school buildings and other buildings used in civil and residential (shopping centres, hospitals, etc.). This paper, starting from an experimental analysis in different classrooms of a University campus in real operating conditions, in different period of the year, proposes a possible correlation between CO2 concentration and the occupancy profile of the spaces. The acquired data are used to present some graphical correlations and to understand the most important variables or combination of them. Starting from an accurate analysis of the data, attempts are made to define a preliminary estimation method through the development of a mathematical models of occupancy dynamics in a building, which show interesting results.

The rapid development of multicopters has led to many security problems. In order to prevent multicopters from invading restricted areas or famous buildings, an Anti-UAV Defense System (AUDS) has been developed and become a research topic of interest. Topics under research in relation to this include electromagnetic interference guns for unmanned aerial vehicles (UAVs), high-energy laser guns, US military net warheads, and AUDSs with net guns. However, these AUDSs use either manual aiming or expensive radar to track UAVs. This paper proposes a dual-axis rotary platform with UAV automatic tracking. The tracking platform uses visual image processing technology to track and lock the dynamic displacement of a UAV. When a target UAV is locked, the system uses a nine-axis attitude meter and laser rangefinders to measure its flight altitude and calculates its longitude and latitude coordinates through sphere coordinates to provide UAV monitoring for further defense or attack missions. Tracking tests of UAV flights in the air were carried out using a DJI MAVIC UAV at a height of 30 meters to 100 meters. It was set up for UAV image capture and visual recognition for tracking under various weather conditions by a thermal imager and a full-color camera respectively. When there was no cloud during the daytime, the images captured by the thermal imager and full-color camera provide a high-quality image recognition result. However, under dark weather, black clouds will emit radiant energy and seriously affect the capture of images by a thermal imager. When there is no cloud at night, the thermal imager performs well in UAV image capture. When the UAV is tracked and locked, the system can effectively obtain the flight altitude and longitude and latitude coordinate values.

New developments in broadcast technology, such as the ad hoc wireless network and the detection devices that work, enable daily connections to the Internet, commonly referred to as the Internet of Things (IoT). IoT saw this as a support for the development of creative and contextual services and applications. The service can respond to the user's environment and preferences. Finite state machines are recently making waves when the issue of self-controlling systems come into the picture of designs. This research focuses on the design of a fault tolerant system that controls random failures impressed on the network. The method comprises of diodes, Resistors, Optocouplers, ESP8266 WI-FI Module Webpage with button, and two Arduinos which in effect will determine iteratively who to handle a failure at hand modelled as when the button on the webpage is pressed. In this work, a Heartbeat Detection and Monitoring System will be made using Arduino that detect the electrical state using the Pulse Sensor and show the readings in Beats Per Minute on the LED connected a microcontroller. It sends the texts to the server using the Wi-Fi module ESP8266 to signals can be monitored from the monitoring zone via the internet.   This is reflected by the declaration of a master state by either of two Arduinos whenever the push button is pressed as observed on the codes. The result obtained from the research shows apparently, the state of the fault tolerance system none power deny of heartbeat condition connected to the LED.

Although different assessments and evaluations of Dual-Miller cycle performed, specified output power and thermal performance associated with engine determined. Besides, multi objective optimization of thermal efficiency, Ecological Coefficient of performance ( ) and Ecological function ( ) by the mean of NSGA-II technique and thermodynamic analysis performed. The Pareto optimal frontier obtaining the best optimum solution is chosen by fuzzy Bellman-Zadeh, LINMAP, and TOPSIS decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved.

Buildings have started to play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large scale energy management strategies from the supply side to the consumer side. When the buildings integrate local renewable energy generation in the form of renewable energy resources they become prosumers and this reflects into additional complexity into the operation of the interconnected complex energy systems. A class of methods of modelling the energy consumption patterns of the building have recently emerged as black-box input-output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produces by non-deterministic processes underlying the energy consumption. We present an application of a class of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load forecasting. The Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects and are considered suitable for further used in future in situ energy management at the building and neighbourhood levels.

A sufficient condition for the almost global sta-bility of nonlinear switched systems under average dwell timerestriction is obtained. This condition is derived leaning uponthe existence of multiple Lyapunov densities, which are associ-ated to subsystems and satisfy some compatibility conditions.An upper bound for the average dwell time that ensures almostglobal stability is obtained.

In this paper, the physical station of space network was extended in time dimension by combining the train diagram information and station technical operation standard time. At the same time, the topology of railway space-time network which considered the secondary operation process of train was constructed and an improved A* algorithm based on car flow routing was proposed to generate feasible path sets. On this basis, a dynamic car flow organization optimization model was built to simulate the railway car flow organization process under abnormal conditions, and the results of solving the model could be used to obtain the real-time quantity of cars at each station.This paper can identify the dynamic bottleneck by comparing the real-time quantity of cars with the maximum quantity of cars at the station. Finally, the feasibility of this method was analyzed and verified by a case.

We present an updated design process for adapting and integrating existing cyber risk assessment approaches for impact assessment for the risk from IoT to the digital economy. The new design process includes a set of changes to the original standards (e.g. NIST) that are adapted for the IoT cyber risk in this paper. This paper also presents a new framework for impact assessment of IoT cyber risk, specific for the digital economy.

There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both business reports and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc). At present, the literature is missing a design process for integrating these constantly evolving systems and technologies in a clear and understandable step by step model. This paper contributes with a new reference architecture model for the integration of these systems and technologies. The reference architecture model is based on grouping of future and present techniques and presenting the design process through a new hierarchical framework and a new cascading model. With the application of the grounded theory, the hierarchical framework and the cascading model detail a new process for creating a taxonomy of categories and grouping of concepts into integration design. The new design process is tested and versified with an empirical review of Industry 4.0 frameworks and results with a new 5 levels reference architecture step by step model for the integration of these related systems and technologies (Industrial Internet of Things, Cyber Physical Systems, and Industry 4.0). We review 118 academic and industry papers published between 2010 and 2019. Then, we report the results of a qualitative empirical study that correlates academic literature with 14 world leading Industry 4.0 frameworks and initiatives. We therefore propose an architectural model that offers a better understanding of the systems integration between the Industrial Internet of Things and Industry 4.0.

