Search Results (19,579)

Under the vehicle as a service (VaaS) paradigm, intelligent connected vehicles continuously generate large-scale, computation-intensive perception data processing tasks. However, limited onboard computing resources and power supply prevent vehicles from handling these tasks efficiently. Vehicular edge computing (VEC) extends available computing resources through vehicle–infrastructure collaboration. Nevertheless, continuous vehicle mobility causes intermittent communication links between vehicles and roadside units, posing new challenges for VEC task scheduling. Therefore, this paper proposes a reinforcement learning-based VEC task scheduling approach that integrates a fuzzy reward mechanism with multi-agent proximal policy optimization (FRMPPO). First, a system architecture is developed by integrating the directed acyclic graph task model, dynamic communication model, and computation model. The scheduling problem is formulated as a partially observable Markov decision process, with the objective of minimizing task completion latency and vehicle energy consumption. Second, a fuzzy reward mechanism is designed to guide the training of multi-agent proximal policy optimization. It takes edge node load pressure and communication state as inputs to adaptively combine local immediate rewards and the global reward, eventually guiding the agents toward a globally optimized cooperative policy. Finally, real-time scheduling decisions under communication intermittency are enabled through a centralized training and decentralized execution framework and gated recurrent unit-based state encoding. Simulation results demonstrate that FRMPPO effectively solves the VEC task scheduling problem, achieving significantly superior performance over existing algorithms in terms of both task completion latency and vehicle energy consumption. The proposed method thereby satisfies the real-time processing demands of perception tasks in VaaS scenarios. Full article

The accelerated deployment of electric vehicles requires planning tools able to quantify how much charging infrastructure can be integrated into distribution systems without violating operational constraints. This paper proposes a multi-scenario optimization framework for the siting and sizing of electric vehicle charging stations in radial distribution networks. The problem is formulated as a mixed-integer nonlinear programming model in which candidate-station slots, binary siting decisions, integer EV assignments, hourly power-flow constraints, voltage limits, thermal limits, charger power factor, and charging strategy are coordinated. The objective function combines hosting capacity maximization with active energy losses and voltage deviation terms through a scalarized formulation. Unmanaged and managed charging strategies are evaluated under weekday and weekend operating scenarios. Four adaptive population-based optimizers are analyzed under identical computational conditions: particle swarm optimization, a population-based genetic algorithm, JAYA, and the multi-verse optimizer. Monte Carlo random sampling is included separately as a non-adaptive baseline without memory or learning. The methodology is tested on a modified 33-bus distribution system using Colombian demand profiles and line-current limits. The campaign includes 720 cases and 7200 independent runs. In the 720-case stochastic campaign, the largest feasible solution serves 765 EVs, equivalent to 5.508 MW, with a minimum voltage of 0.9084 p.u. and a maximum loading of 99.83%. Statistical validation shows no significant Holm-adjusted pairwise differences among the adaptive algorithms in hosting capacity, while PSO provides the most robust feasibility behavior. Supplementary robustness analyses quantify the influence of candidate-site definition, objective scaling, voltage limits, base charging-power scale, and native-load growth. A complementary deterministic 69-bus assessment under a normalized branch-current envelope preserves the qualitative managed-versus-unmanaged trend, with feasible sequential allocations of 779 and 225 equivalent EV charging units, respectively. The proposed framework provides a reproducible basis for identifying robust EVCS locations, estimating hosting capacity, and quantifying tradeoffs among charging capacity, network losses, voltage performance, and computational effort. Full article

