Engineering

Abstract: To address the drawbacks of horizontal- and vertical-axis wind turbines, a new concept has recently emerged. A novel active-axis wind turbine (AAWT) aims to reduce mechanical loads and structural costs when compared to conventional horizontal-axis wind turbines. The AAWT uses a pitch-tilt angle mechanism to balance aerodynamic lift and centrifugal forces acting on the rotor, thereby providing stable rotor rotation. Previous research on a small laboratory prototype AAWT established the relationship between pitch and tilt angles for quasi-static equilibrium. However, the transient dynamic behaviour about the tilt axis during movement between these states has not yet been investigated. This work develops a transient dynamic model of the AAWT pitch-tilt mechanism and controls the transition between the equilibrium states of aerodynamic lift and centrifugal moments. The cross-arm tilt axis is introduced as the first degree of freedom of the rotation system, triggered by a change in balance between the aerodynamic lift moment and the centrifugal moment about the tilt axis. A controller that combines feedforward and PD feedback is used to control the tilt transition and compensate for the lack of a mechanical damper on the tilt arm axis. To achieve a stable response across different rotor speeds, gain scheduling is applied to the controller’s derivative term. The results show that the PD controller can suppress oscillations in the transient response and provide smooth convergence to the new target equilibrium point. At different tested rotor rotation speeds, the settling time decreases with increasing rotation speed. The research provides a basis for future control-strategy experiments to achieve dynamic changes in tilt angle in the AAWT.

Abstract: As the penetration of distributed generation (DG) in distribution networks continues to grow, fault currents shift from a conventional unidirectional flow to bidirectional flow, and the accuracy and adaptability of existing fault location methods are severely challenged when feeder terminal units (FTUs) are subject to false alarms and missed detections. To cope with high DG penetration and FTU information uncertainty, this paper proposes a topology-constrained adaptive binary Archimedes optimization algorithm (TCAB-AOA). Fault location is modeled as a discrete combinatorial optimization problem: an expected fault current function accounting for current direction and information uncertainty is constructed; a hybrid hard-intersection–soft-voting strategy exploits network topology and FTU measurements to compress the search space; differential evolution (DE) and Lévy flight are fused as dual perturbation mechanisms to improve convergence stability; and a greedy cleanup step eliminates residual spurious fault sections. Simulations on the IEEE 33-node system under seven DG-integrated fault scenarios show that TCAB-AOA attains an average location accuracy of 90.0% (47.1 percentage points above the standard Archimedes optimization algorithm) and reduces the average number of misjudged sections to 0.20; in the multi-fault false-alarm scenario, it ranks ahead of particle swarm optimization (PSO), genetic algorithm (GA), and DE.

Abstract: Robust admissibility shows that the closed-loop poles remain within the prescribed region, but it does not determine whether small variations caused by tolerances and changes in operating conditions can lead to abrupt pole movement and deterioration of the dynamic behavior. This paper proposes a sensitivity-mapping method that complements root-contour analysis with modulus, phase, and total pole sensitivity. The local pole displacement is decomposed into radial and tangential components, while the largest singular value of a normalized pole-sensitivity matrix evaluates the worst-case combined influence of the uncertain parameters. The indicators are mapped onto the corresponding pole locations and are complemented by sensitivity measures and envelopes of the transient responses. The method is applied to an uncertain second-order control plant with a PI controller and seven tunings numerically confirmed as robustly admissible. The results reveal differences among the robustly admissible tunings, both in the local pole motion and in the time domain. The approach supports the selection among robustly admissible tunings by localizing fragility, determining the nature of the pole motion, and assessing its manifestation in the time-domain behavior.