This research article reports the results of a qualitative case study that correlates academic literature with five Industry 4.0 cyber trends, seven cyber risk frameworks and two cyber risk models. While there is a strong interest in industry and academia to standardise existing cyber risk frameworks, models and methodologies, an attempt to combine these approaches has not been done until present. We apply the grounded theory approach to derive with integration criteria for the reviewed frameworks, models and methodologies. Then, we propose a new architecture for the integration of the reviewed frameworks, models and methodologies. We therefore advance the efforts of integrating standards and governance into Industry 4.0 and offer a better understanding of a holistic economic impact assessment model for IoT cyber risk.

We present an updated design process for adapting and integrating existing cyber risk assessment approaches for impact assessment for the risk from IoT to the digital economy. The new design process includes a set of changes to the original standards (e.g. NIST) that are adapted for the IoT cyber risk in this paper. This paper also presents a new framework for impact assessment of IoT cyber risk, specific for the digital economy.

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 world is currently experiencing the fourth industrial revolution driven by the newest wave of digitisation in the manufacturing sector. The term Industry 4.0 (I4.0) represents at the same time: a paradigm shift in industrial production, a generic designation for sets of strategic initiatives to boost national industries, a technical term to relate to new emerging business assets, processes and services, and a brand to mark a very particular historical and social period. I4.0 is also referred to as Industrie 4.0 the New Industrial France, the Industrial Internet, the Fourth Industrial Revolution and the digital economy. These terms are used interchangeably in this text. The aim of this article is to discuss major developments in this space in relation to the integration of new developments of IoT and cyber physical systems in the digital economy, to better understand cyber risks and economic value and risk impact. The objective of the paper is to map the current evolution and its associated cyber risks for the digital economy sector and to discuss the future developments in the Industrial Internet of Things and Industry 4.0.

The Internet-of-Things (IoT) enables enterprises to obtain profits from data but triggers data protection questions and new types of cyber risk. Cyber risk regulations for the IoT however do not exist. The IoT risk is not included in the cyber security assessment standards, hence, often not visible to cyber security experts. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. The outcome of such self-assessment needs to define a current and target state, prior to creating a transformation roadmap outlining tasks to achieve the stated target state. In this article, a comparative empirical analysis is performed of multiple cyber risk assessment approaches, to define a high-level potential target state for company integrating IoT devices and/or services. Defining a high-level potential target state represent is followed by a high-level transformation roadmap, describing how company can achieve their target state, based on their current state. The transformation roadmap is used to adapt IoT risk impact assessment with a Goal-Oriented Approach and the Internet of Things Micro Mart model.

Based on the Graph Model of Conflict Resolution (GMCR), a two-stage decision framework is developed to reveal the essence of brownfield incidents and facilitate the resolution of brownfield conflicts caused by the incidents. More particularly, the forward GMCR is utilized in Stage I, the negotiation stage, to simulate the evolution of a brownfield conflict (BC) and predict its potential resolution via stability analysis. If no acceptable equilibrium can be obtained, the BC progresses into Stage II, the third-party-intervention stage, where the inverse GMCR is used to assist a third party in intervening the conflict to achieve a desirable outcome. To illustrate the practicality of this framework, a recent BC occurred in Changzhou, China, is taken as a case study. Invaluable insights are provided through the computation and investigation of the corresponding preference relationships.

Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and rare survey has been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human error and to simplify management, self-management CPS (SCPS) is a wise choice. And to achieve dependable self-management, systematic solution is necessary to verify the design and to guarantee the safety of self-adaptation decision, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focus on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means; then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaption, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaption, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, data--driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods, such as fuzzy and adaptive self--tuning controllers, were already verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. The working conditions of these systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

For many years, the programmable positioners have been widely applied in structures of modern electro-pneumatic final control elements. The positioner consists of an electro-pneumatic transducer, embedded controller and measuring instrumentation. Electro-pneumatic transducers that are used in positioners are characterized by a relatively short mean time-to-failure. The practical and economical method of a reasonable prolongation of this time is proposed in this paper. It is principally based on assessment and minimizing the effort of the embedded controller. For this purpose, were introduced: the control value variability, mean-time and the cumulative controller's effort. The diminishing of controller effort has significant practical repercussions, because it reduces the intensity of mechanical wear of the final control element components. On the other hand, the reduction of the cumulative effort is important in the context of process economy due to limitation of the consumption of energy of compressed air supplying the final control element. Therefore, the minimization of introduced effort factors has an impact on increasing the functional safety and economics of the controlled process. As a result of the performed simulations, the recommendations regarding the selection of the structure and tuning of positioner controller were elaborated. The simulations were performed in the Matlab-Simulink environment with the use of the liquid level control system in which a phenomenological model of a final control element was deployed. It has been proven that under appropriate conditions, it is possible to extend significantly the lifetime of the final control element and simultaneously enhance the control quality factors.

Research on bidirectional human-machine interfaces will enable the smooth interaction with robotic platforms in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. We complement the gesture-based control by means of a tactile-feedback strategy grounding on a spiking artificial neuron model. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user’s hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.

The fault diagnosis of wind turbine systems represent a challenging issue, especially for offshore installations, thus justifying the research topics developed in this work. Therefore, this paper addresses the problem of the fault diagnosis of wind turbines, and it present viable solutions of fault detection and isolation techniques. The design of the so--called fault indicator consists of its estimate, which involves data--driven methods, as they result effective tools for managing partial analytical knowledge of the system dynamics, together with noise and disturbance effects. In particular, the suggested data--driven strategies exploit fuzzy systems and neural networks that are employed to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, as they approximate the dynamic evolution of the system along time. The designed fault diagnosis schemes are verified via a high--fidelity simulator, which describes the normal and the faulty behaviour of an offshore wind turbine plant. Finally, by taking into account the presence of uncertainty and disturbance implemented in the wind turbine simulator, the robustness and the reliability features of the proposed methods are also assessed. This aspect is fundamental when the proposed fault diagnosis methods have to be applied to offshore installations.

