Engineering

Abstract: In this work, we present a method for correcting attitude-dependent fluctuations in fixed-bias Langmuir probe ion density measurements from small, spin-stabilized sounding rocket dropsondes. The approach resolves dropsonde attitude using three-axis magnetometer data, observations from the Langmuir probe itself, and an analytical model of the sonde’s rotational motion. Together, these inputs constrain the dropsonde’s angular momentum vector in inertial space and enable reconstruction of its time-resolved attitude without requiring prior knowledge of the sonde’s orientation at deployment. The derived attitude solution is then used to correct the Langmuir probe ion density measurements for orientation-dependent changes in collection geometry caused by the sonde’s precessional motion. This technique is applied to data from the August 2022 Sporadic E Electrondynamics Demonstration (SpEED Demon) sounding rocket campaign, in which four dropsondes were ejected. Validation is performed by comparing attitude-corrected dropsonde ion density measurements to those of the main payload in a near-coincident measurement regime. The corrected data show strong agreement with the main payload observations, demonstrating that this technique can recover physically consistent ion density profiles from spin-stabilized dropsonde measurements.

Abstract: Objective, portable sensor-based assessment of human motion is vital for monitoring motor development and designing rehabilitation applications. This study investigated upper-limb motor characteristics during 2D circular target tracking across different age groups and speeds. Fifty-one participants were divided into three groups: lower elementary children, upper elementary children, and adults. Using a tablet- and stylus-based input device, suitable for human-computer interaction systems, participants tracked targets at three speeds. Each trial included target-visible and target-invisible segments to separately measure feedback- and feedforward-dominant errors, derived as a tracer error ratio. The results revealed similar error ratios for both child groups across all speeds, whereas adults exhibited lower ratios. As tracking speed increased, the tracer error ratio decreased in all groups, with only adults achieving a ratio below one at high speeds. These findings support the hypothesis that cerebellar motor models for position and speed control continue to develop from childhood into adulthood. The study concludes that this portable stylus-based approach serves as a practical digital metric for effective upper-limb assessment.

Abstract: Systems-of-systems (SoS) integrate constituents that retain operational and managerial independence, and that independence makes their engineering artifacts prone to drift: a change in one constituent does not propagate, misalignment surfaces late, and outputs accumulate that trace to no validated need. A recurring consequence is the stalled SoS, years of effort across independent teams yielding no usable, connected baseline. The SoS-engineering and complex-system-governance literatures describe what governance should achieve but offer little procedure for recovering an SoS that has lost coherence. This paper introduces ADAPT (Anchor, Dependency, Allocation, Production, Traceability), whose five components operationalize the integrating purposes (communication, coordination, control, and integration) for SoS recovery, enhancing rather than replacing control boards and program offices. It is illustrated through four de-identified cases (feasibility evidence, not a test) under an explicit case-study protocol; in the lead case, six prior teams, comparably resourced, had produced no usable baseline and ADAPT produced an approved one in four months against a documented eighteen-month plan of record. The evidence is retrospective, single-organization, and uncontrolled, supporting the propositions analytically, not statistically; a pre-registered confirmatory study with falsification conditions is specified, and ADAPT’s measures are additionally exercised on an independent public SoS dataset (Dronology/SAFA) as external construct validation.

Abstract: How to optimize the single-path transmission is a common issue for resource schedul-ing and network operation in a complex network, where cost and flow must be bal-anced. With the expansion of network scale, traditional optimization methods suffer from rapidly increasing computational complexity in high-dimensional decision space with the nonlinear cost-flow relationship. To solve this problem, this paper proposes a quantum optimization framework for single-path transmission for the first time. In particular, logarithmic-qubit encoding is adopted for path and flow selection, and cost-flow bias coefficients and path flows are embedded into the quadratic uncon-strained binary optimization (QUBO) model, compressing the decision space exponen-tially from O(2^j+k) to O(j(k+i)). The quantum approximate optimization algorithm (QAOA) is then used to obtain the optimal solutions including the selected single path and its discretized optimal flow corresponding to the set bias coefficients. If a precise optimal flow is wanted, the traditional spline interpolation can be applied to refine the discretized flow without introducing much calculation burden. Experimental results on a network with 122 nodes and 131 edges show that the proposed method can achieve high-quality single-path optimization result.