Traditional electric-drive maize seed-metering systems, when operating at high speeds, are constrained by insufficient control accuracy and sluggish dynamic response, making it difficult to guarantee qualified single-seed placement rates, which directly affects field sowing quality and subsequent maize yield. To address the inherent shortcomings of conventional fuzzy PID control—namely, lagging manual parameter tuning and the susceptibility of standard particle swarm optimization to local optima—this paper proposes an improved particle swarm optimization algorithm. By introducing adaptive inertia weight and chaotic mutation operators to optimize the iterative mechanism of the particle swarm optimization, precise tuning of fuzzy PID control parameters is achieved. Simulink simulations show that the improved algorithm reduces the optimal fitness value by 60.66% compared with the standard algorithm, and increases the convergence speed by 30%. Bench test results confirm that, under operating speeds of 10–14 km/h, the proposed control strategy exhibits superior dynamic and steady-state performance; at the high operating speed of 14 km/h, the seeding quality index and coefficient of variation in plant spacing are 91.2% and 10.8%, respectively, representing significant improvements over PSO-Fuzzy-PID (88.1%, 12.8%) and conventional Fuzzy-PID (83.2%, 16.2%). This study can provide an important theoretical basis for precise control of high-speed maize seeding. Full article

High-precision manufacturing in unstructured factories imposes stringent requirements on real-time scene perception and end-effector positioning accuracy. Traditional single-sensor solutions suffer from perception blind spots in human-robot mixed environments with complex lighting, while chassis cumulative error often leads to rigid collisions during end-effector operations. To address this, this paper proposes and evaluates a decision-level multi-sensor coordination mechanism for robust navigation and high-precision planar positioning of industrial mobile robots. The mechanism assigns explicit sensor roles, distance-dependent trigger conditions, and deterministic safety priorities. At the navigation and obstacle avoidance level, a sequential decision policy is constructed: macroscopically, a lightweight You Only Look Once version 5 small (YOLOv5s) is utilized for the early detection of dynamic objects, providing bounding-box coordinates to trigger preemptive deceleration, while LiDAR independently provides geometric ranging for ROS local-costmap updating and detour replanning; microscopically, a low-level hardware interrupt strategy triggered by ultrasonic sensors is proposed to mitigate near-field blind spots and reduce communication latency. At the end-effector positioning level, under illumination conditions ranging from 200 to 1000 lux, an adaptive alignment algorithm combining hue-saturation-value color-space morphological processing and Kalman filtering is proposed to suppress measurement noise caused by illumination variations and mechanical vibrations. Experiments in the tested dynamic human-robot mixed scenarios showed no rigid collisions for the proposed system and an emergency response time of approximately 50 ms against sudden blind-spot intrusions. Simultaneously, the system achieves a 95% reliability rate in controlling the end-effector 2D planar positioning error (X-Y plane) within a ±2 mm tolerance under complex illumination interference. These results demonstrate improved navigation safety and planar-positioning reliability under the tested flexible-manufacturing conditions. Full article

With the rapid development of the electric vehicle (EV) industry, large-scale integration of EVs into the power grid has led to increasingly prominent problems such as low charging efficiency, intensified load fluctuations, and reduced economic benefits for users. To address these issues, an optimization model is constructed with charging time, load fluctuation, and user charging cost as the objectives, comprehensively considering uncertainties including renewable energy output, user charging behavior, and electricity price fluctuations. An uncertainty-aware multi-objective scheduling strategy based on an improved chaotic Lévy flight multi-objective particle swarm optimization (CLM-MOPSO) algorithm is proposed. Specifically, Weibull and Beta distributions are adopted to generate scenarios for wind and photovoltaic power output, while Poisson and normal distributions are used to characterize the uncertainty of user charging behavior. In addition, a stochastic electricity price process and load uncertainty sets are introduced to establish a robust optimization framework based on multi-scenario stochastic programming. On this basis, an improved CLM-MOPSO algorithm is designed, in which Tent chaotic mapping is utilized for high-quality population initialization, Lévy flight mutation is introduced to enhance the global search capability, and adaptive parameter adjustment together with an external archive mechanism is incorporated to improve the search efficiency while maintaining good convergence and diversity of the Pareto solution set. Finally, simulation studies based on real road network and power grid operation data are conducted, and the results verify the effectiveness of the proposed method. The results demonstrate that the proposed method significantly reduces charging time, mitigates load fluctuations, and lowers user charging costs, while also exhibiting strong robustness and potential for practical engineering applications. Full article