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to sprouting of a relatively new yet extremely rewarding sphere of technology. The fusion of current deep reinforcement algorithms with physical advantages of a soft bio-inspired structure certainly directs us to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment to achieve a task they have been assigned. For soft robotics structure possessing countless degrees of freedom, it is often not easy (something not even possible) to formulate mathematical constraints necessary for training a deep reinforcement learning (DRL) agent for the task in hand, hence, we resolve to imitation learning techniques due to ease of manually performing such tasks like manipulation that could be comfortably mimicked by our agent. Deploying current imitation learning algorithms on soft robotic systems have been observed to provide satisfactory results but there are still challenges in doing so. This review article thus posits an overview of various such algorithms along with instances of them being applied to real world scenarios and yielding state-of-the-art results followed by brief descriptions on various pristine branches of DRL research that may be centers of future research in this field of interest.

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to sprouting of a relatively new yet extremely rewarding sphere of technology. The fusion of current deep reinforcement algorithms with physical advantages of a soft bio-inspired structure certainly directs us to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment to achieve a task they have been assigned. For soft robotics structure possessing countless degrees of freedom, it is often not easy (something not even possible) to formulate mathematical constraints necessary for training a deep reinforcement learning (DRL) agent for the task in hand, hence, we resolve to imitation learning techniques due to ease of manually performing such tasks like manipulation that could be comfortably mimicked by our agent. Deploying current imitation learning algorithms on soft robotic systems have been observed to provide satisfactory results but there are still challenges in doing so. This review article thus posits an overview of various such algorithms along with instances of them being applied to real world scenarios and yielding state-of-the-art results followed by brief descriptions on various pristine branches of DRL research that may be centers of future research in this field of interest.

A robust guaranteed cost preview repetitive controller is proposed for a class of polytopic uncertain discrete-time systems. In order to improve the tracking performance, the repetitive controller combined with preview compensator is inserted in the forward channel. By using the L-order forward difference operator, an augmented dynamic system is constructed. Then, the guaranteed cost preview repetitive control problem is transformed into the guaranteed cost control problem for the augmented dynamic system. For given performance index, the sufficient condition of asymptotic stability for the closed-loop system is derived by combining parameter-dependent Lyapunov function method with linear matrix inequality (LMI) techniques. By incorporating the controller obtained into the original system, the guaranteed-cost preview repetitive controller is derived. A numerical example is also included to show the effectiveness of the proposed method.

Home Security Systems are very important features of modern residential and office setups. This system is used to keep home safe from harmful gases, fire and intruders. It must be affordable, reliable and effective. In this paper, a GSM based home security system has been designed and implemented. The system consists of a GSM Module, MQ-2 smoke detector, Ultrasonic sonar sensor, LM35 temperature sensor, Arduino Uno and relay module. The MQ-2 smoke detector is used to detect harmful gases i.e. LPG, CH4, CO, Alcohol, Smoke or Propane. Any of the harmful gases stated above is detected it will warn the owner by activating the buzzer and displaying a warning message on the LCD display. It will also send a SMS to the owner. Our system can also measure temperate and if the temperature is increased above a certain range it will compensate it by activating Air Conditioner through the relay module. The system will also send a SMS to the owner. It can also detect intruders using Ultrasonic sensor and warn us through buzzer, SMS and displaying message on the LCD. Our system is reliable as it has very fast response and cost effective and it has been developed using our own technologies.

The management and proper use of the Urban Public Transport Systems (UPTS) constitutes a critical field that has not been investigated in accordance to its relevance and urgent idiosyncrasy within smart cities realm. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviour to deal with dynamic and fast changing systems. The intelligence of cities can be modelled as a swarm of digital telecommunication networks (the nerves), ubiquitously embedded intelligence, sensors and tags, and software. In this paper, a new approach based on the use of the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation and Fireworks algorithms in order to build a system that makes the complete control of the UPTS a tangible reality.

The sTetro is a stair cleaning robot which can climb the staircase with its shapeshifting capabilities. As this robot is intended to traverse multi-floor environment autonomously, hence its localization/positioning information is an essential component of the overall system. Usually, the indoor mobile robots rely on some external system for localization information, e.g., WiFi, UWB, vision, RFID signals, or indoor Global Positioning System (GPS). This requires the installation of additional hardware and/or modification of the working environment for precise positioning information of the mobile platform. As the dimensions of the staircase are known a priori, this knowledge can be used to localize the sTetro robot on the stairs. In this article, the geometry of the staircase has been exploited to localize the robot in 3D space with measurements from the onboard time-of-flight (ToF) range sensors only. The heading angle of the robot is also estimated with two ToF sensors installed in front of the sTetro robot. Results achieved by conducting experiments on real robot prove the efficacy of the proposed approach.

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible EEG levy was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering electricity price and weather forecasts as well as internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

The management and proper use of the Urban Public Transport Systems (UPTS) constitute a field as critical as little investigated according to its relevance and urgent idiosyncrasy within smart cities realm. In this paper, a newfangled approach by using the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation in order to build a system that makes the complete control of the UPTS a tangible reality.

This paper introduces a model-free, "on-the-fly" learning control strategy for arrays of energy converters with adjustable generator damping. The devices are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each state. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converter (WEC) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.

In accordance with problems such as difficulty in obtaining aerodynamic parameters of a quad-rotor model, the change of model parameters with external interference affects the control performances, an aerodynamic parameter estimation method and an adaptive attitude control method based on LADRC are designed. Firstly, the motion model, dynamics model and control distribution model of quad-rotor are established by using the aerodynamic and Newtonian Euler equations. Secondly, the identification tool CIFER is used to identify the aerodynamic parameters with large uncertainties in frequency domain and a more accurate attitude model of the quad-rotor is obtained. Then an adaptive attitude decoupling controller based on LADRC is designed to solve the problem of poor anti-interference ability of the quad-rotor, so that the control parameter b0 can be automatically adjusted to identify the change of the moment of inertia in real time. Finally, a semi-physical simulation platform is used for simulation verification. The results show that the adaptive LADRC attitude controller designed can effectively estimate and compensate the system's internal and external disturbances, and the tracking speed of the controller is faster and the precision is higher which can effectively improve system's anti-interference and robustness.