Abstract: We address regulation of an ethanol-to-hydrocarbon packed-bed reactor (HZSM-5) with input redundancy by combining a centralized multivariable PI controller with Davison-style shaping and a Moore–Penrose control-allocation layer. A one-cell finite-volume model is derived from axial mass and energy balances with Danckwerts boundary conditions and used to identify a rectangular, highly anisotropic steady-state gain matrix G for the two controlled variables—bed temperature T and in-bed concentration C_i—and three manipulated inputs (superficial velocity μ, inlet temperature T₀, and coolant temperature T_c). Because G is ill-conditioned, the allocator employs the pseudoinverse (with Tikhonov regularization and physical scaling) to distribute the PI demand among actuators, while setpoint prefiltering limits proportional kick and back-calculation anti-windup preserves bias-free recovery under amplitude/rate limits. The numerical allocation clarifies actuator roles: T_c provides dominant thermal authority for T, μ primarily shapes residence time for C_i, and T₀ acts as a trim to reduce interaction. Closed-loop simulations show fast, well-damped tracking and zero steady-state error for reference changes. Under constraints, the augmented loop degrades gracefully without offset. Against a temperature-centric SISO PID baseline, the proposed design markedly reduces peaking (start-up overshoot ≈40% with PID) and ringing, with lower total actuator variation. The results position centralized PI with pseudoinverse allocation as an implementation-ready, interpretable alternative to MPC for over-actuated biofuel reactors, offering robust, bias-free performance at low computational cost.

Abstract: For the drilling engineer, an instrumented drillstring is a resource-constrained, communication-starved, safety-critical edge node: it must estimate its own state, schedule its own work, and survive faults while the surface is too far and the telemetry too narrow to intervene in time. The Apollo Guidance Computer (AGC) met a structurally similar predicament in 1969 under documented and severe constraints, and we argue that its systems-engineering playbook, not its algorithms, is an underused reference architecture for drilling automation. We make four mappings precise and bound each. Apollo’s priority-scheduled, fault-tolerant executive, the design that turned the first 1202 program alarm into a survivable event, is the right structure for protecting a safety-critical drilling-automation task under overload; a reproducible model shows that priority with deliberate work-shedding holds a safety-critical deadline that a priority-blind scheduler misses. Apollo flew a numerically stable square root (Potter–Battin) Kalman filter in fixed point; on an ill-conditioned survey problem we show that this factored form stays positive semidefinite where the conventional and even the Joseph-stabilized covariance updates fail, and we reframe the standard wellbore position-uncertainty model as the propagation half of that estimator. Apollo’s fixed-point, snapshot-telemetry edge discipline matches a downhole tool that has less surface bandwidth than a 1969 spacecraft. Finally, Apollo’s verb-noun interface, in which the machine proposes and the human commits, is a precedent for the driller’s supervisory console. We delimit the analogy at the point where the subsurface denies an absolute position fix and state the breaking points as a research agenda.

