Search Results (166)

The performance of the aircraft anti-skid braking system is critical to the ground operational safety of an aircraft. Conventional Pressure Bias Modulation (PBM) can suffer from deep skidding under low runway friction coefficients or low aircraft speeds. To address these issues, a composite control strategy based on Gaussian Quantum Particle Swarm Optimization (GQPSO) is proposed. This strategy employs the GQPSO algorithm for offline Proportional–Integral–Derivative (PID) parameter optimization, followed by real-time adaptive scheduling through a lookup table to accommodate varying speed domains and runway conditions. Simultaneously, by integrating the main-wheel dynamics model and friction characteristics, a runway identification function based on a Back Propagation Neural Network (BPNN) is designed to provide runway status information. The stability of the controller is verified via phase-plane analysis and Monte Carlo simulation. Subsequently, comparative Hardware-in-the-Loop (HIL) tests are conducted among PBM, PSO-PID, and the proposed GQPSO-PID controller under various runway conditions. The experimental results demonstrate that this composite controller can adapt to different speed domains and runway conditions, stably track the target slip ratio, effectively suppress skidding, and significantly improve braking efficiency, as well as exhibiting excellent robustness and control performance. Full article

For microbiological diagnostics, accurately counting and segmenting microbial colonies is extremely important. However, manual methods are labor-intensive and yield inconsistent results. We develop a hybrid model using swarm intelligence, combining a convolutional transformer with nested skip connections and global context with channel and spatial attention. Parameter tuning is supported by a variety of swarm optimization algorithms (e.g., Particle Swarm Optimization, Quantum-behaved Particle Swarm Optimization, and Differential Evolution Particle Swarm Optimization). Morphological refinement, including a further watershed transform, an attention graph, and post-processing, enhances colony boundaries by separating them. Grad-CAM++, Integrated Gradients, and temperature scaling provide a transparent and trustworthy model through explainability and post hoc calibration. The proposed model was extensively tested on the Microbial Colony Recognition and Circular Bacterial Colony Datasets, achieving a Dice score of 94.2%, an Intersection over the Union of 88.6%, and a mean absolute counting error of 2.7 colonies. These results significantly outperform several baseline models, including U-Net (88.1%), U-Net++ (89.7%), Attention U-Net (90.6%), and Swin-Unet (91.4%). Statistically significant improvements were confirmed (p < 0.01). A cross-dataset analysis demonstrates the framework’s robustness and cross-domain applicability, and positions it as a trustworthy, explainable automated model for assessing microbial colonies in laboratory and clinical settings. Full article

In this work, a multi-objective quantum particle swarm optimization (QPSO) algorithm is proposed to address the optimal scheduling of non-dispatchable sources in a microgrid energy management system (MGEMS) for residential areas under utility-induced demand-side management (DSM) programs. While taking economic and environmental aspects into account, the goal is to maximize energy management by integrating a variety of distributed generation (DG) units with an energy storage device. Using real-time meteorological data, two case studies were analyzed and simulated using MATLAB/Simulink R2025b. The simulation results reveal that the optimum optimization outcome among the case studies is obtained at a higher DSM load participation level of 10%. Without the involvement of DSM, MG’s producing units in the first case had the highest carbon emissions of 797.110 kg and an overall operating cost of 267.10 €. Similarly, with the involvement of DSM, the second case had the lowest overall operating cost of 155.01 € and the lowest carbon emissions of 748.731 kg. The second case, which has optimal DG scheduling, is the suggested way to improve microgrid efficiency and provide a dependable power supply with low operating costs and emission reduction. Full article

For the sake of investigating the theoretical design optimization of high-rise plane frames, an optimization model was established by taking the minimum top-story lateral displacement as the objective function and treating material strength, story height, and span length as design variables. The design parameters of the frame were optimized using an Assimilation–Competition Quantum-behaved Particle Swarm Optimization (ACQPSO) algorithm. First, the accuracy and computational efficiency of the ACQPSO algorithm were evaluated using four benchmark functions. Then, a five-span, seven-story reinforced-concrete plane frame with a total span of 24 m and a total height of 34 m was taken as a case study. The cross-sectional dimensions of the beams and columns were determined according to relevant design specifications, and the top-story lateral displacement calculated by the D-value method was verified using the Finite Element Method (FEM), confirming its accuracy and effectiveness. Finally, a parametric analysis was carried out to investigate the effects of material strength, story height, span length, and member cross-sectional dimensions on the objective function. The results indicate that story height and column concrete strength have a greater influence on the top-story lateral displacement, whereas the effect of span length is relatively small. In addition, the cross-sectional dimensions of beams and columns affect the top-story lateral displacement more significantly than beam strength. Full article