The goal of this contribution is an application of the Linear General Model Predictive Control (LGMPC). In this paper, stability of the LGMPC is proven by means of a demonstration of a Theorem stating a sufficient and constructive condition. This condition can be applied for calculating the weight matrices of the cost function in the optimisation problem in LGMPC. Lower bounds conditions are found for one of these matrices and then a system with saturation is taken into consideration. The conditions could be interpreted and discussions through physical aspects. The obtained results were tested by means of computer simulations and an example with a recover water process is considered.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self--tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. The scope is to analyse common points in the control objectives and the achievable results from the application of different solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

The management and proper use of the Urban Public Transport Systems (UPTS) constitute a field as critical as little investigated according to its relevance and urgent idiosyncrasy within smart cities realm. In this paper, a newfangled approach by using the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation in order to build a system that makes the complete control of the UPTS a tangible reality.

UAV network operation enables gathering and fusion from disparate information sources for flight control in both manned and unmanned platforms. In this investigation, a novel procedure for detecting runways and horizons as well as enhancing surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented real-world situations due to signal misalignment. We address this through a registration step to align the EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. We believe the fusion architecture developed holds promise for incorporation into head-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provided a basis rule selection in other signal fusion applications.

While the demand in reduction of CO₂ increases, the need for CO₂ sequestration processes is very high. One promising technology is the Carbon Capture and Storage (CCS). In this paper we refer to several papers which study the three main steps in CCS chain. CO₂ capture technologies, CO₂ transportation to the storage sites and the very critical step the CO₂ storage. Recently a novel method (mineral carbonation) for CO₂ sequestration has been proposed which is based in the reaction of CO₂ with calcium or magnesium oxides or hydroxides to form stable carbonate materials. Greece is a country that emits CO₂ mainly from the lignite fired power plant in Western Greece. After the study of the bibliographic references about the use of mineral carbonation process while injecting CO₂ in the appropriate geological forms we concluded that there are also these forms in our country and mainly in the area near to the power plant such as in sites Vourinos and Pindos. In these sites exist minerals rich in oxides and hydroxides of Ca, Mg and Fe representing the perfect materials for mineral carbonation.

In recent years, autonomous robots have proven capable of solving tasks in complex environments. In particular, robot manipulations in activities of daily living (ADL) for service robots have been in wide use. However, manipulations such as loading a dishwasher or folding laundry are difficult to automate robustly. In addition, manipulations of grasping multiple objects in domestic environments present difficulty. To perform those applications better, we developed robotic systems based on shared autonomy by combining the cognitive skills of a human operator with autonomous robot behaviors. In this work, we present techniques for integration of a shared autonomy system for assistive mobile manipulation and new strategies to support users in the domestic environment. We demonstrate that the robot can grasp multiple objects with random size at known and unknown table heights. Specifically, we developed three strategies for manipulation. From experimental results, we observed that the first strategy has the highest success rate (70% for random objects) up to 70 cm table height. Two other strategies perform better for 80 cm to 100 cm table height. The success rate of the second and third strategies shows an average 63.3% and 73.3%, respectively, for grasping random objects. We also demonstrated these strategies using two intuitive interfaces, a visual interface in rviz and a voice user interface with speech recognition, which are suitable for elderly people. In addition, the robot can select strategies automatically in random scenarios, which make the robot intelligent and able to make decisions independently in the environment. We obtained interesting results showing that the robot adapts to the environmental variation automatically. After these experimental demonstrations, our robot shows the capabilities for employment in domestic environments to perform actual tasks.

The identification of linear dynamic systems with static nonlinearities in the delta domain has been presented in this paper applying a firefly based hybrid meta-heuristic algorithm integrating Firefly algorithm (FA) and Gray wolf optimizer (GWO). FA diversifies the search space globally while GWO intensifies the solutions through its local search abilities. A test system with continuous polynomial nonlinearities has been considered for hammerstein and wiener system identification in continuous, discrete and delta domain. Delta operator modelling unifies system identification of continuous-time systems with discrete domain at higher sampling frequency. Pseudo random binary sequence, contaminated with white noise, has been taken up as the input signal to estimate the unknown model parameters as well as static nonlinear coefficients. The hybrid algorithm not only outperforms the parent heuristics of which they are constituted but also proves better as compared to some standard and latest heuristic approaches reported in the literature.

This work is located in a growing sector within the field of renewable energies, wave energy converters (WECs). Specifically, it focuses on one of the point absorbers wave (PAWs) of the hybrid platform W2POWER. With the aim of maximising the mechanical power extracted from the waves by these WECs and reduce their mechanical fatigue, the design of five different model predictive controllers (MPCs) with hard and soft constraints has been carried out. As contribution of this paper, two of the MPCs have been designed with the addition of an embedded integrator. In order to validate the MPCs, an exhaustive study on performance and robustness is realized through simulations carried out in which uncertainties in the WEC dynamics are considered. Furthermore, looking for realistic in these simulations, an identification methodology for PAWs is proposed and validated by means of real time series of a scale prototype.

For a dynamical system, it is known that the existence of a Lyapunov density implies almost global stability of an equilibrium. It is then natural to ask whether the existence of a common Lyapunov density for a nonlinear switched system implies almost global stability, in the same way as a common Lyapunov function implies global stability for nonlinear switched systems. In this work, the answer to this question is shown to be affirmative as long as switchings satisfy a dwell-time constraint with an arbitrarily small dwell time. As a straightforward extension of this result, we employ multiple Lyapunov densities in analogy with the role of multiple Lyapunov functions for the global stability of switched systems. This gives rise to a minimum dwell time estimate to ensure almost global stability of nonlinear switched systems, when a common Lyapunov density does not exist. The results obtained for continuous-time switched systems are based on some sufficient conditions for the almost global stability of discrete-time non-autonomous systems. These conditions are obtained using the duality between Frobenius-Perron operator and Koopman operator.

This review paper deals with recently proposed algorithms for distributed blind macro-calibration of sensor networks based on consensus (synchronization), not requiring any fusion center. The basic algorithm, performing the estimation of the local calibration parameters, is derived commencing from appropriate local criteria, and developing the corresponding gradient descent scheme. It is shown that the estimated parameters of the calibration functions asymptotically converge, in the mean-square sense and with probability one (w.p.1), to such values that ensure consensus on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are introduced: one using a simple compensation term, and a more robust one based on an instrumental variable. By utilizing stochastic approximation arguments it is shown that the modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. Convergence rate is analyzed in terms of an upper bound of the mean-square error. It is also shown that the communications between nodes can be completely asynchronous, which is of substantial importance for real-world applications. Suggestions for design of \textit{a priori} adjustable weights are given. Finally, it is shown that, if there is a subset of (precalibrated) reference sensors with fixed calibration parameters, the calibrated sensor gains and offsets of the rest of the sensors do not achieve consensus - they converge to different points dictated by the reference sensors and the network characteristics. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples.