Abstract: This paper presents a dual-quaternion framework for the rigid kinematic modeling and validation of Kresling origami robots undergoing prescribed FOLD–TWIST–FOLD motion. The formulation is developed in the rigid-origami regime, where triangular panels are treated as rigid bodies and crease lines are modeled as fixed revolute axes. Unit dual quaternions are used to represent the rigid transformations of the Kresling panels and cells in SE(3), while a DQ-parametric homotopy organizes the motion into three sequential phases: fold, twist, and fold. The numerical validation is performed on a three-cell hexagonal Kresling origami robot with Ns = 6, R = 48 mm, total initial height H0 = 60 mm, and initial cell twist of 30°. The prescribed command consists of Δh1 = −2 mm, Δφ2 = 5°, and Δh3 = −2 mm. The final run achieves this command with negligible residual and reports an overall PASS. Ring-edge, primary-crease, di-agonal-crease, and triangular-panel rigidity are preserved over the full homotopy trajectory, with maximum relative physical residual of order 10−10. Unit dual-quaternion consistency is maintained at order 10−16, and the DQ panel-map error remains below 10−8 mm. Bennett-limit compatibility is evaluated as a local diagnostic in the intersecting-axis regime d = 0, which is appropriate for the local Kresling crease-axis geometry. The validation checks 37 homotopy states and confirms that all rigid-origami, DQ, Bennett-limit, homotopy, and conditioning gates are satisfied. These results provide a numerical illustration of the proposed DQ-based formulation for rigid Kresling origami FOLD–TWIST–FOLD robots and support its use as a basis for future synthesis, validation, and optimization of rigid origami robotic mechanisms.

Abstract: This paper presents a hybrid computer vision framework that explicitly decouples geometric stem measurement from visual foliage condition classification for automated post-harvest quality assessment of Ruscus hypophyllum ornamental foliage. The proposed approach addresses a gap in the literature where heterogeneous quality attributes are typically treated within a single unified learning framework. In the first stage, stem size is estimated using a pixel-based geometric method that incorporates trigonometric orientation correction and spatial calibration via a reference marker of known length, enabling accurate conversion of image measurements to real-world physical dimensions. In the second stage, foliage condition is classified as good or poor using supervised machine learning models trained on Bag of Features representations extracted with the SIFT descriptor. A dataset of 1,233 Ruscus hypophyllum images was acquired under controlled conditions using a consumer-grade smartphone camera and processed using open-source Python libraries. Twenty-four classifier configurations across six model families were evaluated using stratified 10-fold cross-validation. The geometric estimation stage achieved a Mean Absolute Error (MAE) of 1.2 mm, a Root Mean Square Error (RMSE) of approximately 1.3 mm, and a size categorization accuracy of 99.84%. For foliage condition classification, the Linear Support Vector Machine achieved the best performance, with an accuracy of 92.4 ± 1.0% and an F1-score of 91.1±1.2%, outperforming all other evaluated configurations. The proposed framework provides an interpretable, computationally efficient, and accessible solution for automated foliage quality grading, with potential applications in export-oriented ornamental foliage processing facilities.

Abstract: Natural landscape morphology is the emergent result of continuous, reciprocal interactions between biological agents and their physical environment. While agent-based modeling has been successfully utilized to simulate human-environment dynamics in urban settings, its application to understanding non-human geomorphological change remains an underexplored frontier. This paper presents a bio-inspired multi-agent system framework to investigate how individual animal behaviors — specifically those of the North American beaver — shape the development of adaptive landscapes. We introduce a specialized agent-based architecture in which key beaver behaviors are modeled by two different agent types with distinct ecological roles: Explorers, which drive spatial diffusion and resource identification, and Builders, which reinforce environmental traces through localized engineering. Utilizing a dynamic environment characterized by seasonal vegetation cycles and coupled hydrology, we demonstrate that simple behavioral heuristics can trigger significant geomorphological shifts. Our results show that while smaller colonies maintain ecological homeostasis, larger ones cross a threshold that activates hydrological expansion and riverbed widening. This work provides an open-source tool and methodology for simulating regenerative strategies harnessing natural processes and non-human agency, for use in landscape architecture and environmental design.