The increasing adoption of Retrieval-Augmented Generation (RAG) in the oil and gas industry has created a growing need for systematic evaluation of domain-specific knowledge bases, particularly with respect to retrieval failures, numerical and entity inconsistencies, knowledge timeliness, and unsupported generation. This study presents a domain-adapted evaluation and optimization framework for industrial RAG knowledge bases based on an LLM-as-a-Judge paradigm. The framework organizes evaluation into four dimensions—Data, Retrieval, Generation, and Utility (DAAE)—and combines deterministic metrics with LLM-based semantic assessment. A two-tier evaluation procedure combines retrieval-based screening with fine-grained LLM judging while retaining retrieval failures in end-to-end evaluation statistics. Rather than introducing new retrieval or generation algorithms, the framework integrates established RAG techniques with evaluation criteria motivated by oil-and-gas knowledge characteristics, including domain-entity and numerical consistency, temporal validity, chunk-level semantic integrity, and controlled abstention. Evaluation results are mapped to corresponding optimization actions across the data, retrieval, and generation layers, including semantic-aware chunking, metadata augmentation, hybrid sparse–dense retrieval, Cross-Encoder reranking, and structured evidence-grounded prompting. The framework was evaluated using the Intelligent Knowledge Base for Natural Gas Economic Research and an expert-annotated benchmark comprising 150 domain questions. In the industrial before–after comparison, Retrieval Hit@5 increased from 58.0% (87/150) to 89.3% (134/150), Context Precision increased from 0.64 to 0.88, and Faithfulness increased from 0.65 to 0.94. These values are reported as system-level point estimates rather than as component-wise causal effects. The results demonstrate the practical value of evaluation-guided optimization for improving the reliability of domain-specific RAG systems in natural-gas economic research and provide an industrial case for systematic RAG assessment and iterative optimization in the energy sector. Full article

Underwater point cloud registration using Multibeam Echosounder (MBES) data is fundamental to marine exploration and seafloor mapping. However, MBES point clouds present unique challenges compared to terrestrial Light Detection and Ranging (LiDAR): high noise levels, low overlap rates, and strongly anisotropic distributions caused by the strip-like sonar scanning pattern. These characteristics degrade existing registration algorithms, which predominantly assume locally isotropic point distributions. To address these challenges, this paper proposes Anisotropic Kernel Point Convolution (A-KPConv), a novel operator tailored to the strip-like structure of MBES point clouds. A-KPConv uses Principal Component Analysis (PCA) to estimate local geometric principal directions and constructs an affine transformation that adapts the convolution kernel shape and orientation to align with the local geometry, thereby shifting feature extraction from isotropic aggregation to structure-aware feature learning along the principal structural directions. Building upon this operator, we integrate A-KPConv into Predator—a framework for low-overlap registration—to develop A-Predator, in which the standard isotropic KPConv in the first three encoder layers is replaced with A-KPConv so that structure-aware feature learning is performed where geometric information is most salient. Extensive experiments on the public Dotson-east dataset and a self-collected LiQuan Lake (LQL) MBES dataset demonstrate that A-Predator achieves the highest registration recall among the evaluated methods. On Dotson-east, recall improves from 31.63% to 59.55% under 10% overlap, with consistently low translation and rotation errors. Ablation studies support the effectiveness of A-KPConv relative to the evaluated anisotropic operators, and cross-dataset experiments suggest more effective transfer than the evaluated baselines from Dotson-east to the rescaled LQL-MBES data without fine-tuning. Full article

Passive Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) systems deployed in metallic commercial aircraft cabins suffer from severe multipath fading, non-stationary channel dynamics, and operator gait-induced signal jitter. Addressing these challenges without physical airframe modifications or regulatory recertification remains a critical operational bottleneck. This paper presents an edge-native, software-defined framework that integrates micro-electromechanical system (MEMS) inertial measurements with an IMU-assisted Adaptive Kalman Filter (AKF) and a distilled surrogate decision tree. The proposed algorithm extracts localized motion energy ($E_{\mathrm{IMU}}$) to dynamically scale the measurement noise covariance ($R_k$) prior to physical-layer signal corruption, thereby eliminating phase lag and power hunting. For deterministic edge execution on COTS handheld devices, surrogate model distillation compresses a parent Random Forest ensemble into an $8.2\mathrm{KB}$ 13-leaf decision tree (depth 5) yielding $0.12\mathrm{ms}$ inference latency. Empirical validation across 17 operational sessions in Airbus A320, Boeing 737, and Airbus A321 cabins (10,720 valid reads) demonstrates a $99.45\%$ mean RSSI jitter reduction ($95\%\mathrm{CI}:[99.21\%,99.63\%]$) and a $7.30\times$ suppression of transmit power oscillations. Statistically, asset detection completeness is fully preserved ($0.791$ vs. $0.795$ baseline, $z=0.281,p=0.779$). Operating entirely within standard handheld software runtimes, this approach bypasses Supplemental Type Certificate (STC) requirements while ensuring robust aerospace asset visibility. Full article