Reliable and robust control of power converters is a key issue in the performance of numerous technological devices. In this paper we show a design technique for the control of a DC-DC buck converter with a switching technique that guarantees not only good performance but also global stability. We show that making use of the contraction theorem in the Jordan canonical form of the buck converter, it is possible to find a switching surface that guarantees stability but it is incapable of rejecting load perturbations. To overcome this, we expand the system to include the dynamics of the voltage error and we demonstrate that the same design procedure is not only able to stabilize the system to the desired operation point but also to reject load, input voltage and reference voltage perturbations.

Some professionals in the industrial community are not as familiar with digital PID controllers as the analog one. So the art work of this present paper is to show properly conditions and aspects to reach stability in the process control, as also to avoid instability, through the use of digital PID controllers, with the objective of help the industrial community. The discussed control strategy used to reach stability is to manipulate variables that are directly proportional to their controlled variables. To validate and to put the exposed principles into practice, it was done two simulations of a continuous fermentation tank process start-up with the yeast strain S. Cerevisiae NRRL-Y-132, in which the substrate feed stream contains different types of sugar derived from cane bagasse hydrolysate. These tests were carried out through the resolution of conservation laws, more specifically mass and energy balances, in which the fluid temperature and level inside the tank were the controlled variables. The results showed that if the exposed proceedings are followed, the system stabilize without difficulties.

Most systems that are the subject of control engineering studies have some non-linearity. An example of this is the horizontal cylindrical tank, commonly used in process industries. To deal with cases like this, several control theories have been developed over time, each one presenting better results in certain systems. This work presents an alternative for the control of nonlinear systems, without necessary modeling or previous information about the system, based on a new optimization law for the artificial neural network training in real time.

A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather, occupancy and lighting. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, non-linear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a ``brick wall'' preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 3\% reduction in costs vs. a ``brick-wall'' MPC approach with similar comfort and 13\% reduction in costs vs. a standard night setback strategy. CCPSO also reduced peak-hours demand by 3\% vs. the ``brick-wall'' strategy and 15\% vs. standard night-setback. At the same time, the CCPSO strategy increased off-peak energy consumption by 15\% vs. the ``brick-wall'' strategy. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours.

A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather, occupancy and lighting. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, non-linear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a ``brick wall'' preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 3\% reduction in costs vs. a ``brick-wall'' MPC approach with similar comfort and 13\% reduction in costs vs. a standard night setback strategy. CCPSO also reduced peak-hours demand by 3\% vs. the ``brick-wall'' strategy and 15\% vs. standard night-setback. At the same time, the CCPSO strategy increased off-peak energy consumption by 15\% vs. the ``brick-wall'' strategy. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours.

The article presents a non-destructive test system based on electrical impedance tomography for monitoring flood embankments. The technology of cyber-physical systems and the Internet of Things with the use of electrical impedance tomography enables real-time monitoring of flood embankments. This solution provides a visual analysis of damage and leaks, which allows for quick and effective intervention and possible prevention of danger. A dedicated solution based on the IT structure, dedicated laboratory models and a dedicated measurement system with various types of sensors and machine learning algorithms for image reconstruction has been developed. The system includes specialized intelligent devices for tomographic measurements. The application contains the analysis of anomalies occurring in the structure of the object as a result of damage or danger and breaking the shaft during the flood. The presented solution enables ongoing monitoring of objects by collecting measurement results, forecasts and simulations. The main advantage of the proposed system is the spatial ability to analyse shafts, high accuracy of imaging and high speed of data processing. The use of tomographic techniques in conjunction with image reconstruction algorithms allow for non-invasive and very accurate spatial assessment of humidity and damages of flood embankments. The presented results show the effectiveness of the presented research.

This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

Control as a Service (CaaS) is an emerging paradigm where low-level control of a device is moved from a local controller to the Cloud, and provided to the device as an on-demand service. Among its many benefits, CaaS gives the device access to advanced control algorithms which may not be executable on a local controller due to computational limitations. As a step toward 3D printer CaaS, this paper demonstrates the control of a 3D printer by streaming low-level stepper motor commands (as opposed to high-level G-codes) directly from the Cloud to the printer. The printer is located at the University of Michigan, Ann Arbor, while its stepper motor commands are calculated using an advanced motion control algorithm running on Google Cloud computers in South Carolina and Australia. The stepper motor commands are sent over the Internet using the user datagram protocol (UDP) and buffered to mitigate transmission delays; checks are included to ensure accuracy and completeness of the transmitted data. All but one part printed using the cloud-based controller in both locations were hitch free (i.e., no pauses due to excessive transmission delays). Moreover, using the cloud-based controller, the parts printed up to 54% faster than using a standard local controller, without loss of accuracy.

In this work, an algorithm was developed to measure the respiration rate for an embedded device that can be used by a field robot for relief operation. With this algorithm, the rate measurement was calculated based on direct influences of respiratory-induced intensity variation (RIIV) on blood flow in cardiovascular pathways. For that, a photoplethysmogram (PPG) sensor was used to determine changes in heartbeat frequencies. The PPG sensor readings were filtered using an Information Filter and a Fast Fourier transform (FFT) to determine the state of RIIV. With a relatively light initialization, the information filter can estimate unknown variables based on a series of measurements containing noise and other inaccuraties. Therefore, this filter is suitable for application on an embedded device. For faster calculation time in the implementation, the FFT analysis was calculated only for a major peak in the frequency domain. Test and measurement of respiration rate was conducted based on the device algorithm and spirometer. Heartbeat measurement was also evaluated by comparing the heartbeat data of the PPG sensor and the medical tool kit. Based on the test, the implemented algorithm can measure respiration rate with about 80% accuracy compared with the spirometer.

A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather, occupancy and lighting. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, non-linear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a ``brick wall'' preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 3\% reduction in costs vs. a ``brick-wall'' MPC approach with similar comfort and 13\% reduction in costs vs. a standard night setback strategy. CCPSO also reduced peak-hours demand by 3\% vs. the ``brick-wall'' strategy and 15\% vs. standard night-setback. At the same time, the CCPSO strategy increased off-peak energy consumption by 15\% vs. the ``brick-wall'' strategy. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours.

