Search Results (415)

High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy cooperative optimization framework derived from stock exchange trading principles, referred to as MESETO. The proposed method departs from the single-path evolutionary process of the standard SETO algorithm by introducing a diversified strategy collaboration mechanism that enables the dynamic adjustment of search behaviors throughout the optimization process. Multiple complementary update strategies are jointly employed to balance global exploration and local exploitation, while an adaptive probability regulation scheme continuously reallocates computational effort toward strategies that demonstrate superior performance. In addition, a solution validation mechanism is incorporated to prevent population degradation by rejecting ineffective evolutionary moves, thereby enhancing convergence stability. Extensive numerical experiments conducted on the CEC2017 and CEC2022 benchmark suites across different dimensional configurations demonstrate that MESETO consistently achieves improved solution accuracy, faster convergence, and stronger robustness compared with several representative state-of-the-art metaheuristic algorithms. Furthermore, the applicability of the proposed optimizer is verified through a 24 h microgrid economic scheduling case that integrates renewable energy sources, energy storage systems, dispatchable generators, and grid interaction. Simulation results confirm that MESETO effectively reduces operational costs while maintaining stable and efficient scheduling performance. Overall, the results indicate that MESETO constitutes a reliable and efficient optimization framework for solving complex global optimization problems and practical energy management applications. Full article

Real-time performance remains a core requirement for safety-critical robotic applications. ROS 2 has become a de facto middleware standard, while Docker is increasingly adopted for modular and portable deployment. However, embedded hardware updates often constrain Linux distributions and real-time kernel versions, while existing software stacks depend on older ROS 2 releases and legacy libraries. This mismatch forces costly porting and revalidation, motivating heterogeneous deployments that mix ROS 2 versions across host and Docker container runtimes. Yet the overheads introduced by Docker and cross-version ROS 2 communication are not well quantified in terms of real-time guarantees. This paper presents a Docker-enabled real-time framework for evaluating robotic applications in heterogeneous ROS 2 deployments. The framework integrates an RT-PREEMPT–patched Linux kernel, Dockerized ROS 2 distributions, and configurable cross-version communication pathways to enable controlled, repeatable experiments without full-stack migration. We empirically quantify Docker-induced effects on real-time execution using task periodicity, jitter, and response time, and assess ROS 2 communication using end-to-end latency under host-only, container-only, and hybrid configurations. To demonstrate practical viability, we apply the framework to an operational mobile-robot use case that integrates legacy control code with new modules, including a reinforcement-learning decision layer, within a mixed host–container ROS 2 stack. The resulting analyses provide reusable tooling and actionable guidelines for deploying deterministic ROS 2 systems under containerized heterogeneous constraints. Full article

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements. Full article

Federated Learning (FL) enables collaborative model training without sharing raw data, offering a promising solution for privacy-sensitive applications. However, in real-world deployments, significant disparities in client computational capabilities lead to imbalanced model updates, resulting in slow convergence and degraded model generalization. To address this challenge, this paper proposes a novel federated aggregation optimization method, FedAWR, which features adaptive adjustment of learning rates and weights. Specifically, during the global aggregation phase, our method dynamically adjusts each client’s aggregation weight based on its computational capability and configures an appropriate learning rate to balance training progress. Experiments on multi-classification tasks using the Steel Rail Defect and CIFAR-10 datasets demonstrate that the proposed method exhibits significant advantages over mainstream federated algorithms in both convergence efficiency and model generalization performance, thereby validating its effectiveness and superiority. Full article

We propose a stepwise in-flight method for automatically determining flight-envelope-related parameters for the longitudinal control of small fixed-wing unmanned aerial vehicles (UAVs), including pitch-angle limits, maximum climb and sink rate limits, and the cruise (trim) throttle. The method performs steady-state evaluation using onboard state estimates and sequentially updates the parameter set of ArduPilot’s energy-based longitudinal controller (Total Energy Control System, TECS). The algorithm was implemented in ArduPilot Plane v4.6.1 via Lua scripting, enabling real-time parameter determination and immediate application during flight. The proposed procedure was assessed in software-in-the-loop (SITL) simulations and further validated through flight experiments. The results demonstrated that the target parameters could be automatically identified during flight and implemented in real time. The proposed method is expected to reduce reliance on expert trial-and-error and contribute to improving portability across airframes and configuration changes. Full article