The development of accurate, robust and adaptive methods for monitoring the optical parameters of fiber-optic sensors (FOS) is one of the priority tasks in the field of precision measurements, especially in the context of rapidly growing requirements for intelligent monitoring systems. This paper presents a comprehensive approach to the use of modern deep learning algorithms for analyzing and processing spectral data coming from FOS. The proposed solution is based on the use of convolutional neural networks (CNN) for the automatic extraction of informative features, as well as autoencoders for noise suppression and signal restoration. A hybrid architecture combining CNN and recurrent neural networks (RNN) was developed. The experiments conducted confirmed the effectiveness of the evaluated models. On the independent regression test set, the CNN-only model achieved a macro-averaged R²-based prediction score of 98.2% without added noise and 89.4% under high-noise conditions; on a separate temporal test sequence, the hybrid CNN + RNN model achieved 95.0% compared with 88.0% for CNN alone. The presented approach has high resistance to noise and the ability to scale to various types of FOS. At the conclusion, the prospects for the practical applications of the proposed system are discussed: the structural monitoring of buildings and structures and the automation of processes in industry and energy, with an emphasis on reliability, autonomy and integration with existing platforms. Full article

Particle swarm optimization (PSO) is a classical metaheuristic algorithm that has been widely used to solve continuous optimization problems. However, it still suffers from inherent drawbacks, such as being prone to premature convergence and an imbalance between exploration and exploitation. To address these problems, a Multi-swarm Collaborative Particle Swarm Optimization with Adaptive Learning Strategy (MCPSO-ALS) is proposed in this paper. Firstly, an Adaptive Population Division Strategy (APDS) is designed to realize effective information interaction among particles. At each iteration, the overall population is dynamically divided and reorganized, based on the fitness values of all the particles. Specifically, particles with high fitness form an elite population focusing on local search; particles with low fitness form a poor population focusing on global search; and ordinary particles form a general population aiming to achieve a balance between exploration and exploitation. Secondly, an Adaptive Learning Mechanism (ALM) is introduced. In this mechanism, particles with different roles adopt distinct updating strategies, which productively guarantees the dynamic equilibrium between exploration and exploitation. To validate the outstanding performance of the proposed algorithm, comprehensive experiments are conducted on CEC2013 and CEC2017. The experimental results demonstrate that the proposed method exhibits significant advantages in convergence speed, solving accuracy, and comprehensive optimization capabilities compared with several state-of-the-art algorithms. Full article

