Search Results (23,554)

As the automation and intelligence of low-voltage distribution networks continue to advance, the inter-layer coupling between medium- and low-voltage distribution networks is increasingly strengthened, making traditional fixed-point iteration methods inadequate for distributed power flow calculation in such a collaborative framework. To address this issue, this paper proposes a distributed power flow calculation method for medium- and low-voltage distribution networks based on edge intelligence. First, a cooperative operational framework for medium- and low-voltage distribution networks is designed by integrating edge intelligence technology. Then, a distributed power flow calculation model is established, and its fixed-point iterative characteristics are analyzed. A convergence index calculation method based on small perturbations is proposed, followed by an iterative algorithm based on continuous intersection estimation. Finally, simulation case studies validate the proposed method in terms of accuracy, convergence, and computational efficiency, demonstrating its capability to meet the modeling and analytical needs of power flow calculation in medium- and low-voltage distribution networks, providing methodological support for the development of distributed intelligent power grids. Full article

This paper addresses the Multi-Target Reconnaissance (MTR) problem for heterogeneous Fixed-Wing Unmanned Aerial Vehicles (FW-UAVs), focusing on synchronized and time-optimal mission execution under stringent constraints. A two-stage coordinated assignment and planning framework is proposed. First, a time-balanced clustering algorithm is designed to minimize the overall mission duration while balancing individual UAV workloads by jointly employing a target reallocation strategy and an improved Genetic Algorithm (GA). Subsequently, an online trajectory planning method based on differential flatness is developed, integrating a robust replanning and flight-time synchronization strategy to ensure coordinated execution. Simulation results unequivocally demonstrate that the proposed approach enhances time optimality and temporal coordination in complex scenarios. Full article

Electricity demand forecasting plays a crucial role in energy planning and power system operation. However, it is affected by numerous factors and complex relationships, making accurate prediction challenging. Therefore, from the perspective of sample diversity in the base dataset, we propose an improved stacking-based ensemble regression algorithm to enhance the accuracy of electricity demand forecasting. Firstly, a continuous sampling strategy is constructed between the sample integration selection probability and the base dataset using D²-Sampling and KNN; secondly, multiple base regression models are integrated through stacking to improve the predictive performance. In the electricity demand forecasting experiments conducted on three different datasets and across multiple base models, the proposed improved stacking ensemble learning regression algorithm (DK-Stacking) achieved the best performance. This symmetric experimental evaluation ensured consistent and balanced assessment of the model performance across datasets and models, highlighting the robustness and generalization of the proposed algorithm. Compared to the ANN, SVR, and RF models, its prediction accuracy increased by more than 1 percentage point. Even when compared to the optimized XGBoost model, it showed an improvement of 0.44 percentage points. Overall, the proposed DK-Stacking demonstrates symmetry-inspired robustness in electricity demand forecasting through the balanced treatment of datasets and model integration. Full article

This paper introduces a novel, integrated optimization framework for sustainable multi-site logistics planning, which simultaneously addresses production, inventory, and distribution decisions. The proposed hybrid methodology combines a Genetic Algorithm (GA) with Linear Programming (LP) to minimize total logistics costs while proactively integrating environmental impact assessment. The model determines optimal production schedules across multiple facilities, manages inventory levels, and solves the Vehicle Routing Problem (VRP) for distribution. A key innovation is the incorporation of a CO₂ emission forecasting module directly into the optimization loop, allowing the algorithm to anticipate and mitigate the environmental consequences of logistics decisions during the planning phase, rather than performing a post-hoc evaluation. The framework was implemented in Python 3.13.4, utilizing the PuLP library for LP components and custom-developed GA routines. Its performance was validated through a numerical case study and a series of sensitivity analyses, which investigated the effects of fluctuating demand and key cost parameters. The results demonstrate that the inclusion of emission forecasting enables the identification of solutions that achieve a superior balance between economic and environmental objectives, leading to significant reductions in both total costs and predicted CO₂ emissions. This work provides practitioners with a scalable and practical decision-support tool for designing more sustainable and resilient multi-echelon supply chains. Full article