Abstract: Residential microgrids are expected to enhance both environmental sustainability and disaster resilience in future housing developments. This study investigates two distributed control schemes for residential DC microgrids: an autonomous distributed cooperative control based on the state of charge (SoC) of battery-driven DC/DC converters, and an autonomous decentralized control based solely on feeder voltage, enabled by directly connected distributed batteries. A detailed DC microgrid model consisting of five houses was constructed in MATLAB/Simulink, incorporating PV panels with different orientations, small wind turbines, household load profiles, EV charging, and realistic meteorological data. The cooperative control scheme effectively stabilized feeder voltage, suppressed surplus renewable generation, and reduced reverse power flow to the utility grid. The decentralized scheme also maintained stable feeder voltage without device-to-device communication, although it resulted in greater reverse power flow due to less effective generation curtailment. In both schemes, houses near the grid-interconnection point experienced more frequent battery cycling, indicating the need for larger battery capacities at those locations. Simulations showed that installing approximately 20 kWh of battery capacity per house enables more than 80% of annual consumption can be obtained from renewable energy and allows continued operation during off-grid conditions with reduced consumption. These results demonstrate the practical feasibility of distributed-control residential microgrids.

Abstract: Ultrasonic welding of Carbon Fibre Reinforced Polymers (CFRPs) is a joining process that promises a high velocity and advanced welds. Furthermore, the process is suitable for large-scale automation projects that can accommodate the corresponding high-rate capability requirements. Continuous ultrasonic welding, in particular, is subject to specific boundary conditions that are constantly changing, owing to the progressing weld seam. For this reason, it is particularly important for applications in the aerospace sector that the continuous ultrasonic welding process is actively controlled, especially when a thorough and robust weld seam quality is required. This short communication introduces a novel control method where material feedback (temperature) is incorporated as a reference variable by using an infrared thermography camera as a feedback sensor in a closed loop controller.

Abstract: During the deadly hantavirus outbreak of May 2026, the loudest online response called it a hoax, while measles, the one rising threat, drew little attention. That inversion is the half of the infodemic we rarely measure: not amplified fear, but the quiet downplaying of a real risk. Detecting it requires what the field lacks: an external benchmark for the true risk. Anchoring one in the epidemiological and mass-gathering literature, with the 2026 FIFA World Cup run-up as a dated window, two coders judged Reddit claims about measles (high), hantavirus (low), and Ebola (near zero) for accuracy and direction (Cohen's kappa 0.72 and 0.68). Of 66 coded claims, 41 were inaccurate, and distortion ran both ways: 24 downplayed risk and 17 inflated it (exact binomial p = 0.35), with downplaying at least as common as inflating. Attention ran opposite to danger, hantavirus drawing the most claims (44 of 66) and measles the fewest (8): the infodemic did not mirror the epidemic. The curated, single-platform sample makes these patterns exploratory, but they suggest that defenses built against panic alone miss its mirror image, the denial of a real threat that prior work links to weaker protection among the most exposed.

Abstract: Distributed manufacturing requires communication that remains observable and recoverable as device populations, publish rates, and site boundaries grow. This paper presents a Message Queuing Telemetry Transport (MQTT) reference architecture and evaluates its communication core in a controlled, reproducible single-host experiment. The architecture combines a governed multi-factory topic namespace, message-class-specific Quality of Service (QoS), bounded queues, idempotent command handling, and randomized exponential reconnect control. Eclipse Mosquitto 2.1.2 was exercised with MQTT 5 clients on an Apple M3 Pro host. Forty-two steady-state trials covered 10–1000 concurrent publishers, aggregate loads of 25–2500 messages/s, and QoS 0–2 using 512-byte JSON payloads; six additional trials imposed a nominal 5 s broker outage on 250 publishers. Across 406,350 steady-state and 39,000 outage-test messages, measured delivery was 100%, with no duplicates or publisher errors. At 1000 publishers and 1000 messages/s, trial-level mean latency was 0.63 ± 0.61 ms and P95 latency was 1.43 ± 1.22 ms (mean ± 95% confidence interval), while broker CPU averaged 2.79 ± 0.64%. At 2500 messages/s, mean broker CPU was 4.81 ± 0.60%. QoS 2 increased mean broker CPU from 1.72 ± 0.10% at QoS 0 to 3.39 ± 0.20% under the matched 500-message/s workload. During recovery, jittered exponential backoff reduced peak connection attempts from 237.3 ± 54.5 to 57.0 ± 6.6 per 100 ms, although P95 reconnection increased from 0.13 ± 0.12 s to 0.93 ± 0.04 s. These results validate functional scaling and reconnect shaping within the tested loopback environment, not hard real-time or factory-wide performance.