Abstract: Visual simultaneous localization and mapping in indoor dynamic environments remains challenging because moving objects introduce unreliable correspondences, whilst removing dynamic feature points often leaves insufficient static features in low-texture regions. This paper proposes a robust visual SLAM framework that combines semantic-geometric feature filtering with texture-aware feature compensation to improve pose estimation under dynamic interference. The framework first identifies potentially dynamic regions through pixel-level semantic segmentation and removes features associated with highly dynamic objects. To reduce over-filtering and address semi-static objects, depth variation and multi-view geometric consistency are further used to distinguish static and moving feature points across consecutive frames. After dynamic filtering, a learned local feature extractor is introduced to improve descriptor discriminability and feature density in reliable static regions. Two additional modules, semantic confidence weighting and static region feature compensation, adaptively adjust feature extraction thresholds so that low-texture but geometrically useful areas can contribute more stable correspondences. The proposed system is implemented within a visual SLAM pipeline and evaluated on public dynamic RGB-D benchmarks, including TUM and Bonn sequences. Experimental results indicate that the method improves localization robustness in high-dynamic scenarios and reduces trajectory error compared with conventional ORB-based SLAM and several dynamic SLAM baselines. The study demonstrates the potential of combining semantic priors, geometric verification and adaptive feature enhancement for dynamic indoor localization.

Abstract: Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm x 300 mm x 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user's traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal-odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms.

Abstract: In this study, coevolution approach rather than evolution is considered to analyse how enabling technologies influence mechatronics progress, advancements and innova-tions. Attention of this work is given to reveal the mutual interaction between mecha-tronics technology and its enabling technologies, since mechatronics methodologies, engineering tools and applications support their advancements, along their coevolu-tion with mechatronics. With their coevolution mechatronics technology reach new maturity levels to fulfil the demand of many industrial domains and other economic sectors for new advanced innovative equipment. For systematic reasons, the impact of each enabling technology on the evolution of mechatronics is investigated and the support of mechatronics to the advancement of the considered enabling technology is examined using carefully selected publications after an exhaustive and focused search. The coevolution of mechatronics is considered through the progress and synergy of its enabling technologies in a reciprocal mode. The investigated and demonstrated co-evolution of mechatronics with its enabling technologies is expected to contribute to identifying the future challenges of mechatronics that are briefly presented in the sec-tion of discussion. The paper concludes with hints for future research and develop-ment work under the proposed coevolution conceptualization and investigation.

Abstract: Standard H_∞ controller synthesis produces robust controllers with well-shaped sensitivity and complementary sensitivity transfer functions, S and T. However, at times H_∞ does not enforce strict requirements on sensitivity, in particular the desired requirement that T has unity gain at DC frequency. This results in typically negligible steady-state tracking error, as the H_∞ optimization produces T(0)≈1. In drive cycle applications where reference velocity profiles contain extended ramp segments, this negligible deviation is integrated over time into a growing, non-negligible bias. The conventional remedy is to augment the plant with an integrator prior to synthesis, but this increases the order of the plant model and can be inconvenient when the control designer’s modeling has already been completed. This paper presents a post-synthesis gain adjustment method using Youla parameterization that corrects the DC tracking deficiency without modifying the plant or repeating H_∞ synthesis. The poles and zeros corresponding to the H_∞ controller’s Youla transfer function Y are preserved, with a free parameter K replacing the gain of Y. Re-calculating the controller after solving for the value of K that enforces T(0)=1 results in a hybrid controller that retains the robustness of the original but with improved performance in ramp-input scenarios with minimal effort for the control designer. Simulation results on a vehicle speed tracking problem confirm elimination of accumulating bias while preserving robustness margins from the original H_∞ design.

Abstract: The integrity of cold chains is critical for preserving the quality, safety, and efficacy of temperature-sensitive products, including pharmaceuticals, vaccines, and perishable goods. However, real-world cold-chain operations are subject to environmental variability, operational disturbances, and transport-related uncertainties that are often inadequately captured by deterministic models. This study presents a physics-informed stochastic framework for modeling temperature dynamics and product degradation under uncertainty. Temperature evolution is represented through a stochastic heat-transfer model incorporating random perturbations, while product degradation is quantified using Arrhenius-based kinetics that link thermal exposure to quality loss. The framework integrates stochastic simulation, degradation modeling, and machine-learning-based correction to capture nonlinear effects not explicitly represented by the governing equations. Numerical experiments based on the Euler–Maruyama method and Monte Carlo simulation are used to generate physically consistent synthetic datasets and evaluate system performance. Results indicate that stochastic variability can produce transient temperature excursions even when average operating conditions remain acceptable, leading to increased degradation and higher failure probabilities. The proposed methodology provides improved estimates of thermal risk and product quality deterioration compared with deterministic approaches. Overall, the framework offers a practical tool for uncertainty quantification, reliability assessment, and decision support in temperature-sensitive supply chains.

