Research on Location Planning of Battery Swap Stations for Operating Electric Vehicles by Pengcheng Ma, Shuai Zhang, Bin Zhou, Wenqi Shao, Haowen Li, Tengfei Ma, Dong Guo

“…The results show that the MLP-NSGA-II joint algorithm has the lowest cost of optimizing the location of switching stations. …”
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A study on multi-objective optimization for the location selection of smart underground parking facilities in high-density urban areas of megacities: A case study of Jing'an distri... by Xiaodan Li, Yunci Guo, Zhen Liu, Dandan Sun, Yidi Liu, Wencan Wang

“…Based on real-world data, a multi-objective optimization model is constructed considering convenience, coverage, and cost-efficiency. …”
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Transmission and Generation Expansion Planning Considering Virtual Power Lines/Plants, Distributed Energy Injection and Demand Response Flexibility from TSO-DSO Interface by Flávio Arthur Leal Ferreira, Clodomiro Unsihuay-Vila, Rafael A. Núñez-Rodríguez

“…The results obtained demonstrate a reduction in total costs, an improvement in the efficient use of the transmission system and an improvement in the locational marginal price indicator of the transmission system.…”
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Design of an Efficient Deep Learning-Based Diagnostic Model for Wind Turbine Gearboxes Using SCADA Data by Xuan-Kien Mai, Jun-Yeop Lee, Jae-In Lee, Byeong-Soo Go, Seok-Ju Lee, Minh-Chau Dinh

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Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning by Shupei Zhang, Huapeng Shi, Wei Zhang, Ying Pang, Pengju Sun

“…Moreover, traditional optimization algorithms make it difficult to achieve timeliness in real-time changing traffic conditions. …”
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Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming:... by Pattanun Chanpiwat, Fabricio Oliveira, Steven A. Gabriel

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Time-Variation Damping Dynamic Modeling and Updating for Cantilever Beams with Double Clearance Based on Experimental Identification by Yunhe Zhang, Fanjun Meng, Xueguang Li, Wei Song, Dashun Zhang, Faping Zhang

“…The quantum genetic algorithm (QGA) is used to optimize the scale factor, which determines the identification accuracy and calculation efficiency. …”
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Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images by Charles Ekene Chika

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Multiphysics Feature-Based State-of-Energy Estimation for LiFePO4 Batteries Using Bidirectional Long Short-Term Memory and Particle Swarm-Optimized Kalman Filter by Zhengpu Wu, Xu He, Haisen Chen, Lu Lv, Jiuchun Jiang, Lujun Wang

“…A fusion model that integrates a bidirectional long short-term memory (BiLSTM) network, particle swarm optimization (PSO), and Kalman filter (KF) algorithm is proposed for SOE estimation. …”
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An Enhanced Generative Adversarial Network Prediction Model Based on LSTM and Attention for Corrosion Rate in Pipelines by Pujun Long, Mi Liang, Hongjian Chen, Qin Yang

“…This model integrates an improved Generative Adversarial Network with Grey Wolf Optimization and Support Vector Regression (LAGAN-GWO-SVR). …”
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Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game by Xiu Ji, Mingge Li, Zheyu Yue, Haifeng Zhang, Yizhu Wang

“…For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. …”
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An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…The stacking ensemble learning framework comprises four base-models and a meta-model, and model hyperparameters are re-optimized using the particle swarm optimization algorithm. …”
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An efficient eigenvector-based crossover for differential evolution: Simplifying with rank-one updates by Tae Jong Choi

“…This represents a substantial improvement in achieving rotational invariance in the DE algorithm.…”
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Modeling of cross line operation of urban rail transit trains based on passenger travel time by LUO Qin, LIN Bin, GU Mengqi, YANG Liang, ZHANG Xiaochun

“…An optimization model of cross line operation organization is established with the objective function of minimizing the total travel time cost of passengers. …”
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Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Po... by Zhuoyu Jiang, Rujie Liu, Weiwei Guan, Lei Xiong, Changli Shi, Jingyuan Yin

“…Compared with traditional optimization algorithms, it can effectively improve the ability to search for the optimal solution. …”
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A Diagnostic and Performance System for Soccer: Technical Design and Development by Alberto Gascón, Álvaro Marco, David Buldain, Javier Alfaro-Santafé, Jose Victor Alfaro-Santafé, Antonio Gómez-Bernal, Roberto Casas

“…Results indicate high accuracy rates for detecting ball-striking events and CoDs, with improvements in algorithm performance achieved through adaptive thresholds and ensemble neural network models. …”
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The integration of artificial intelligence in assisted reproduction: a comprehensive review by Pragati Kakkar, Shruti Gupta, Kasmiria Ioanna Paschopoulou, Ilias Paschopoulos, Ioannis Paschopoulos, Vassiliki Siafaka, Orestis Tsonis

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A study on life assessment of metro cowcatcher based on multi-axis vibration environment reconstruction by ZHENG Yuhao, WU Xingwen, LIU Yang, CHI Maoru, LIANG Shulin

“…Then, the modal damping ratio was optimized by the genetic algorithm, improving the consistency between the simulation and measured response results, and proving the correctness of the modeling method. …”
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Seasonally Adaptive VMD-SSA-LSTM: A Hybrid Deep Learning Framework for High-Accuracy District Heating Load Forecasting by Yu Zhang, Keyong Hu, Lei Lu, Qingqing Yang, Min Fang

“…Subsequently, the SSA is utilized to optimize the hyperparameters of the LSTM network, with targeted adjustments made according to the seasonal characteristics of the heating load, enabling the identification of optimal configurations for each season. …”
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State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models by Zuriani Mustaffa, Mohd Herwan Sulaiman, Jeremiah Isuwa

“…This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks. Specifically, teaching-learning based optimization (TLBO) is employed to optimize the weights and biases of the deep neural networks model, enhancing estimation accuracy. …”
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