Abstract: Trajectory smoothing is a fundamental requirement in robotic systems, as abrupt velocity variations and discontinuities may generate vibrations, increase mechanical stress, and reduce motion accuracy. This paper presents a data-driven methodology for smooth trajectory generation based on human demonstrations, data augmentation, and deep learning techniques. Human-executed trajectories were acquired from video recordings under two motion conditions: paused trajectories containing deliberate stops at direction changes and continuously executed smooth trajectories. The extracted coordinate sequences were normalized and expanded through geometric data augmentation, including reflections, scaling, and translations, to improve training diversity and generalization. Two learning architectures were investigated: a feedforward artificial neural network (ANN) with two hidden layers and a Long Short-Term Memory (LSTM) network. The ANN successfully learned the nonlinear mapping between paused and smooth trajectories, producing accurate results for square-shaped trajectories and their augmented variants. However, additional experiments revealed limitations when trajectories contained multiple consecutive direction changes, indicating that trajectory smoothing cannot be interpreted solely as a geometric transformation problem. To address this limitation, an LSTM-based approach was implemented to explicitly model temporal dependencies within the trajectory sequence. Experimental results demonstrate that LSTM architecture generates smoother and more dynamically consistent trajectories while preserving the geometric characteristics of the original path. The proposed method effectively eliminates zero-velocity segments, improves motion continuity, and reduces trajectory discontinuities without requiring analytical trajectory models. These findings suggest that trajectory smoothing should be regarded as a spatiotemporal learning problem and highlight the potential of recurrent neural networks for generating smooth robotic motions from demonstration data in complex and unstructured environments.

Abstract: Corrective congestion management in a transmission network with embedded HVDC requires coordinating continuous set-points and discrete topological actions under the full nonlinear AC power flow constraints, a mixed-integer nonlinear problem for which no polynomial-time solution is known. This paper formulates the task as a Markov decision process and solves it with a hybrid Soft Actor-Critic agent that handles both action types under one maximum-entropy objective: a squashed-Gaussian head for redispatch and the VSC-HVDC set-point, and two categorical heads trained through the Gumbel-Softmax Straight-Through estimator for generator and line disconnection. Each action subspace carries its own entropy temperature rather than a shared coefficient, and the behaviour of the three is measured. The state is 906-dimensional and covers branch loadings and reactive flows, bus voltages and angles, and the HVDC set-point. On the IEEE 118-bus system extended with a VSC-HVDClink,theagentclears98.6%ofa500-scenariointact-gridbatch and 97.4% of a 500-scenario batch that includes up to three simultaneous branch outages, in a mean of 1.5 and 1.7 corrective steps respectively, with an independent AC verification power flow confirming the resulting states. Inference requires a single forward pass, which makes the policy usable inside an operational decision cycle.

Abstract: The increasing density and mobility of IoT-enabled Vehicular Ad Hoc Networks (VANETs) create major challenges for energy-efficient wireless communication. Conventional orthogonal access mechanisms may lead to inefficient spectrum use, while centralized learning-based control may introduce excessive communication overhead and privacy concerns. This paper proposes a mobility-aware cross-layer framework for energy-efficient NOMA user pairing in IoT-enabled VANETs. The framework combines NOMA at the communication layer, DBSCAN-based clustering at the mobility-organization layer, and Federated Learning (FL) at the control layer. DBSCAN groups vehicles according to local mobility and spatial density, while FL enables distributed learning of energy-aware pairing decisions without centralizing raw vehicular data. The proposed pairing policy jointly considers channel-gain disparity, mobility stability, and battery awareness to select suitable intra-cluster NOMA pairs. The framework is implemented through an ns-3/Python co-simulation environment, where ns-3 models vehicular communication and mobility, while Python performs clustering and FL-based pairing decisions using Flower. Simulation results show that the proposed approach can substantially reduce transmission energy and improve energy efficiency compared with OMA and heuristic NOMA baselines under the adopted simulation assumptions. Limitations related to ideal SIC, channel estimation, and simplified mobility are discussed, and future extensions toward more realistic validation and joint pairing-power optimization are identified.