Abstract: In-situ detection of tracking poses of heliostats from a single image can help improve the tracking accuracies of heliostats and reduce the task loads of heliostat calibration in a large-scale concentrated solar power plant, as traditional methods normally require a period of off-tracking during the calibrating process of a heliostat. This paper presents a deep-learning-based keypoint detection framework for in-situ detection and calibration of heliostat pose. The proposed framework is built upon YOLOv8-Pose but integrates a high-resolution P2 feature branch to recover fine-grained spatial details that are otherwise lost in deep semantic layers. Further, a geometry-consistency loss is introduced to regularize the predicted quadrilateral, enforcing strict structural integrity under dynamically changing illumination. Experimental study on an actual dataset of heliostats shows that the proposed framework achieves an end-to-end inference speed of 25.14 FPS, making in-situ detection of tracking poses of heliostats possible in CSP plants. The mean end-point error (EPE) of detected keypoints is around 1.89 pixels, while the stringent mAP@0.5:0.95 metric reaches 0.9847. The proposed in-situ detection framework can be integrated with in-field heliostat control systems to further improve the working efficiency of heliostats in a large-scale CSP plant in the future.

Abstract: Existing data center cooling optimizations primarily focus on PID set-point adjustments, which often trigger energy antagonism between actuators and suffer from a lack of interpretability and safety inherent in pure data-driven black-box models. This paper proposes a Safety-aware Direct Control Optimization (SDCO) method featuring process deconstruction and built-in safety constraints. The approach integrates three core components: interpretable feature engineering based on multi-dimensional sensitivity analysis, embedded safety-aware control policy optimization, and industrial-grade efficiency and safety evaluation. Industrial empirical results based on real-world operating data demonstrate that, while strictly adhering to temperature safety boundaries, the proposed SDCO method achieves a PUE optimization rate of up to 11.733%. This performance significantly outperforms traditional PID and classic reinforcement learning algorithms, providing a closed-loop solution with both rigorous theoretical foundations and high engineering potential for the intelligent upgrading of high-energy-consuming industrial facilities.

Abstract: Multi-material structures such as carbon fiber reinforced polymer (CFRP) and epoxy resin are increasingly used in modern power equipment. However, significant differences in their thermophysical properties result in distinct defect thermal responses, which can reduce the reliability of infrared thermography inspections. To address this issue, this study investigates the thermal response mechanisms and quantitative characterization of defects in multi-material power equipment through finite element simulation and experimental validation. Three-dimensional transient heat transfer models containing air-void and heterogeneous insert defects were established using COMSOL Multiphysics for both CFRP and epoxy resin matrices. Pulsed infrared thermography experiments were subsequently conducted to verify the simulation results. The effects of material properties, defect geometry, and cover-layer thickness on thermal response characteristics were systematically analyzed. The results show that thermal diffusivity is the key factor governing defect signal evolution. CFRP exhibits rapid thermal propagation and early transient responses, whereas epoxy resin produces delayed and slowly increasing thermal signals. Greater defect depth weakens thermal contrast and delays peak response time, while larger defect diameters enhance defect detectability. In-creasing cover-layer thickness significantly attenuates defect signals and reduces imaging contrast. Experimental results are in good agreement with simulation predictions, confirming the validity of the proposed models. This work provides a quantitative understanding of defect thermal behavior in multi-material systems and offers a theoretical basis for adaptive infrared thermography inspection and condition assessment of power equipment.