This paper presents a system that merges computer vision and surface electromyography techniques to carry out grasping tasks. To perform this, the vision-driven system is used to compute pre-grasping poses of the robotic system based on the analysis of tridimensional object features. Then, the human operator can correct the pre-grasping pose of the robot using surface electromyographic signals from the forearm during wrist flexion and extension. Weak wrist flexions and extensions allow a fine adjustment of the robotic system to grasp the object and finally, when the operator considers that the grasping position is optimal, a strong flexion is performed to initiate the grasping of the object. The system has been tested with several subjects to check its performance showing a grasping accuracy of around 95% of the attempted grasps which increases by around a 9% the grasping accuracy of previous experiments in which electromyographic control was not implemented.

This paper presents a new adaptive and optimal algorithm for the trajectory tracking control of a quadrotor using iterative learning algorithm (ILA) and enumerative learning algorithm. Ordinarily the ILA, as an adaptive method, can perform well with PID control to improve the controller’s performance for a nonlinear system. Quadrotors are considered as non-linear and unstable systems which the use of an adaptive and optimal controller can increase its stability and decrease error level. In this method, a PID controller is proposed for the outer and inner control loops of a quadrotor and the ILA is used to adapt PID control gains. Subsequently, an enumerative learning algorithm is used to optimize the learning rates of the ILA. For this purpose, at first, the dynamic model of the quadrotor is acquired. After that, the structure of the inner and outer control loops is defined. In the end, the simulation results for the trajectory tracking control of a quadrotor are demonstrated. Through simulation, it is concluded that as time increases, the performance of the suggested control method in trajectory tracking control becomes better and better and error signals convergence to zero.

The increasing variability in power plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO₂ emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.

This article addresses the trajectory tracking between two non-identical systems with chaotic properties. We employ the Rossler chaotic and RCL-shunted Josephson junctions model in similar phase space to study trajectory tracking. In order to achieve the goal tracking, we afford two stages to approximate the target tracking. The first stage utilizes the active control technique to transfer the output signal from the RCLs-J system into the quasi-Rossler system. Then next, the RCLs-J system employs the proposed the iterative learning control scheme and the control signal from the drive system to trace the trajectory of Rossler system. The numerical results demonstrate the proposed method and the tracking system is asymptotically stable.

This research presents alternative solutions for an Energy Efficiency Management System (EEMS) serving as a framework for optimizing the energy consumption algorithm and lowering energy consumption. First, a monitoring Wireless Sensor and Actuator Network (WSAN) is used for sensing, measuring, gathering data, and modeling all the dynamic disturbance parameters of the rooms in the building. Second, integrated software for metering and controlling the processes of digital data flow is used. Third, an alternative solution is proposed to reduce energy consumption. The primary benefits of this system are real-time monitoring; rapid, alternative solutions; and the ability to make a prudent decision on how to lower energy consumption. The system shows instant and accumulated solutions for short and long-term time planning. The solutions identified can be implemented in the same buildings under the same circumstances. The universities of Majmaah and Philadelphia have buildings with similar infrastructure. The system was applied to the buildings at Philadelphia University. The results were generalized to both universities. After implementation, the energy consumption of the EEMS using WSAN (based on the monitoring was reduced up to 23% when compared to that of the initial state.

This paper focuses on the integrated guidance and autopilot design with control input saturation in the end-game phase of hypersonic flight. Firstly, uncertain nonlinear integrated guidance and autopilot model is developed with third actuator dynamics, where the control surface deflection has magnitude constraint. Secondly, neural network is implemented in extended state observer (ESO) design, which is used to estimate the complex model uncertainty, nonlinearity and state coupling. Thirdly, a command filtered back-stepping controller is designed with hybrid sliding surfaces to improve the terminal performance. In the process, different command filters are implemented to avoid the influences of disturbances and repetitive derivation, meanwhile solve the problem of unknown control direction caused by saturation. The stability of closed-loop system is proved by Lyapunov theory, and the principles abided by the controller parameters are concluded through the proof. Finally, series of 6-DOF numerical simulations are presented to show the feasibility and validity of the proposed controller.

The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 2014-12-01 to 2018-01-08 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

In this paper, by evaluating extreme value of the qth-power current, a torque sharing function (TSF) family for reducing the torque ripples in the switched reluctance motor (SRM) is proposed. The optimization criteria of the TSF has two secondary objectives, including the maximization of the torque-speed range and the minimization of copper loss. The evaluation indices in terms of the peak phase current, the rms phase current, and the torque ripple factor are compared between the proposed TSF family and four conventional TSFs including linear, sinusoidal, exponential, and cubic TSFs. An optimization objective function that combines the maximum absolute value of the rate-of-change of the flux linkage (MAV-RCFL) and the qth-power of current is proposed and a weighting factor is used to balance the influence of the two optimization objectives. An optimal TSF can be easily obtained by solving the optimization problem from the TSF family. The proposed TSF is validated by using simulations and experiments with a three-phase 6/4 SRM with 7.5 kW, 3000 rpm, and 270 V DC-link voltage. The dynamic simulation model is implemented in Matlab/Simulink. The results demonstrate the validity and superiority of the proposed control method; the optimal TSF provides better torque-speed performance, and a better reduction in copper loss and torque ripples at high speed compared to the conventional TSFs obviously.

Landing is one of the most dangerous tasks in all the operations on the aircraft carrier, and the landing safety is very important to the pilot and the flight deck operation. The problem of landing path selection is studied in this paper as there several candidates corresponding to different situations. A fuzzy path selection strategy is proposed to solve the problem considering the fuzziness of environmental information and human judgment, and the goal is to provide the pilot with more reasonable decision. The strategy is based on Fuzzy Multi-attribute Group Decision Making (FMAGDM), which has been widely used in industry. Firstly, the background of the path selection problem is given. Then the essential elements of the problem are abstracted to build the conceptual model. A group decision-making method is applied to denote the preference of each decision maker for each alternative route, and the optimal landing path under the current environment is determined taking into account the knowledge and the weight of both decision makers. Experimental studies under different setups, i.e., different environments, are carried out. The results demonstrate that the proposed path selection strategy is validated in different environments, and the optimal landing paths corresponding to different environments can be determined.