Variable-rate pesticide spraying and fertilizer application require coordinated operation of heterogeneous agricultural machines, particularly in irregular fields where task demands, vehicle mobility, payload capacity, energy consumption, and resupply requirements vary spatially. However, most existing studies optimize aerial spraying or ground fertilization separately and do not jointly consider prescription-map demands, air–ground synchronization, pesticide-drift risk, and agricultural vehicle constraints. This study formulates collaborative UAV spraying and UGV fertilization as a multi-objective mixed-integer nonlinear programming problem with three objectives: minimizing system makespan, weighted energy consumption, and pesticide-drift penalty. A prescription-map-guided bi-level planning framework is proposed. At the upper level, the problem-specific TNSAOO solver determines UAV and UGV task sequences and collaborative resupply-point activation. At the lower level, adaptive Theta* and row-constrained Hybrid A* generate UAV spraying and UGV fertilization trajectories, respectively, while prescription-dependent application commands are assigned along active operation segments and a time-window mechanism detects and corrects residual air–ground conflicts. The framework was evaluated using 30 real farmland boundaries and 90 randomized prescription scenarios. Mean geometric coverage rates reached 98.82% for UAV spraying and 98.95% for UGV fertilization, while the mean prescription-compliance errors were 6.21% and 2.13%, respectively. In addition, 96.7% of the batch runs contained no more than one detected air–ground conflict, with a mean corrective waiting time of 1.07 s. Compared with traditional independent operation, collaborative planning reduced mean system makespan by 9.32%, weighted energy consumption by 7.21%, modeled drift penalty by 3.62%, and total path length by 5.79%. In the multi-objective comparison, TNSAOO obtained a mean hypervolume of 0.597 and a mean inverted generational distance of 0.375, showing competitive Pareto-search performance relative to established comparison algorithms, particularly NSGA-II. Additional terrain and drift sensitivity analyses produced systematic changes in energy, completion time, and modeled drift risk under controlled parameter perturbations. These findings demonstrate the simulation-based feasibility of jointly planning heterogeneous variable-rate spraying and fertilization under a shared prescription map. Physical field experiments remain necessary to validate spray deposition, fertilizer-distribution uniformity, terrain effects, and model calibration under environmental uncertainty. Full article

Background: Directionality algorithms are fundamental to modern hearing aids, enhancing speech perception in noisy environments by improving the signal-to-noise ratio. Conventional adaptive approaches rely on heuristic optimization criteria and often improve speech understanding from specific directions, typically the front, at the expense of spatial awareness. A deep neural network (DNN)-based directionality system was developed that dynamically optimizes spatial filtering through a data-driven framework capable of representing complex acoustic scenes. The system intelligently enhances target speech while preserving awareness of environmental sounds. Methods: The proposed DNN-based directionality system was evaluated against conventional directionality algorithms by assessing word recognition performance, perceived speech clarity, and listener preference across diverse acoustic scenarios. To complement subjective measures, an artificial intelligence (AI)-based objective intelligibility metric was additionally developed using automated speech-to-text analysis, enabling scalable and consistent benchmarking across devices. Results: Results across the behavioral and objective assessments indicate improved speech intelligibility outcomes together with maintained access to environmental sounds. Mean SRT50 improved by 1.4 dB relative to legacy directionality for target speech presented at 90° and by 4.7 dB relative to an omnidirectional pattern when frontal target speech was accompanied by a rear interfering talker. Spatial adaptation further improved environmental sound audibility in three out of four conditions and significantly improved the detection threshold for speech presented at 135° by 2.42 dB. Conclusions: These results show that the system can preserve speech originating away from the front while using acoustic context and spatial filtering to attenuate competing speech that interferes with a frontal conversation. Full article

Metaheuristic algorithms are widely used to solve complex optimization problems, but the trade-off between exploration and exploitation often limits their performance. The Crested Porcupine Optimizer (CPO) employs four bio-inspired defense mechanisms and achieves competitive performance, but it still tends to converge prematurely, initialize populations poorly, and rely on static parameters that cannot adapt to different phases. This paper proposes a Multi-Strategy Enhanced Crested Porcupine Optimizer (MSCPO) with four phase-targeted enhancement strategies: (1) Kent Chaos Opposition-Based Learning Initialization (KCOL) improves the initial population distribution through chaotic-weighted reflection; (2) Arctic Puffin Optimization (APO)-Inspired Dual-Modal Evasion (APO-DME) reduces dependence on the global best solution and increases population diversity through dual-mode differential perturbation; (3) Adaptive Dual-Differential Perturbation (ADDP) refines the search by combining population diversity and elite guidance information; (4) Periodic Dynamic Adaptive Perturbation (PDAP) enhances exploitation through periodic trigonometric perturbation and a fitness-conditioned update rule. These strategies interact across the optimization process to strengthen each defense mechanism at the appropriate phase. On the CEC 2017 and CEC 2022 benchmark suites, MSCPO achieves the best overall mean rank among all compared algorithms, with an overall rank of 2.638 on CEC 2017 and 2.375 on CEC 2022. A full-factorial ablation over all 16 strategy combinations confirms that each strategy contributes positively: removing any single strategy degrades the overall mean rank, and the complete MSCPO achieves the best mean rank (3.34), significantly outperforming all single-strategy variants (Wilcoxon signed-rank test, p < 0.001). To verify the practical applicability of MSCPO, the algorithm is further applied to three engineering design problems: step-cone pulley design, hydrostatic thrust bearing design, and robotic gripper design. MSCPO ranks first on the hydrostatic thrust bearing problem, second on the step-cone pulley problem, and third on the robotic gripper problem. Future work will explore adaptive population sizing to further improve the scalability of MSCPO on very high-dimensional problems. Full article