Abstract: Focus stabilization systems can use various types of actuators, depending on the lens mounting method. One method involves magnetically mounting the lens and levitating it using a magnetic field generated by an external solenoid. The lens itself is rigidly connected to a permanent magnet or electromagnet, which creates a force that holds the lens suspended and compensates for the gravitational force. This actuator has guide rods to prevent the lens from moving horizontally; at least one such rod is present. This rod creates a dry friction force, which leads to nonlinearity in the mathematical description of this actuator. This "dead zone" nonlinearity hinders high stabilization accuracy, since the actuator does not provide ultra-small displacements at low electric field stimuli. The best achievable accuracy in this case is 0.5 µm. Another method of mounting the actuator involves holding the lens on two taut strings. This mounting method is characterized by a strong tendency of the actuator to oscillate. In this case, the transient process is characterized by a large number of damped oscillations before the stabilization error becomes sufficiently small. These issues are analyzed using mathematical modeling and solved using numerical optimization. Recommendations are proposed to overcome these challenges, achieving lens positioning accuracy significantly lower than 0.5 µm. The results are validated by simulation.

Abstract: Transition towers and conveyor structures in steel industries that are utilized for material transfer require regular visual inspections to ensure optimal performance. However, due to their high altitude and the challenging environmental conditions, such as strong winds and extreme dust, these inspections are both costly and labor-intensive for decision-makers. Additionally, severe dust necessitates frequent maintenance of any wireless cameras installed for continuous monitoring, as they require regular cleaning and cannot cover all sections of the area. Tilt-rotor quadcopters have emerged as a promising solution for inspecting these industrial structures at hard-to-reach locations within large-scale environments. Nevertheless, wind disturbances, particularly at high altitudes, pose a significant challenge. Therefore, implementing a robust control system capable of maintaining stable performance under such conditions is essential. In this paper, we introduce a tilt-rotor quadcopter control system based on a gain scheduling controller (GSC). Our choice of this control method is motivated by its potential to effectively manage wind disturbances during structural inspections and capture images of the specified structures. We provide a mathematical formulation of the GSC method and present performance simulations using MATLAB and a machine learning approach. Consequently, we evaluated and monitored the control method's performance, observing satisfactory chattering levels relative to those reported in the literature for the specified environment.

Abstract: The concept of an extended PID controller is introduced for the first time. This controller combines the properties of two well-known variants of the classical PID controller. The extended PID controller includes two additional parameters in addition to the three parameters of the classical PID controller. Its properties are examined using the yaw channel of a rocket angular stabilization system equipped with an extended PID controller. Linearized equations of motion for the yaw channel of the rocket angular stabilization system with the extended PID controller are formulated. The transfer function of the rocket angular stabilization system and its characteristic polynomial are obtained. Stability and performance indices of the rocket angular stabilization system are introduced. These indices are determined from the coefficients of the characteristic polynomial and are expressed directly in terms of the parameters of the extended PID controller of the stabilization system. Based on sufficient conditions for stability and performance of the stabilization system, systems of algebraic inequalities are derived with respect to the required values of the control-law parameters that satisfy the stability and performance requirements of the stabilization system. It is shown that the set of their solutions is nonempty. It is demonstrated that the introduction of additional extension parameters into the classical PID controller makes it possible to vary the zeros of the transfer function of the stabilization system. This enables the stability and performance requirements of the rocket angular stabilization system to be satisfied independently of one another. At the same time, by varying the additional parameters, the zeros of the transfer function of the stabilization system can be made equal to its poles. This changes the structure of the transfer function by reducing its order by two. Numerical experimental studies of the dynamics of the rocket angular stabilization system are carried out using the technical characteristics of the developed test bench for the rocket angular stabilization system. The results confirm the high effectiveness of the extended PID controller: 1) the transient response retains an aperiodic character, which follows directly from the form of the transfer function of the extended PID controller; 2) the settling time is reduced by a factor of 7.53 compared with the classical PID controller.