Tropical rainforests play a vital role in maintaining global ecological balance, carbon cycling, and biodiversity conservation, making research on their biomass dynamics scientifically significant. This study integrates multi-source remote sensing data, including canopy height derived from GEDI and ICESat-2 satellite-borne lidar, Landsat imagery, and environmental variables, to estimate forest biomass dynamics in Hainan’s tropical rainforests at a 30 m spatial resolution, involving a correlation analysis of factors influencing spatiotemporal changes in Hainan Tropical Rainforest biomass. The research aims to investigate the spatiotemporal variations in forest biomass and identify key environmental drivers influencing biomass accumulation. Four machine learning algorithms—Backpropagation Neural Network (BP), Convolutional Neural Network (CNN), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were applied to estimate biomass across five forest types from 2003 to 2023. Results indicate the Random Forest model achieved the highest accuracy (R² = 0.82). Forest biomass and carbon stocks in Hainan Tropical Rainforest National Park increased significantly, with total carbon stocks rising from 29.03 million tons of carbon to 42.47 million tons of carbon—a 46.36% increase over 20 years. These findings demonstrate that integrating multimodal remote sensing data with advanced machine learning provides an effective approach for accurately assessing biomass dynamics, supporting forest management and carbon sink evaluations in tropical rainforest ecosystems. Full article

The rapid growth of electric vehicle (EV) adoption poses significant challenges for the rational planning of charging infrastructure, where economic efficiency and service quality are inherently conflicting. To support scientific decision-making in charging station siting, this study proposes an integrated multi-objective optimization and decision-support framework that combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an entropy-weighted TOPSIS method. A bi-objective siting model is developed to simultaneously minimize total operator costs and maximize user satisfaction. User satisfaction is explicitly characterized by a nonlinear charging distance perception function and a queuing-theoretic waiting time model, enabling a more realistic representation of user service experience. To enhance convergence performance and solution diversity, the NSGA-II algorithm is improved through variable-wise random chaotic initialization, opposition-based learning, and adaptive crossover and mutation operators. The resulting Pareto-optimal solutions are further evaluated using an improved entropy-weighted TOPSIS approach to objectively identify representative compromise solutions. Simulation results demonstrate that the proposed framework achieves superior performance compared with the standard NSGA-II algorithm in terms of operating cost reduction, user satisfaction improvement, and multi-objective indicators, including hypervolume, inverted generational distance, and solution diversity. The findings confirm that the proposed NSGA-II–TOPSIS framework provides an effective, robust, and interpretable decision-support tool for EV charging station planning under conflicting objectives. Full article

To address the autonomous traversability challenge of an Unmanned Articulated Road Roller (UARR) operating on harsh terrains where low-adhesion slipperiness and geometric irregularities are coupled, and traction capacity is severely limited, this paper proposes a Terrain-Adaptive Maximum-Entropy Policy Optimization (TAMPO). A unified multi-physics simulation platform is constructed, integrating a high-fidelity vehicle dynamics model with a parameterized terrain environment. Considering the prevalence of geometric irregularities in construction sites, a parameterized mud-pit model is established—generalized from a representative case—as a canonical physical model and simulation carrier for this class of traversability problems. Based on this model, a family of training and test scenarios is generated to span a broad range of terrain shapes and adhesion conditions. On this foundation, the TAMPO algorithm is introduced to enhance vehicle traversability on complex terrains. The method comprises the following: (i) a Terrain Interaction-Critical Reward (TICR), which combines dense rewards representing task progress with sparse rewards that encourage terrain exploration, guiding the agent to both climb efficiently and actively seek high-adhesion favorable terrain; and (ii) a context-aware adaptive entropy-regularization mechanism that fuses, in real time, three feedback signals—terrain physical difficulty, task-execution efficacy, and model epistemic uncertainty—to dynamically regulate policy entropy and realize an intelligent, state-dependent exploration–exploitation trade-off in unstructured environments. The performance and generalization ability of TAMPO are evaluated on training, interpolation, and extrapolation sets, using PPO, SAC, and DDPG as baselines. On 90 highly challenging extrapolation scenarios, TAMPO achieves an average success rate (S.R.) of 60.00% and an Average Escape Time (A.E.T.) of 17.56 s, corresponding to improvements of up to 22.22% in S.R. and reductions of up to 5.73 s in A.E.T. over the baseline algorithms, demonstrating superior decision-making performance and robust generalization on coupled slippery and irregular terrains. Full article