This study provides a comprehensive evaluation of bagging ensemble models for financial time series (FTS) classification and addresses a gap in the literature regarding how bootstrap methods, ensemble sizes, voting mechanisms, and loss functions jointly influence model performance. The analysis evaluates decision tree (DT), logistic regression (LR), and multi-layer perceptron (MLP) ensemble models modified by six time series bootstrap methods, five ensemble sizes, and three voting mechanisms across six FTS data sets. The study also examines the influence of entropy- and profit-based loss functions within particle swarm (PSO) and quantum-inspired particle swarm (QPSO) optimization for weighted voting. The results show that LR-based ensembles provide the strongest overall performance and outperform ARIMA, DT, LR, MLP, and LSTM baseline models on both accuracy and profit metrics. Bootstrap effects are model specific. DT and MLP ensembles perform best under the Tukey bootstrap, while LR ensembles achieve strong results under the block bootstrap, the sub-sample bootstrap method, and the Tukey method, and remain the strongest performers across all bootstrap configurations. Optimized voting mechanisms yield clear improvements over equal-weight majority voting, with the profit loss function producing the most consistent gains. The findings also indicate that FTS classification problems exhibit an optimal range of ensemble sizes, as larger ensembles do not always improve performance. The study contributes a systematic assessment of ensemble design choices for FTS classification and highlights the importance of jointly considering bootstrap diversity, ensemble size, and voting strategy when developing ensemble models for financial applications. Full article

In different light intensities, photovoltaic (PV) arrays with multi-peak characteristics encounter numerous challenges in maximum power point tracking (MPPT) control, such as low computational efficiency, slow convergence speed, and susceptibility to local optima. To address these issues, this study proposes an improved quantum particle swarm optimization (QPSO) algorithm that combines the advanced features of the Whale Optimization Algorithm (WOA) with the concept of Lévy flight, thereby forming a novel mechanism. The convergence of this algorithm, together with the particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) algorithms, was simulated and evaluated using 10 single-peak and multi-peak test functions, and then applied to a simulation model of PV array partial shading. The results show that, compared with the traditional PSO method, the tracking accuracy of this algorithm is improved by 2.80% and the convergence speed is increased by 57.14%. Under both static and sudden shading conditions, this algorithm can effectively enhance the tracking ability of the maximum power point of the PV array and achieve stable maximum power output. The average tracking accuracy of this algorithm reaches 99.71% and the average tracking speed is 0.06 s in simulation, showing an obvious advantage over both the PSO and QPSO algorithms. This simulation-based validation confirms the algorithm’s effectiveness, though hardware validation remains for future work. These results fully demonstrate the unique advantages and innovation of this algorithm in dealing with complex optimization problems, laying a solid foundation for improving the efficiency and reliability of PV systems and providing strong support for related research in this field. Full article

In the highly competitive e-commerce landscape, platforms must strategically balance complex operational and marketing parameters. These real-world systems inherently involve high-dimensional nonlinear interactions and strongly coupled variables, leading to complex consumer response behaviors and highly non-convex optimization landscapes. Traditional optimization approaches usually suffer from high computational costs in business environments, while conventional surrogate models are prone to premature convergence during hyperparameter estimation. To address these management and operational challenges, this study proposes a Chaos-initialized Quantum-behaved Particle Swarm Optimization Kriging (CQPSO–Kriging) framework. Chaotic mapping is introduced to enhance population diversity, while quantum-behaved particle dynamics improve global exploration capability. Utilizing large-scale real-world transaction data from the Brazilian e-commerce industry, high-fidelity surrogate response surfaces are constructed for three core business indicators: profitability, customer loyalty, and value density. Experimental results show that the proposed CQPSO–Kriging model significantly outperforms conventional approaches, such as support vector regression and radial basis function networks, achieving an exceptional coefficient of determination of R² = 0.9586 in profit prediction. Furthermore, Sobol variance-based global sensitivity analysis is employed to extract critical managerial insights, revealing that financial variables act as interaction-driven utility multipliers in consumer decision-making. Multi-objective Pareto analysis further demonstrates that profit maximization naturally converges toward a balanced operational configuration, providing a robust quantitative tool for e-commerce precision marketing. Full article

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy. Full article

A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV equipped with a biomimetic skipping plate. By comprehensively accounting for the aerodynamic, impact, hydrodynamic, and frictional forces during the water entry process, a dynamic model for the HAAV’s gliding water entry is established. The reliability of the model is verified through comparisons between numerical simulations and theoretical predictions. Parametric modeling of the skipping plate’s configuration and layout is performed to analyze the influence of different parameters on the water entry dynamics. With the objectives of minimizing the overload and pitch angle variation, a hybrid infilling strategy based on a radial basis function neural network (RBFNN) surrogate model is constructed to improve optimization efficiency. This is combined with a quantum-behaved particle swarm optimization (QPSO) algorithm to conduct the multi-objective optimization of the biomimetic plate, thereby obtaining its optimal configuration and layout parameters. The results demonstrate that the established dynamic model is effective and can accurately capture the kinematic characteristics of the gliding water entry process. The error between the peak load and the pitch angle variation is less than 5%. Compared with the direct QPSO algorithm, the proposed method reduces the number of model evaluations by 66.7%, the computational time by 52.1%, and the optimal solution response value by 12.01%, demonstrating strong potential for engineering applications. Full article