This paper first discusses a fractional-order Liu system of order as low as 2.7 and shows its chaotic characteristics by carrying out numerical simulations such as Lyapunov exponents, bifurcation diagrams and phase portraits. Then, by using the topological horseshoe theory and computer-assisted proof, the existence of chaos in the system is verified theoretically. Finally, the fractional-order system is implemented on a Field Programmable Gate Array (FPGA) and the results obtained show that the fractional-order Liu system is indeed chaotic.

Aiming at the defects of low precision and time cumulative error, an external wireless signal weighted centroid localization algorithm aided inertial positioning method is designed in this paper. According to the signal strength of each anchor node received at the test point, the distance between the anchor node and the anchor node is obtained by using the attenuation model of the wireless signal. Three anchor nodes are used to measure the distance between the anchor node and the measured point. We can obtain the area to be measured according to the actual situation, the position of the measured point is obtained by the weighted centroid localization algorithm and a combined model of wireless signal aided inertial navigation system is established. The simulation results show that the method can greatly improve the positioning accuracy and restrain the divergence of the longitude error and latitude error.

Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and compares existing algorithms for line fitting applicable also in case of strong covariances between the coordinates at each single data point, which must not be neglected if range-bearing sensors are used. Besides, particularly the determination of the covariance matrix is considered, which is required for stochastic modeling. The main contribution is a new error model of straight lines in closed form for calculating fast and reliably the covariance matrix dependent on just a few comprehensible and easily obtainable parameters. The model can be applied widely in any case when a line is fitted from a number of distinct points also without a-priori knowledge of the specific measurement noise. By means of extensive simulations the performance and robustness of the new model in comparison to existing approaches is shown.

The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. This paper proposes a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients and (4) the fitting of the historical Bitcoin market data from 2014-12-01 to 2018-01-08.

This paper is concerned with bump-less transfer of parametrized disturbance observer based controller (DOBC) with Individual Pitch Control (IPC) strategy for full load operation of wind turbine. Aerodynamic cyclic loads are reduced by tuning multivariable DOBC with the objective to reduce output power fluctuation, tower oscillation and drive-train torsion. Furthermore tower shadow and wind shear effect are also mitigated using parametrized controller. A scheduling mechanism between two DOBC is developed and tested on Fatigue, Aerodynamics, Structures, and Turbulence ( FAST) code model of National Renewable Energy Laboratory (NREL)’s 5 MW wind turbine. The closed-loop system performance is assessed by comparing the simulation results of proposed controller with a fixed gain and Linear Parameter Varying (LPV) DOBC with Collective Pitch Control (CPC) for full load operation. It is tested with step changing wind to see the behavior of the system under step change with wind shear and tower shadow (cyclic load) effects. Also turbulent wind is applied to see the smooth transition of the controllers. It can be concluded from the results that the proposed parametrized control DOBC with IPC shows smooth transition from one controller to another by interpolation. Moreover fatigue of the gear and tower due to wind shear and tower shadow effects are reduced considerably by the proposed controller as compared to collective pitch control.

GNSS receivers are nowadays commonly used in monitoring applications, e.g., in estimating crustal and infrastructure deformations. This is basically due to the recent improvements in GNSS instruments and methodologies that allow high precision positioning, 24 h availability and semiautomatic data processing. In this paper, GNSS estimated deformations on a dam structure have been analyzed and compared with pendulum data. This study has been carried out for the Eleonora D’Arborea (Cantoniera) dam, which is in the Sardinia Island. Time series of pendulum and GNSS over a time span of 2.5 years have been aligned so to be comparable. Analytical models fitting these time series have been estimated and compared. Those models were able to properly fit pendulum data and GNSS data, with standard deviation of residuals smaller than one millimeter. This encouraging results led to the conclusion that GNSS technique can be profitably applied to dam monitoring allowing a denser description, both in space and time, of the dam displacements than the one based on pendulum observations.

The current work addresses the control of two-input two-output (TITO) Wood and Berry model of a binary distillation column. The controller design problem is formulated in terms of multivariable H_∞ control synthesis. The controller structure takes the form of simplest static output feedback (SOF) control. The controller synthesis is performed using a hybrid approach of blending linear matrix inequalities (LMI) and genetic algorithm (GA). The performance of the static output feedback controller is compared with three other controllers designed for Wood and Berry model available in the literature. The first simulation study is performed for the case of tracking a unit step command in the presence of a step change in output disturbance. A second simulation study is performed for rejecting a change in sinusoidal output disturbance.

In this paper, an extensive study on the operation, control and performance of a hybrid photovoltaic and wind turbine (PV-WT) system is presented. The study includes the operation of the hybrid systems under different weather conditions which are reflected on a change in the wind speed and irradiance. Various loading conditions are investigated such a change in active and reactive power demands for static and dynamic loads. The performance of the hybrid system is examined via MATLAB/SIMULINK simulations.

Incorporating linear-scanning MEMS micromirrors into Fourier transform spectral acquisition systems can greatly reduce the size of the spectrometer equipment, making portable Fourier transform spectrometers (FTS) possible. How to minimize the tilting of the MEMS mirror plate during its large linear scan is a major problem in this application. In this work, an FTS system has been constructed based on a biaxial MEMS micromirror with a large piston displacement of 180 μm, and a biaxial H∞ robust controller is designed. Compared with open-loop control and PID closed-loop control, H∞ robust control has good stability and robustness. The experimental results show that the stable scanning displacement reaches 110.9 μm under the H∞ robust control, and the tilting angle of the MEMS mirror plate in that full scanning range falls within ±0.0014°. Without control, the FTS system cannot generate meaningful spectra. In contrast, the FTS yields a clean spectrum with an FWHM spectral linewidth of 96 cm-1 under the H∞ robust control. Moreover, the FTS system can maintain good stability and robustness under various driving conditions.