Abstract: Light Detection and Ranging (LiDAR) has become a benchmark sensing modality for autonomous unmanned aerial vehicle (UAV) navigation in GPS-denied and obstacle-dense environments such as forests, urban canyons, and indoor structures. This paper presents a structured review of the LiDAR-based UAV autonomy pipeline, spanning raw point-cloud processing, three-dimensional environment representation and mapping, simultaneous localization and mapping (SLAM), multi-sensor fusion, and real-time obstacle avoidance and trajectory planning. Reactive geometric methods, volumetric and distance-field mapping frameworks, tightly coupled LiDAR-inertial and LiDAR-inertial-visual odometry systems, gradient- and sampling-based trajectory optimizers, and learning-based end-to-end policies are compared with respect to computational cost, robustness, and applicability to resource-constrained micro-UAV platforms. The review further synthesizes current technical bottlenecks, including onboard computational limits, LiDAR performance degradation under adverse atmospheric conditions, and the difficulty of tracking fast-moving dynamic obstacles, as well as emerging research directions such as solid-state LiDAR integration, kinodynamic trajectory optimization, multi-sensor fusion (including radar- and event-camera-assisted schemes), learning-based exploration and foundation-model-based control, multi-UAV collaborative mapping, and simulation-to-reality transfer. The synthesis indicates that LiDAR remains a strong perceptual backbone for UAV autonomy, but that state-of-the-art systems increasingly combine it with inertial, visual, radar, and learning-based components rather than relying on LiDAR in isolation.

Abstract: A distribution feeder is built meshed and operated radially, so at any instant it occupies one of a combinatorial family of admissible configurations. We show that this family, weighted in the natural maximum-entropy way, is a determinantal point process whose kernel is the transfer-current matrix of the network, and we read that kernel in the operator's language: the probability that a line section is energised equals its own self transfer-current factor, Foster's sum rule is the trace identity, and the covariance of two switching states is minus the square of their transfer current. Independent faults leave the feeder exactly within this family for any number of faults, whereas no restoration mechanism ignorant of the section resistances can return it there; among those that can, one is canonical, being the unique mechanism that reverses the fault, and its weight is the section's transfer-current factor in the post-fault network with everything still energised shorted. We show, and report, that these weights are a structural diagnostic and not a repair priority. The maintained feeder is solved in closed form, the reported reliability indices prove robust to the dispatch policy, the same calculus governs a fleet of distributed generators with a different kernel, and an exact transport equation prices what a reinforcement programme costs the feeder's ability to reconfigure. The central spanning-tree and sector identities are verified against exhaustive enumeration; the dynamical and sensitivity statements are checked by exact master-equation computations and finite differences.

Abstract: Modern wind turbines require effective control strategies to maximize energy capture under partial-load conditions while maintaining generator-speed and power regulation above the rated wind speed. This study proposes and applies a controlled and reproducible benchmarking framework for evaluating classical Differential Evolution (DE) variants in the tuning of Proportional–Integral–Derivative (PID) and Proportional–Integral–Derivative–Accelerative (PIDA) controllers for a nonlinear model of the National Renewable Energy Laboratory 5-MW reference wind turbine. Twenty classical DE variants were assessed using a fixed-seed initialization strategy, unified simulation procedures, consistent objective-function definitions, and region-specific optimization settings applied uniformly to all variants within each operating region. The controllers were evaluated in Region 2, where maximum power point tracking is required, and Region 3, where generator speed and electrical power must be regulated under above-rated wind conditions. Their generalization performance was subsequently evaluated in simulation using a measured wind-speed profile obtained from a Supervisory Control and Data Acquisition system. In Region 2, under the considered fixed-seed configuration, the DE-tuned controller achieving the lowest objective-function value reduced the objective function by approximately 2.93% compared with the baseline PID controller, indicating a moderate improvement. In Region 3, the PIDA controller tuned with the DE variant yielding the lowest objective-function value achieved a reduction of up to 92% relative to the baseline controller, demonstrating a substantially greater benefit under above-rated operation. However, validation using the measured wind profile indicated that some controllers with favorable tuning-stage results showed signs of overfitting and reduced generalization performance. Overall, the results indicate that the suitability of the controller structure and DE configuration depends on the wind turbine operating region and highlight the importance of controlled, reproducible optimization procedures and validation beyond the tuning scenario in wind turbine controller design.

