Two-sided Energy Storage Cooperative Scheduling Method for Transmission and Distribution Network Based on Multi-agent Attention-deep Reinforcement Learning by CHEN Shi, ZHU Yujie, LIU Yihong, XU Liuchao, TANG Guodeng

“…Compared to traditional methods, the convergence speed and optimization performance are significantly improved. …”
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Mathematical model of on-demand route formation for public transport based on individual passenger requests in low-density population area by Svetlana S. Titova, Andrey V. Ostroukh

“…A mathematical model was developed that accounts for the specifics of populated areas with low population density, including uneven distribution of demand, large distances between populated areas, and limited financial resources. Various route optimization algorithms were investigated, and the most suitable method was selected for solving the stated problem. …”
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Short-Term Load Forecasting for Electrical Power Distribution Systems Using Enhanced Deep Neural Networks by Shewit Tsegaye, Sanjeevikumar Padmanaban, Lina Bertling Tjernberg, Kinde Anlay Fante

“…This represents a 7.486% improvement over the prediction obtained using only LSTM model. …”
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Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China by Quanshan Liu, Zongjun Wu, Ningbo Cui, Shunsheng Zheng, Shouzheng Jiang, Zhihui Wang, Daozhi Gong, Yaosheng Wang, Lu Zhao, Renjuan Wei

“…This study showed that combining multimodal information from low-cost UAV with the optimized machine learning algorithm can provide relatively accurate and robust estimates of citrus Gs. …”
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Beyond the Grid: GLRT-Based TomoSAR Fast Detection for Retrieving Height and Thermal Dilation by Nabil Haddad, Karima Hadj-Rabah, Alessandra Budillon, Gilda Schirinzi

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The Hydrodynamic Performance of a Vertical-Axis Hydro Turbine with an Airfoil Designed Based on the Outline of a Sailfish by Aiping Wu, Shiming Wang, Chenglin Ding

“…Response surface methodology was employed to establish predictive models for these critical performance indicators, effectively reducing computational resource consumption and experimental validation costs. The refined bio-inspired configuration demonstrated multi-objective performance improvements compared to the baseline configuration, validating the computational framework’s effectiveness for hydrodynamic profile optimization studies. …”
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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R² by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively.…”
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INFO-RF-based fault diagnosis and analysis method for busbars by Chen Xue, Jian Zhu, Haiou Cao, Yan Gu, Siyu Chen

“…A simulation model of a dual-busbar power system is first established, and key electrical quantities such as differential current, bus tie current, and voltage are extracted to quantify fault features using Root Mean Square (RMS) values. The RF model is then used to predict fault types and fault resistance, with the INFO algorithm iteratively optimizing the hyperparameters of the RF model to further improve prediction accuracy. …”
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Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks by Ihor Blinov, Virginijus Radziukynas, Pavlo Shymaniuk, Artur Dyczko, Kinga Stecuła, Viktoriia Sychova, Volodymyr Miroshnyk, Roman Dychkovskyi

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Research on path planning for coal mine rescue robots by ZHU Hongbo, YIN Hongliang

“…Firstly, an adjustment mechanism of collision constraint function is incorporated into the bidirectional A* algorithm to improve path safety. Next, a correction factor is incorporated into the cost function of the Bidirectional A* algorithm to ensure that the forward and backward search paths intersect, preventing them from diverging. …”
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Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models by Yasmine Gaaloul, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, Aissa Chouder, Mahmoud Hamouda, Santiago Silvestre, Sofiane Kichou

“…Accurate and reliable fault detection in photovoltaic (PV) systems is essential for optimizing their performance and durability. This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. …”
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Bond behavior of galvanized iron fiber reinforced concrete with recycled coarse aggregate and model prediction using machine learning by Md. Jahidul Islam, Ummul Wara Labiba, Tasfiah Faisal Chowdhury, Md. Shahjalal, Tanvir Mustafy, Tanvir Hassan Tusher

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Multi-Agent Mapping and Tracking-Based Electrical Vehicles with Unknown Environment Exploration by Chafaa Hamrouni, Aarif Alutaybi, Ghofrane Ouerfelli

“…When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. …”
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SC-Route: A Scalable Cross-Layer Secure Routing Method for Multi-Hop Inter-Domain Wireless Networks by Yanbing Li, Yang Zhu, Shangpeng Wang

“…Second, we introduce a cross-layer information fusion mechanism that allows nodes to adapt routing costs in real time under heterogeneous network conditions, thereby improving path reliability and load balancing. …”
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Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning by YAN Li, WEN Hu, WANG Zhenping, JIN Yongfei

“…The application results from the 215 working face of the Huangling No. 2 Coal Mine in Shaanxi Province showed that the maximum validity clustering rate (MVCR) and adjusted rand index (ARI) of SSGMM and SSK-Means reached 82.64% and 85.83%, respectively, significantly outperforming conventional clustering methods. After optimization through a dynamic feedback mechanism, boreholes initially classified as "poor" showed an improvement of 5.26% to 5.80% in extraction efficiency, achieving a 100% remediation rate.…”
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Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

“…This study proposes a novel approach that integrates the Dwarf Mongoose Optimization (DMO) algorithm with a Gated Recurrent Unit (GRU) neural network to develop an AI-powered interactive online learning model. …”
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Lossless Compression with Trie-Based Shared Dictionary for Omics Data in Edge–Cloud Frameworks by Rani Adam, Daniel R. Catchpoole, Simeon J. Simoff, Zhonglin Qu, Paul J. Kennedy, Quang Vinh Nguyen

“…The preprocessed data are subsequently transmitted to the cloud, where advanced compression algorithms, such as Zstd, GZIP, Snappy, and LZ4, further compress them. …”
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Robust Framework for PMU Placement and Voltage Estimation of Power Distribution Network by Nida Khanam, Mohd. Rihan, Salman Hameed

“…The framework improves network observability with a minimum of PMUs, lowering deployment costs while retaining excellent estimation accuracy. …”
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New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading by Dingyi Zhou, Zhifang Zhao, Wenfei Xi, Xin Zhao, Jiangqin Chao

“…The landslide susceptibility model is developed utilizing the Particle Swarm Optimization-Back Propagation (PSO-BP) algorithm. It includes evaluation techniques for regions without deformation rates. …”
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Electric Vehicle Cluster and Scheduling Strategy Based on Dynamic Game by LI Shuai, DING Xiying, JIANG Hongfa

“…The upper layer takes the peak shaving demand and peak shaving cost of distribution system operator (DSO) as the optimization objectives, and uses an improved multi-objective particle swarm optimization algorithm to obtain the game strategy set of DSO. …”
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