This research not only dedicated a less restrictive method of iteration-varying function for a learning control law to design a controller but also synchronize two nonlinear systems with free time-delay. In addition, the mathematical theory of system synchronization has proved rigorously and the theory verified through an example to demonstrate the behavior of each parameter in the theory. The design of a controller using the iterative learning control law is significant for robotic tracking. The controller in this research generates a feed-forward control input using the error dynamics among the drive-response systems. The error dynamics satisfies the Lyapunov function and the combination of output errors, which respectively represented relative estimated differences of the drive-response systems. The iterative learning control rule serves the function of a filter adding previous control error after the end of each iteration. The numerical example of a synchronous system is given a Lorenz system for driving and another with the iterative learning control law for response under different initial condition. The results verify and demonstrate the proposed mathematical theory. The simulation exhibits consistency in the behavior of each parameter to match mathematical theory.

Serious damage caused by floor heave in the coal given chamber of a vertical coal bunker is one of the challenges faced in underground coal mines. Engineering practice shows that it is more difficult to maintain the coal given chamber (CGC) than a roadway. More importantly, repairing the CGC during mining practice will pose major safety risks and reduce production. Based on the case of the serious collapse that occurred in the bearing structure of the CGC at the lower part of the 214# coal bunker in Xiashijie mine, China, this work analysed (i) the main factors influencing floor heave and (ii) the failure mechanism of the load-bearing structure in the CGC using FLAC^2D numerical models and expansion experiment. The analysis results indicate that: the floor heave, caused mainly by mine water, is the basic reason leading to the instability and repeated failure of the CGC in the 214# coal bunker. Then a new coal bunker, without building the CGC, is proposed and put into practice to replace the 214# coal bunker. The FLAC^3D software program is adopted to establish the numerical model of the wall-mounted coal bunker (WMCB), and the stability of the rock surrounding the WMCB is simulated and analysed. The results show that: (1) the rock surrounding the sandstone segment is basically stable. (2) The surrounding rock in the coal seam segment, which moves into the inside of the bunker, is the main zone of deformation for the entire rock mass surrounding the bunker. Then the surrounding rock is controlled effectively by means of high-strength bolt–cable combined supporting technology. According to the geological conditions of the WMCB, the self-bearing system, which includes (i) H-steel beams, (ii) H-steel brackets, and (iii) self-locking anchor cables, is established and serves as a substitute for the CGC to transfer the whole weight of the bunker to stable surrounding rock. The stability of the new coal bunker has been verified by field testing, and the coal mine has gained economic benefit to a value of 158.026174 million RMB over three years. The new WMCB thus made production more effective and can provide helpful references for construction of vertical bunkers under similar geological conditions.

Quantitative and qualitative fuzzy measures have been proposed to hesitant fuzzy sets (HFSs) from different points. However, few of the existing HFSs fuzzy measures refer to the grey relational analysis (GRA) theory. Actually, the GRA theory is very useful in the fuzzy measure domain, which has been developed for such the intuitionistic fuzzy sets. Therefore, in this paper, we apply the GRA theory to the HFSs and interval-valued hesitant fuzzy sets (IVHFS) domain. We propose the HFSs grey relational degree, HFSs slope grey relational degree, HFSs synthetic grey relational degree and IVHFSs grey relational degree, which describe the closeness, the variation tendency and both the closeness and variation tendency of HFSs and closeness of IVHFSs, respectively, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs and IVHFSs MADM problems. Finally, combined with two practical MADM examples about energy policy selection with HFSs information and emergency management evaluation with IVHFSs information, we obtain the most desirable decision results, and compared with the previous methods, the validity, effectiveness and accuracy of the proposed grey relational degree for HFSs and IVHFSs are demonstrated in detail.

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.

The RPL protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource; so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfiguration.

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar the targets are detected by sonar equation (with constant threshold) which increase the detection error in shallow water. Purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.

Increasingly, there is a focus on utilising renewable energy resources in a bid to fulfil increasing energy requirements and mitigate the climate change impacts of fossil fuels. While most renewable resources are free, the technology used to usefully convert such resources is not and there is an increasing focus on improving the conversion economy and efficiency. To this end, advanced control technologies can have a significant impact and is already a relatively mature technology for wind turbines. Though hydroelectric plants can use simple regulation systems, significant benefits have been shown to accrue from the appropriate use of the same control methods designed for wind turbine plants. This represents the key point of the paper. In fact, to date, the application communities connected with wind and hydraulic energies have had little communication, resulting in little cross fertilisation of control ideas and experience, particularly from the more mature wind area to hydrodynamic systems. Therefore, this paper examines the models and the application of control technology across both domains, both from a comparative and contrasting point of view, with the aim of identifying commonalities in models and control objectives, as well as potential solutions. Key comparative reference points include the articulation of the exployed models, specification of control objectives, development of high--fidelity simulators, and development of solution concepts. Not least, in terms of realistic system requirements are the set of physical and constraints under which such renewable energy systems must operate, and the need to provide reliable and robust control solutions, which respect the often remote and relatively inaccessible location of many onshore and offshore deployments.

This paper presents a methodology for determining the sensitivity of the main air flow directions in ventilation subnetworks to changes of aerodynamic resistance and of air density in mine workings. Formulae for determination of the sensitivity of the main subnetwork air flows by establishing the degree of dependency of the air volume stream in a given working on the variations in resistance or air density of other workings of the network have been developed. They have been implemented in the VentGraph mine ventilation network simulator. This software, widely used in Polish collieries provides an extended possibility to predict the process of ventilation, air distribution and, in the case of underground fire, also the spread of combustion gasses. The new method facilitates assessment by mine ventilation services of the stability of ventilation systems in exploitation areas and determine of the sensitivity of the main subnetwork air flow directions to changes of aerodynamic resistance and air density. Recently in some Polish collieries new longwalls are developed in seams located deeper then the bottom of the intake shaft. Such solution is called “exploitation below the level of access” or “sublevel”. The new approach may be applied to such developments to assess the potential of changes of direction and air flow rates. In addition, interpretation of the developed sensitivity indicator is presented. While analyzing air distributions for sublevel exploitation, application of current numerical models for calculations of the distribution results in tangible benefits, such as the evaluation of the safety or risk levels for such exploitation. Application of the VentGraph computer program, and particularly the module POŻAR (fire) with the newly developed options, enables an additional approach to the sensitivity indicator in evaluating air flow safety levels for the risks present during exploitation below the level of the intake shaft. The analyses performed and examples presented enabled useful conclusions in mining practice to be drawn.