Abstract: Efficient facility-scale tomato ripeness monitoring remains difficult in greenhouses where uneven terrain limits conventional wheeled and rail-guided platforms and planar cameras provide restricted coverage. This study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera. An adaptive gait-switching strategy supported navigation across heterogeneous terrain. Panoramic images were projected into six perspective views, and the left and right views were processed using a YOLOv8-based ripeness recognition model. Time-synchronized detections and robot poses were fused to map ripeness observations into three-dimensional greenhouse coordinates. Five field experiments in a commercial tomato facility demonstrated autonomous row traversal, inter-row transition, and avoidance of pedestrians, obstacles, and cultivation boundaries. The recognition pipeline continuously identified multiple ripeness stages under variable illumination, foliage occlusion, and robot motion, while the spatial fusion procedure produced a facility-scale three-dimensional ripeness distribution. The integration of terrain-adaptive quadruped mobility, panoramic perception, and spatial mapping provides a practical framework for continuous ripeness monitoring and can support targeted harvesting, yield forecasting, and crop management.

Abstract: We propose a unified physics informed neural modelling framework for the simultaneous emulation of hydraulic states and water quality in water distribution systems (WDS). Building on recent advances in physics informed graph neural networks for hydraulics and spatio temporal PINN-GNN hybrids for chlorine transport, our approach (PINNWDSFQ) couples a physics aware Graph Neural Network (GNN) emulator of hydraulic variables (flows, heads, pressures) with a spatio temporal graph PINN for reactive advective transport. The hydraulic GNN employs message passing based on conservation laws to recover nodal/edge flow fields with a small number of layers and low computational cost, matching high fidelity simulator outputs while enabling fast inference across network sizes. The quality module discretizes pipes with virtual nodes and imposes PDE constraints (advection-reaction) using physics informed loss terms in a GNN encoder-processor-decoder, thereby preserving network topology and chemical reaction kinetics. Both modules are jointly trained in a multitask loss that includes supervised data terms (sparse sensor readings or EPANET surrogate labels) as well as physics constraints and consistency coupling (mass/flux conservation at nodes and flow-transport coupling). We show that the hybrid model obtains emulator level accuracy for hydraulics and state-of-the-art water quality RMSE/MAE (order 1e-3-1e-2 mg/L), scales to large networks with thousands of virtual nodes and runs orders of magnitude faster than traditional simulators on multiple benchmark networks. We discuss training strategies, handling transient/pump driven dynamics, sensor placement implications for calibration, and uncertainty quantification for operational use.

Abstract: This research focuses on the development of a robotic cell test bench for manufacturing variable-geometry tubular parts with minimal human intervention. The system integrates dual arm manipulation with cutting, welding, and coating stations, and was validated in collaboration with an industrial partner with strict requirements on weld strength, surface quality, and air leak rate. Stable and repeatable operation of the robotic cell was achieved through the characterization and optimization of key welding process parameters, particularly the robot end-effector travel speed (mm/s) and laser power (W). In addition, preliminary sealing evaluations demonstrated that the welded components could withstand internal pressures. These results highlight the influence of mechanical components, geometries, and material behavior, as well as the need for advanced digital tools. A preliminary automated path generation methodology is introduced to support adaptable robot trajectories. This research aims to advance composite manufacturing using industrial robotics and dedicated tooling for complex processes, while inspiring engineers and researchers to further automate repetitive and labour-intensive tasks that remain challenging to robotize.

Abstract: The paper presents the design, development, and simulation of an intelligent control system for the pitch-decoupled vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV). In contrast to conventional tilt-rotor VTOL UAVs, the mechanical structure of the pitch-decoupled VTOL UAV allows passive transition from vertical to horizontal flight modes and vice versa without the use of additional servo actuators. A detailed kinematic scheme and the dynamic equations of motion of the pitch-decoupled VTOL UAV equipped with a two-degree-of-freedom robotic arm are developed using the Denavit-Hartenberg parameters and the Euler-Lagrange equations. The aerodynamic stability of the UAV is investigated, and the aerodynamic coefficients used in the complete dynamics model are derived. On this basis, a multivariable control system is developed. To address the well-known transition-phase issues of VTOL UAVs, a neural controller is designed using the reinforcement learning actor-critic method. It is shown that the proposed neural controller provides a smoother and more accurate transition phase and subsequent horizontal flight of the pitch-decoupled VTOL UAV compared with conventional and cascaded PID controllers.