This paper proposes a comprehensive framework for integrating Digital Twins (DT) with real-time traffic optimization systems to enhance urban mobility management in Smart Cities. Using the Pobitno Roundabout in Rzeszów as a case study, we established a calibrated microsimulation model (validated via the GEH statistic) that serves as the core of the proposed Digital Twin. The study goes beyond static scenario analysis by introducing an Adaptive Inflow Metering (AIM) logic designed to interact with IoT sensor data. While traditional geometrical upgrades (e.g., turbo-roundabouts) were analyzed, simulation results revealed that geometrical changes alone—without dynamic control—may fail under peak load conditions (resulting in LOS F). Consequently, the research demonstrates how the DT framework allows for the testing of “Software-in-the-Loop” (SiL) solutions where Python-based algorithms dynamically adjust inflow parameters to prevent gridlock. The findings confirm that combining physical infrastructure changes with digital, real-time optimization algorithms is essential for achieving sustainable “green transport” goals and reducing emissions in congested urban nodes. Full article

The escalating volume of waste electrical and electronic equipment (WEEE) poses a significant global environmental challenge. The disassembly of printed circuit boards (PCBs), a critical step for resource recovery, remains inefficient due to limitations in the adaptability and dexterity of existing automated systems. This paper proposes an intelligent disassembly system for PCB components that integrates a multimodal large language model (MLLM) with a multi-agent framework. The MLLM serves as the system’s cognitive core, enabling high-level visual-language understanding and task planning by converting images into semantic descriptions and generating disassembly strategies. A state-of-the-art object detection algorithm (YOLOv13) is incorporated to provide fine-grained component localization. This high-level intelligence is seamlessly connected to low-level execution through a multi-agent framework that orchestrates collaborative dual robotic arms. One arm controls a heater for precise solder melting, while the other performs fine “probing-grasping” actions guided by real-time force feedback. Experiments were conducted on 30 decommissioned smart electricity meter PCBs, evaluating the system on recognition rate, capture rate, melting rate, and time consumption for seven component types. Results demonstrate that the system achieved a 100% melting rate across all components and high recognition rates (90-100%), validating its strengths in perception and thermal control. However, the capture rate varied significantly, highlighting the grasping of small, low-profile components as the primary bottleneck. This research presents a significant step towards autonomous, non-destructive e-waste recycling by effectively combining high-level cognitive intelligence with low-level robotic control, while also clearly identifying key areas for future improvement. Full article

This study proposes a robust chaotic encryption framework based on a Fuzzy Rule-Based Sugeno Inference (FRBSI) system, integrated with high-level security analyses. The algorithm employs a dynamic mixture of Lorenz chaotic state variables, which are numerically modeled using the Euler-Forward method to ensure computational accuracy. Unlike conventional methods, the carrier signal’s characteristics are not static; instead, its amplitude and dynamic behavior are continuously adapted through the FRBSI mechanism, driven by the instantaneous thresholds of the information signal. The security of the proposed system was rigorously evaluated through Histogram analysis, Number of Pixels Change Rate (NPCR), and Unified Average Changing Intensity (UACI) metrics, which confirmed the algorithm’s high sensitivity to plaintext variations and resistance against differential attacks. Furthermore, Key Sensitivity tests demonstrated that even a single-bit discrepancy in the receiver-side Sugeno rule base leads to a total failure in signal reconstruction, providing a formidable defense against brute-force attempts. The system’s performance was validated in the MATLAB/Simulink of R2021a version environment, where frequency and time-domain analyses were performed via oscilloscope and Fourier transforms. The results indicate that the proposed multi-layered fuzzy-chaotic structure significantly outperforms traditional encryption techniques in terms of unpredictability, structural security, and robustness. Full article

The aim of this study is to present an integrated approach to ski personalization by combining a detailed material and geometric characterization of ski structure with real-time on-slope dynamic performance data. Longitudinal and torsional stiffness were selected as the basic material parameters for a wide range of skis with different application profiles. Acceleration graphs for turns were synchronized with video footage recorded by a drone, which provided real-time monitoring of the skiers’ actual course and facilitated the precise correlation between kinematic data and on-slope performance. To estimate style similarity between different skiers, the Pearson correlation coefficient was utilized due to its robust mathematical properties. Comprehensive analyses helped to finally formulate a multistage ski personalization algorithm. Full article

