Power Load Data Completion Method Based on Integrated Graph Convolutional Variational Transformer by YAN Li, HU Hailin, SHI Lei, WU Qinzheng, LÜ Tianguang, XU Yingdong, ZHANG Wenbin, WANG Gaozhou

“…The raw data are processed by the GCN to learn spatial features and deeply mine spatial dependencies； the hidden layer data are reconstructed by the VAE to more effectively restore data distribution characteristics； and the temporal autocorrelation information of the sequence is mined based on the Transformer model. In addition， an improved whale optimization algorithm （WOA） is introduced to optimize the network model hyperparameters and improve the completion accuracy and applicability of the model. …”
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Multi-Objective Vibration Control of a Vehicle-Track-Bridge Coupled System Using Tuned Inerter Dampers Based on the FE-SEA Hybrid Method by Xingxing Hu, Qingsong Feng, Min Yang, Jian Liu

“…Using the vibration acceleration amplitudes of both the rail and track slab as dual control objectives, a multi-objective optimization model is established, and the TID’s optimal parameters are determined using a multi-objective genetic algorithm. …”
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CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance by Shuang WANG, Qian LUO, Bo TANG, Lan JIANG, Jin LI

“…Then, the Tent chaotic map embedded chaotic hybrid pelican optimization algorithm (CHPOA) is developed to optimize the number of hidden layer nodes and reverse fine-tuning learning rate of DBN, and the CHPOA-DBN transformer fault diagnosis model is constructed. …”
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A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning by Yuzhu Yang, Hongda Li, Miao Sun, Xingyu Liu, Liying Cao

“…Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). …”
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The development of an intelligent comprehensive detection instrument for circuit breakers in power systems and its key technologies by Weimin Guan, Han Hu, Chao Sun, Jie Ji

“…Additionally, this study optimizes the fault diagnosis algorithm, enhancing detection stability and robustness. …”
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Small Scale Multi-Object Segmentation in Mid-Infrared Image Using the Image Timing Features–Gaussian Mixture Model and Convolutional-UNet by Meng Lv, Haoting Liu, Mengmeng Wang, Dongyang Wang, Haiguang Li, Xiaofei Lu, Zhenhui Guo, Qing Li

“…The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. …”
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A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study by Rui-Tao Sun, Chang-Lei Li, Yu-Min Jiang, Ao-Yun Hao, Kui Liu, Kun Li, Bin Tan, Xiao-Nan Yang, Jiu-Fa Cui, Wen-Ye Bai, Wei-Yu Hu, Jing-Yu Cao, Chao Qu

“…A combination of radiomic and clinical features was selected using the Boruta-LASSO algorithm. Predictive models were constructed using six machine learning algorithms and validated, with model performance evaluated based on the AUC, accuracy, Brier score, and DCA to identify the optimal model. …”
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Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP by Shuang Liu, Enxiang Qu, Chun LV, Xueyuan Zhang

“…Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.…”
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Characteristics and prediction methods of coal spontaneous combustion for deep coal mining in the Ximeng mining area by Li MA, Wenbo GAO, Longlong TUO, Pengyu ZHANG, Zhou ZHENG, Ruizhi GUO

“…Then, the hyperparameters of the random forest (RF) model were optimized using the crested porcupine optimizer (CPO) algorithm. …”
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Training multi-layer binary neural networks with random local binary error signals by Luca Colombo, Fabrizio Pittorino, Manuel Roveri

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Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…In order to improve the performance of the machine learning models, hyperparameter tuning was performed by Grid Search Optimization method. …”
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Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique by Lijie Jiang, Qi Li, Huiqing Liao, Hourong Liu, Bowen Tan

“…Abstract This study develops and evaluates advanced hybrid machine learning models—ADA-ARD (AdaBoost on ARD Regression), ADA-BRR (AdaBoost on Bayesian Ridge Regression), and ADA-GPR (AdaBoost on Gaussian Process Regression)—optimized via the Black Widow Optimization Algorithm (BWOA) to predict the density of supercritical carbon dioxide (SC-CO2) and the solubility of niflumic acid, critical for pharmaceutical processes. …”
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Multi-Scale Spatiotemporal Feature Enhancement and Recursive Motion Compensation for Satellite Video Geographic Registration by Yu Geng, Jingguo Lv, Shuwei Huang, Boyu Wang

“…Based on the SuperGlue matching algorithm, the method achieves automatic matching of inter-frame image points by introducing the multi-scale dilated attention (MSDA) to enhance the feature extraction and adopting a joint multi-frame optimization strategy (MFMO), designing a recursive motion compensation model (RMCM) to eliminate the cumulative effect of the orbit error and improve the accuracy of the inter-frame image point matching, and using a rational function model to establish the geometrical mapping between the video and the ground points to realize the georeferencing of satellite video. …”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Adaptive Temporal Reinforcement Learning for Mapping Complex Maritime Environmental State Spaces in Autonomous Ship Navigation by Ruolan Zhang, Xinyu Qin, Mingyang Pan, Shaoxi Li, Helong Shen

“…The model integrates an enhanced Proximal Policy Optimization (PPO) algorithm for efficient policy iteration optimization. …”
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Development of a Weighted Average Ensemble Model for Predicting Officially Assessed Land Prices Using Grid Map Data and SHAP by Surin Im, Kangmin Kim, Geunhee Lee, Hoi-Jeong Lim

“…The model analyzes the impact of key variables through SHAP for improved interpretability. …”
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Multi-Skilled Project Scheduling for High-End Equipment Development Considering Newcomer Cultivation and Duration Uncertainty by Yaohui Liu, Ronggui Ding, Shanshan Liu, Lei Wang

“…Therefore, we put forward an adaptive simulation–optimization approach featuring two-fold: a simulation module capable of dynamically adjusting sample sizes based on convergence feedback and evaluating solutions with improved efficiency and stable accuracy; a tailored non-dominated sorting genetic algorithm II (NSGA-II) with adaptive evolutionary operators that enhance search effectiveness and ensure the identification of a well-distributed Pareto front. …”
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Three-Dimensional Shape Reconstruction from Digital Freehand Design Sketching Based on Deep Learning Techniques by Ding Zhou, Guohua Wei, Xiaojun Yuan

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Research on Dynamic Performance of Autonomous-rail Rapid Tram by ZHONG Hanwen, LI Xiaoguang, XIAO Lei, YANG Yong, ZHANG Chenlin, HUANG Ruipeng, YUAN Xiwen

“…Through detailed Simpack dynamic model, the simulation research was carried out to provide guidance for optimization and improvement of vehicle dynamic performance. …”
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