Abstract: Resilience optimization in adversarial Multi-Agent Mission Systems (MAMSs) requires more than resilience measurement: it requires a decision mechanism whose objective is explicitly aligned with the resilience metric itself. This paper proposes a net-effectiveness-based evaluation–optimization framework for bilateral temporal adversarial operational-loop networks (BTAR), turning resilience evaluation into a reusable world model and reward source for decision-making. We model bilateral confrontation as a heterogeneous temporal network and quantify resilience through the integral of net effectiveness, which captures temporal and adversarial dynamics beyond static topological indicators. We then formulate resilience enhancement as a scale-invariant, cost-aware Markov decision process and solve it with a hierarchically decoupled reinforcement learning architecture: an upper-level PPO policy learns when and for which chains to trigger selective replanning, while a lower-level greedy routine selects concrete replacement nodes. In this way, the optimization objective remains formally aligned with the evaluation metric. Experiments on a reproducible multi-scale, multi-style scenario pool show that the learned policy improves Rtotal from 0.3673 to 1.1696, reaching 96.8% of an idealized oracle benchmark (1.2082), while slightly outperforming the always-replan rule (1.1482) with a lower trigger rate. Under nonzero replanning cost, retraining yields a cost-aware policy whose trigger rate drops to 21.2% and whose cost-adjusted resilience at the target cost setting increases by +0.15, surpassing the greedy oracle benchmark in the high-cost regime. These results show that coupling net-effectiveness evaluation with hierarchical RL yields a deployable, scale-invariant, and cost-aware resilience optimizer.

Abstract: In gravimetric liquid-flow calibration systems, the diverter valve defines the effective start and end of mass collection. For this reason, its temporal repeatability affects the mass associated with the transition and can become a relevant contribution to the measurement uncertainty, especially in low-flow modules. Although the state of the art has addressed this problem through geometric improvements, time or mass correction models, CFD analysis, and higher-precision actuators, these approaches are usually treated in isolation, without systematically linking the variable hydrodynamic load, switching repeatability, and the affected-mass contribution within a single optimization framework. This work presents the genetic-algorithm-based tuning of a PID controller for the servo-electric actuation of a diverter valve in a 200 kg gravimetric module, operating in the interval from 2 L/min to 40 L/min. The main contribution consists of optimizing the PID gains as a function of the flow rate and the hydrodynamic load, with the purpose of reducing the temporal dispersion of the mechanism and its relative contribution to the gravimetric calculation. At 40 L/min, the original pneumatic system presented a switching-time standard deviation of 0.6149 s, while with the servo-electric system and the optimized PID this value decreased to 0.0832 s, equivalent to a reduction of 86.5%. Likewise, the relative contribution of the diverter decreased from 5.10% to 0.688%. The results show that the evolutionary optimization of the PID allows improving the repeatability of the diverter and reducing the variability of the affected mass, although it does not represent by itself the complete uncertainty budget of the gravimetric module.

Abstract: Energy-autonomous Internet of Things (IoT) nodes are becoming important components of distributed wireless networks used in building automation, industrial monitoring and wider smart systems. Their operation is constrained not only by the amount of harvested and stored energy, but also by sensing activity, communication cost, computational workload and required service quality. This review analyzes these dependencies from a cross-layer perspective linking energy harvesting and power management, field-level IoT nodes, wireless communication technologies, and edge–fog–cloud computing. The main original contribution is a decision-oriented framework for energy-autonomous distributed intelligence, supporting adaptive placement of sensing, processing, inference and coordination functions across field, edge, fog and cloud resources. The analysis shows that energy autonomy cannot be achieved by optimizing individual nodes only. Wireless connectivity, network topology and communication overhead directly affect the feasibility of higher-level processing, while edge and fog resources can reduce field-node load and improve local service continuity. The proposed framework therefore combines energy feasibility, communication conditions, service requirements and coordination scope. The resulting guidelines are particularly relevant to building automation and smart IoT systems, supporting interoperable, adaptive and energy-efficient distributed wireless architectures.