Shutdown dose rate (SDDR) assessments have been performed for the DEMO tokamak model, including the latest design and environmental configurations. The main objective of this study was to evaluate the shutdown radiation fields and establish dose rate limits to ensure safe personnel access to the Vacuum Vessel (VV) and nearby components. The simulations were based on the DEMO baseline model, further refined with the minor updates of the lower port, equatorial port limiter, and upper port assemblies. The computational approach employed the Monte Carlo particle transport code MCNP for neutron and photon transport calculations, coupled with the activation and decay code FISPACT-II to determine time-dependent decay gamma source terms. The mesh-coupled Rigorous Two-Step (R2Smesh) methodology developed in KIT was applied to achieve spatially resolved decay of photon source distributions and to compute corresponding SDDR 3D maps within the DEMO reactor configuration. The results provide a detailed characterization of the residual radiation environment inside the VV, offering insight into the accumulated activity, shielding performance of different materials, and potential access scenarios for maintenance operations in next-generation fusion devices. Full article

Reinforcement learning (RL) is widely used to learn sequential decision-making policies in complex environments. Deep Q-network (DQN) extends Q-Learning using deep neural networks, enabling learning in high-dimensional state spaces. However, conventional DQN-based approaches can exhibit variability in learning stability and convergence speed even under similar training conditions. Random Ensemble Mixture (REM) has been introduced to improve stability by combining multiple Q-value estimates, but it typically requires running multiple models simultaneously, which increases computational cost. This paper proposes an enhanced DQN method that integrates REM with a Weight Referring and Merging (WRM) mechanism to improve training stability and efficiency. The proposed approach updates a single primary agent using standard DQN learning while maintaining diversity among auxiliary agents by selectively referring to and partially merging weights from the primary network. Q-values from the primary and auxiliary agents are then combined through REM to produce the final value estimate for action selection. Experiments in the Catch Game environment indicate that the proposed method reaches stable performance earlier than a baseline DQN and reduces training time under the tested configuration (approximately 78%). While the results are encouraging in this environment, further evaluation on additional benchmarks is required to assess broader applicability. Full article

Contactless text entry is increasingly important in immersive and constrained computing environments, yet most vision-based approaches rely on character-level recognition or key localization, which are fragile under monocular sensing. This study investigates the feasibility of recognizing natural QWERTY typing motions directly at the word level using only a single RGB camera, under a fixed single-user and single-camera configuration. We propose a word-level contactless typing framework that models each word as a distinctive spatiotemporal finger motion pattern derived from hand joint trajectories. Typing motions are temporally segmented, and direction-aware finger displacements are accumulated to construct compact motion representations that are relatively insensitive to absolute hand position and typing duration within the evaluated setup. Each word is represented by multiple motion prototypes that are incrementally updated through online learning with a trial-delayed adaptation protocol. Experiments with vocabularies of up to 200 words show that the proposed approach progressively learns and recalls word-level motion patterns through repeated interaction, achieving stable recognition performance within the tested configuration at realistic typing speeds. Additional evaluations demonstrate that learned motion representations can transfer from physical keyboards to flat-surface typing within the same experimental setting, even when tactile feedback and visual layout cues are reduced. These results support the feasibility of reframing contactless typing as a word-level motion recall problem, and suggest its potential role as a complementary component to character-centric camera-based input methods under constrained monocular sensing. Full article