As an important component of waters constituent that affects ocean color and the underwater ecological environment, the accurate assessment of Chromophoric Dissolved Organic Matter (CDOM) is crucial for observing the continuous changes in the marine ecosystem. However, remote sensing estimation of CDOM remains challenging for both coastal and oceanic waters due to its weak optical signals and complex optical conditions. Therefore, the development of efficient, practical, and robust models for estimating the CDOM absorption coefficient in both coastal and oceanic waters remains an active research focus. This study presents a novel algorithm (denoted as DQAAG) that incorporates ultraviolet bands into the inversion model. The design leverages the distinct spectral absorption characteristics of phytoplankton versus detrital particles in the ultraviolet (UV) region, enabling improved discrimination of water color parameters. Furthermore, the algorithm replaces empirical formulas commonly used in semi-analytical approaches with an artificial intelligence model (deep learning) to achieve enhanced inversion accuracy. Using IOCCG hyperspectral simulation data and NOMAD dataset to evaluates Shanmugam (2011) (S2011), Aurin et al. (2018) (A2018), Zhu et al. (2011) (QAA-CDOM), DQAAG, the results indicate that the a_g(443) derived from the DQAAG exhibit good agreement with the validation data, with root mean square deviation (RMSD) < 0.3 m⁻¹, mean absolute relative difference (MARD) < 0.30, mean bias (bias) < 0.028 m⁻¹, coefficient of determination (R²) > 0.78. The DQAAG algorithm was applied to SeaWiFS remote sensing data, and validation was performed through match-up analysis with the NOMAD dataset. The results show the RMSD = 0.14 m⁻¹, MARD = 0.39, and R² = 0.62. Through a sensitivity analysis of the algorithm, the study reveals that R_rs(670) and R_rs(380) exhibit more significant characteristics. These results demonstrate that UV bands play a crucial role in enhancing the retrieval accuracy of ocean color parameters. In addition, DQAAG, which integrates semi-analytical algorithms with artificial intelligence, presents an encouraging approach for processing ocean color imagery to retrieve a_g(443). Full article

As urban power grids grow increasingly complex and underground space resources become increasingly scarce, traditional two-dimensional cable design methods face significant challenges in spatial representation accuracy and design efficiency. This study proposes an automated cable path planning method based on an improved Rapidly exploring Random Tree (RRT) algorithm. This framework first introduces an enhanced RRT algorithm (referred to as ABS-RRT) that integrates adaptive stride, target-biased sampling, and Soft Actor-Critic reinforcement learning. This algorithm automates the planning of serpentine cable laying paths in confined environments such as cable tunnels and manholes. Subsequently, through trajectory simplification and smoothing optimization, it generates final paths that are safe, smooth, and compliant with engineering specifications. Simulation validation on a typical cable tunnel project in a city’s core area demonstrates that compared to the traditional RRT algorithm, this approach reduces path planning time by over 57%, decreases path length by 8.1%, and lowers the number of nodes by 52%. These results validate the algorithm’s broad application potential in complex urban power grid projects. Full article

Extreme environments such as low pressure, high temperature, and intense radiation pose severe challenges for humidity sensors, causing conventional hygroscopic materials to exhibit sluggish responses, drift, and instability. In response, recent research has adopted multi-level strategies involving material modification, structural engineering, and packaging optimization to enhance the adaptability of humidity-sensitive materials in extreme environments. This review examines humidity sensing from an environmental perspective, integrating sensing mechanisms, material classifications, and application scenarios. The performance, advantages, and limitations of six major categories of humidity-sensitive materials, including carbon-based, metal oxides, conductive and insulating polymers, two-dimensional (2D) materials, and composites, are systematically summarized under extreme conditions. Finally, emerging development trends are discussed, highlighting a shift from material-driven to system-driven approaches. Future progress will rely on multidisciplinary integration, including interface engineering, multiscale structural design, and intelligent algorithms, to achieve higher accuracy, stability, and durability in extreme-environment humidity sensing. Full article

This paper addresses the critical challenge of multi-objective optimization in residential Home Energy Management Systems (HEMS) by proposing a novel framework based on an Improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. The study specifically targets the low convergence efficiency of Multi-Agent Deep Reinforcement Learning (MADRL) for coupled Battery Energy Storage System (BESS) and Air Source Heat Pump (ASHP) operation. The framework synergistically integrates an action constraint projection mechanism with an economic-performance-driven dynamic learning rate modulation strategy, thereby significantly enhancing learning stability. Simulation results demonstrate that the algorithm improves training convergence speed by 35–45% compared to standard MAPPO. Economically, it delivers a cumulative cost reduction of 15.77% against rule-based baselines, outperforming both Independent Proximal Policy Optimization (IPPO) and standard MAPPO benchmarks. Furthermore, the method maximizes renewable energy utilization, achieving nearly 100% photovoltaic self-consumption under favorable conditions while ensuring robustness in extreme scenarios. Temporal analysis reveals the agents’ capacity for anticipatory decision-making, effectively learning correlations among generation, pricing, and demand to achieve seamless seasonal adaptability. These findings validate the superior performance of the proposed centralized training architecture, providing a robust solution for complex residential energy management. Full article