Conventional quasi-steady-state models treat a self-adaptive STATCOM and line-commutated converter (SLCC) station as an LCC with an external reactive-power source, which cannot fully represent valve-side coupling. This paper develops a three-phase stationary-frame differential model for the SLCC and derives quasi-steady-state expressions for the average DC voltage and fundamental displacement angle. The non-commutation equivalent voltage is decomposed into fundamental and nonfundamental components. The fundamental component is retained in the power-flow model, while a control-informed harmonic extension evaluates the corresponding average DC-voltage correction over the tested operating domain. A positive-sequence fundamental-frequency formulation calculates the commutation overlap angle, and a first-zero diagnostic identifies control-sensitive conditions associated with the fast SVG voltage response. When the fast commutation-direction voltage reaches zero or reverses before current transfer is completed, a control-equivalent effective-area formulation provides an alternative low-order representation. The station equations are incorporated into a sequential AC/DC power-flow algorithm and validated against the engineering PSCAD/EMTDC main-circuit and control model of the Yangzhou–Zhenjiang HVDC Phase II project. Across the stable tested operating points, the phase-aware EMT-derived harmonic DC-voltage correction ranges from 0.585% to 1.245%, remaining below the adopted 2% screening threshold. The control-informed estimate follows the EMT-derived correction, whereas the phase-independent conservative bound reaches 2.128% at high controller gain. Across eight cases with available PSCAD reference values, the control-equivalent formulation reduces the mean and maximum overlap-angle errors from $0.{90}^{\circ }$ and $1.{37}^{\circ }$ to $0.{78}^{\circ }$ and $1.{09}^{\circ }$. For the benchmark power-flow cases, the maximum relative errors are 1.3% for the SLCC bridge reactive power and 1.0% for the SVG reactive-power output, and the calculation converges without sustained oscillation. A representative operating-point calculation is completed in approximately 3 s with the quasi-steady-state (QSS) formulation, compared with about 15 min for the engineering EMT benchmark. Full article

This study examines how autonomous digital technologies are associated with Sustainable Business Performance in circular-economy settings. Drawing on the Resource-Based View and dynamic capabilities theory, it develops a framework linking AI Agent Autonomy, Digital Twin Capability, Self-Optimization Capability, Circular Process Integration, Algorithmic Trust, and Sustainable Business Performance. Cross-sectional survey data were collected from 319 purposively selected managers and decision-makers in Jordanian organisations between January and April 2026 and analysed using Partial Least Squares Structural Equation Modelling in SmartPLS 4. The findings show that AI Agent Autonomy and Digital Twin Capability are positively associated with Self-Optimization Capability, which is subsequently associated with Circular Process Integration. Circular Process Integration and Algorithmic Trust are positively associated with Sustainable Business Performance. However, AI Agent Autonomy had no significant direct relationship with Sustainable Business Performance, while the direct relationship of Digital Twin Capability was weak. The simple and serial mediation results indicate that Self-Optimization Capability, Circular Process Integration, and Algorithmic Trust represent important mechanisms connecting autonomous technologies with sustainable performance. The study contributes by integrating technological, organisational, operational, and behavioural mechanisms within one circular-economy framework. The findings suggest that the organisational value of AI agents and digital twins depends on adaptive capability, circular-process implementation, and stakeholder trust rather than technology adoption alone. Full article