This research focuses on characterizing typical displacement patterns in concentrically braced frame (CBF) systems for use in the direct displacement-based seismic design (DDBD) methodology. Using the finite-element program SeismoStruct, two-dimensional models were developed for nonlinear time–history analysis (NLTHA), employing scaled real accelerograms, conventional gravity loads, and detailed numerical models. Thirty varied CBF configurations with different numbers of storeys, spans, and bracing types were evaluated. It was found that the conventional displacement profiles, commonly used for moment-resisting frames, do not accurately represent the actual behavior of CBFs in the inelastic range. Therefore, fitted equations were developed and validated to accurately represent the actual displacements of CBF systems, accounting for factors such as the fundamental vibration period and equivalent system damping. These improvements enable the seismic design optimization, advanced displacement and drift control, and strengthen structural safety and performance in high-seismicity zones in the region. This contribution is relevant to performance-based engineering, facilitating a plausible update to regulations and best practices for seismic-resistant design. Full article

This paper proposes a cooperative localization method based on time-division processing of interferometric measurements, in which the receiver updates the signals from multiple UAVs in separate time slots, thereby reducing spectrum usage and baseband hardware overhead. A Kalman-enhanced tracking loop is designed to achieve high-precision carrier-phase and Doppler estimation under low-SNR conditions. For angle estimation, a time-division update strategy is employed such that the receiver performs full carrier tracking for only one UAV in each time slot, while the carrier phases of the remaining UAVs are extrapolated from the Doppler states estimated in the previous epoch. This avoids the hardware complexity associated with maintaining multiple parallel tracking loops. By fusing the estimated azimuth, elevation, and pseudorange measurements with the master UAV’s high-precision GNSS observations, a factor-graph-based sliding-window cooperative localization algorithm is constructed. Simulation results show that the proposed method improves the RMSE of carrier-phase and Doppler estimation by nearly an order of magnitude compared with the traditional FLL-assisted PLL. The system maintains angle estimation accuracy better than 0.01° within a four-node configuration and achieves centimeter-level ranging accuracy when SNR ≥ 0 dB. In a cooperative flight scenario with one master and three follower UAVs, the method consistently delivers sub-decimeter 3D localization accuracy. Full article

This paper reports on a fully automatic structured Computational Fluid Dynamics (CFD) mesh generation method based on a parametric multi-block topology for airfoil configurations. The method parameterizes the control vertices and the control edges, which construct the multi-block topology, and then assembles blocks with the control edges. Once the airfoil shape is determined, the topology is immediately updated based on the parameterization, and the CFD mesh of the airfoil is generated using transfinite interpolation. The present method is tested on airfoils with different topologies, shapes, and mesh sizes to check its robustness, efficiency, and quality. The test results show that the mesh of an airfoil of any shape can be generated automatically with high quality. In addition, an airfoil CFD mesh with about 50 million nodes can be automatically generated in less than ten seconds on a laptop, and the Jacobi of over 97% of the mesh cells is larger than 0.9. The flow simulation results for the NACA0012 airfoil agree well with the wind-tunnel test data, demonstrating the method’s applicability to CFD. Full article

The integration of unmanned aerial vehicles (UAVs) and Federated Learning (FL) enables distributed model training while preserving data privacy. To overcome the challenges caused by centralized and synchronous model updates, we integrate Directed Acyclic Graph (DAG) blockchain-based FL into UAV networks. In this decentralized and asynchronous framework, UAVs can independently and autonomously participate in the FL process according to their own requirement. To achieve the high FL performance, it is essential for UAVs to actively contribute their computational and data resources to the FL process. However, it is challenging to ensure that UAVs consistently contribute their resources, as they may have a propensity to prioritize their own self-interest. Therefore, it is crucial to design effective incentive mechanisms that encourage UAVs to actively participate in the FL process and contribute their computational and data resources. Currently, research on effective incentive mechanisms for DAG blockchain-based FL framework in UAV networks remains limited. To address these challenges, this paper proposes a novel incentive mechanism that integrates both rewards and punishments to encourage UAVs to actively contribute to FL and to deter free riding under incomplete information. We formulate the interactions among UAVs as an evolutionary game, and the aspiration-driven rule is employed to imitate the UAV’s decision-making processes. We evaluate the proposed mechanism for UAVs within a DAG blockchain-based FL framework. Experimental results show that the proposed incentive mechanism substantially increases the average UAV contribution rate from $77.{04}_{\pm 0.84}\%$ (without incentive mechanism) to $97.{48}_{\pm 1.29}\%$. Furthermore, the higher contribution rate results in an approximate 2.23% improvement in FL performance. Additionally, we evaluate the impact of different parameter configurations to analyze how they affect the performance and efficiency of the FL system. Full article