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro-inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three-switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve-aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications. Full article

The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities. Full article

As small modular reactors (SMRs) evolve towards longer lifespans, autonomous operation, and high reliability, the accuracy and reliability of sensor data are crucial for ensuring the safe operation of nuclear power systems. To improve the accuracy of multi-source sensor fault detection and localization in small reactors, this paper proposes a multi-scale cross-correlation-based convolutional autoencoder (MSCC-CAE) framework. First, multiple sensor cross-correlation matrices are constructed across multiple time scales to explicitly characterize the dynamic coupling relationships between heterogeneous sensors. These multi-scale correlation features can effectively capture both short- and long-term dependencies among sensors. Then, a convolutional autoencoder is used to compress and reconstruct the correlation matrix, thereby learning low-dimensional discriminative representations for fault detection. To enhance the stability and generalization of the proposed framework, a multi-strategy improved quantum particle swarm optimization (MSQPSO) algorithm is proposed to adaptively optimize key network hyperparameters. Finally, the proposed method was validated using data from an SMR simulation model. Experimental results demonstrate that the proposed MSCC-CAE achieves a fault detection accuracy of 98.21%, outperforming CNN and conventional CAE models by 15.17 and 12.04 percentage points, respectively. The localization accuracy reaches 97.12%. These results verify the effectiveness and superiority of the proposed framework for intelligent sensor fault detection in the SMR system. Full article

The growing integration of renewable energy sources in grid-connected microgrids (MG) has made it increasingly challenging to attain the most cost-effective and emission-efficient power dispatch in the face of uncertainty. This study addresses the scheduling problem of MG under utility-induced demand side load participation level for residential areas. Our research overcomes the constraints of conventional techniques by utilizing quantum-inspired particle swarm optimization (QPSO) to improve the operational efficiency and resilience of MG’s. In this study, a three-stage stochastic framework is proposed to address the optimal energy scheduling of MGs while taking economic and emission aspects into account. Using real-time meteorological data, five Cases were investigated and simulated using MATLAB/Simulink. Without the involvement of load participation, MG’s producing units in first Case, had carbon emissions of 797.110 kg and an operating cost of 267.10 €. Similar to this, the impact of demand side on the MG was evaluated in the remaining Cases. According to the simulation results, the fifth Case, which has optimal DGs scheduling, is the suggested way to improve MGs efficiency and provide a dependable power supply with low operating costs, emission reduction, and convergence features. This study not only demonstrates the practicality of QPSO algorithms but also paves the way for more resilient, efficient, and sustainable energy systems. Full article

Accurate estimation of the state of charge (SOC) is essential for the safe and efficient operation of lithium-ion batteries. Conventional Adaptive Unscented Kalman Filter (AUKF) methods often exhibit limited accuracy, primarily due to the empirical selection of process and measurement noise covariance matrices. To overcome this limitation, this study proposes a QPSO-AUKF algorithm based on a second-order RC equivalent circuit model, which integrates Quantum-behaved Particle Swarm Optimization (QPSO) with online parameter identification. In this approach, the QPSO algorithm optimizes the noise covariance matrices, which are subsequently used within the AUKF framework for SOC estimation. MATLAB R2020a simulations conducted on the Maryland and Wisconsin datasets demonstrate that the QPSO-AUKF reduces the root mean square error (RMSE) by more than 60% compared with the conventional AUKF, indicating a significant improvement in SOC estimation accuracy. Full article

To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing (VEC) framework. This approach integrates RIS to optimize wireless connectivity and semantic communication to minimize latency by transmitting semantic features. We formulate a comprehensive joint optimization problem by optimizing offloading ratios, the number of semantic symbols, and RIS phase shifts. Considering the problem’s high dimensionality and non-convexity, we propose a two-tier hybrid scheme that employs Proximal Policy Optimization (PPO) for discrete decision-making and Linear Programming (LP) for offloading optimization. The simulation results have validated the proposed framework’s superiority over existing methods. Specifically, the proposed PPO-based hybrid optimization scheme reduces the average end-to-end latency by approximately 40% to 50% compared to Genetic Algorithm (GA) and Quantum-behaved Particle Swarm Optimization (QPSO). Moreover, the system demonstrates strong scalability by maintaining low latency even in congested scenarios with up to 30 vehicles. Full article