High penetration and output volatility of island wind and photovoltaics (PV) pose challenges to energy consumption and supply–demand balance, and cost-effective energy storage configuration. A coupled dispatch model for a wind–PV–storage system is proposed, which treats multiple canal units as virtual ‘loads’ that switch between generation and pumping under constraints of power balance and available water head model. Considering the variable reservoir–irrigation feature, a multi-objective model framework is developed to minimize both economic cost and storage capacity required. An augmented Lagrangian–Nash product enhanced NSGA-II (AL-NP-NSGA-II) algorithm enforces constraints of irrigation shortfall and overflow via an augmented Lagrangian term and allocates fair benefits across canal units through a Nash product reward. Moreover, updates of Lagrange multipliers and reward weights maintain power balance and accelerate convergence. Finally, a case simulation (3.7 MW wind, 7.1 MW PV, and 24 h rural load) is performed, where 440.98 kWh storage eliminates shortfall/overflow and yields 1.5172 × 10⁴ CNY. Monte Carlo uncertainty analysis (±10% perturbations in load, wind, and PV) shows that increasing storage to 680 kWh can stabilize reliability above 98% and raise economic benefit to 1.5195 × 10⁴ CNY. The dispatch framework delivers coordination of irrigation and power balance in island microgrids, providing a systematic configuration solution. Full article

Optimizing the furniture layout of middle school laboratories is crucial for improving the emergency safety, operational efficiency, and resilience of teaching buildings. This study used AnyLogic software to model and simulate pedestrian evacuation behavior in a typical middle school laboratory layout. In a standardized laboratory (90.75 m²), we constructed a behavior-oriented multi-agent evacuation model. The model incorporated key student parameters, including shoulder width (312–416 mm), walking speed (1.5–2.5 m/s), and reaction time (10–15 s). To ensure comparability between different layouts, the number of evacuees was fixed at 48. Evacuation performance was evaluated based on total evacuation time, spatial density, and detour distance. The results showed that the hybrid layout achieved the shortest evacuation time (28.0 s), which was 10.3% shorter than the island layout (31.2 s) and 34.7% shorter than the parallel layout (42.9 s). The hybrid layout also had a shorter average detour distance (9.78 m) and the lowest path variability (coefficient of variation CV = 0.33), indicating a more balanced evacuation load and a smaller bottleneck effect. Overall, these findings provide evidence-based recommendations for improving laboratory safety, space utilization, and behavioral adaptability, and provide a quantitative reference for updating educational building codes, school laboratory construction standards, and guidelines for laboratory furniture and safety facility configuration. Full article

Accurate volatility forecasting is essential for risk management in increasingly interconnected financial markets. Traditional econometric models capture volatility clustering but struggle to model nonlinear cross-market spillovers. This study proposes a Temporal Graph Attention Network (TemporalGAT) for multi-horizon volatility forecasting, integrating LSTM-based temporal encoding with graph convolutional and attention layers to jointly model volatility persistence and inter-market dependencies. Market linkages are constructed using the Diebold–Yilmaz volatility spillover index, providing an economically interpretable representation of directional shock transmission. Using daily data from major global equity indices, the model is evaluated against econometric, machine learning, and graph-based benchmarks across multiple forecast horizons. Performance is assessed using MSE, $R^2$, MAFE, and MAPE, with statistical significance validated via Diebold–Mariano tests and bootstrap confidence intervals. The study further conducts a strict expanding-window robustness test, comparing fixed and dynamically re-estimated spillover graphs in a fully out-of-sample setting. Sensitivity and scenario analyses confirm robustness across hyperparameter configurations and market regimes, while results show no systematic gains from dynamic graph updating over a fixed spillover network. Full article