Research on Long-Term Scheduling Optimization of Water–Wind–Solar Multi-Energy Complementary System Based on DDPG by Zixing Wan, Wenwu Li, Mu He, Taotao Zhang, Shengzhe Chen, Weiwei Guan, Xiaojun Hua, Shang Zheng

“…To address the challenges of high complexity in modeling the correlation of multi-dimensional stochastic variables and the difficulty of solving long-term scheduling models in continuous action spaces in multi-energy complementary systems, this paper proposes a long-term optimization scheduling method based on Deep Deterministic Policy Gradient (DDPG). …”
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A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning by Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

“…Among these DRL algorithms, the combination of actor-critic algorithms and deep function approximators is the most widely used DRL algorithm. …”
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DAB unified ZVS control strategy with optimal current stress in full power range under TPS control by Shuai Cheng, Fusheng Wang, Zongfeng Han, Chuanqi Zhang

“…Based on three‐phase shift control, Karush–Kuhn–Tucker (KKT) algorithm is used to solve the optimal control strategy of current stress under soft switching conditions, and the interval construction method that KKT cannot solve is given. …”
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Three-dimensional localization algorithm of mobile nodes based on received signal strength indicator-angle of arrival and least-squares support-vector regression by Lieping Zhang, Huihao Peng, Jiajie He, Shenglan Zhang, Zuqiong Zhang

“…Finally, the unknown mobile nodes were localized based on least-squares support-vector regression modeling. The experimental results showed that compared with the localization algorithms without optimized ranging information or least-squares support-vector regression modeling, the algorithm proposed in this study exhibited significantly improved stability, a reduced mean localization error by more than 50%, and increased localization accuracy.…”
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Robust Optimal Control for a Microbial Batch Culture Processes: Incorporating Free Terminal Time and Sensitivity Analysis by Yang Qi, Zheng Shijin, Xie Suzhen, Feng Enmin

“…Subsequently, we develop an improved particle swarm optimization algorithm to solve this equivalent problem. …”
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A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system by Arom Choi, Kwanhyung Lee, Heejung Hyun, Kwang Joon Kim, Byungeun Ahn, Kyung Hyun Lee, Sangchul Hahn, So Yeon Choi, Ji Hoon Kim

“…The algorithm’s predictions, based solely on triage information, significantly outperformed traditional logistic regression models, with notable improvements in the area under the precision-recall curve (AUPRC). …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). …”
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Piecewise Model and Parameter Obtainment of Governor Actuator in Turbine by Jie Zhao, Li Wang, Dichen Liu, Jun Wang

“…Several testing functions are selected to compare genetic algorithm and particle swarm optimization algorithm (GA-PSO) with other basic intelligence algorithms. …”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…The Ivy algorithm is employed to optimize the hyperparameters of CEEMD-SVD. …”
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Optimal Trajectory Determination and Mission Design for Asteroid/Deep-Space Exploration via Multibody Gravity Assist Maneuvers by Sean Fritz, Kamran Turkoglu

“…This paper discusses the creation of a genetic algorithm to locate and optimize interplanetary trajectories using gravity assist maneuvers to improve fuel efficiency of the mission. …”
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Diagnosis Method for High-Speed Train Axle Box Bearing Slight Faults Based on Improved SAE and Temperature-Vibration Fusion by XU Xiao, SONG Dongli, WANG Zifan

“…［Method］ First, an AE (auto encoder) driven bearing temperature feature extraction method is designed to obtain the abnormal bearing temperature features, and EMD (empirical modal decomposition) method is used to process the vibration signal，so as to obtain the statistical features of the effective vibration IMF (intrinsic modal function). Then, by optimizing the dimensionality reduction algorithm based on SAE (stacked auto encoder), an effective fusion method of temperature-vibration features is proposed to achieve nonlinear fusion and dimensionality reduction of temperature and vibration features. …”
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Intelligent Energy-Efficient Train Trajectory Optimization Approach Based on Supervised Reinforcement Learning for Urban Rail Transits by Guannan Li, Siu Wing Or, Ka Wing Chan

“…Second, an IETTO model is established to handle uncertain disturbances in real-time train operation and generate optimal energy-efficient train trajectories online by optimizing energy efficiency and receiving supervisory information from trajectories of pre-trained TTO models. …”
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Enhanced PID for pedal vehicle force control using hybrid spiral sine-cosine optimization and experimental validation by Azrul Azim Abdullah Hashim, Nor Maniha Abdul Ghani, Mohammad Osman Tokhi

“…This study develops and validates a force feedback control system for automotive pedals utilizing an optimized PID controller using the hybrid Spiral Sine-Cosine algorithm (SSCA). …”
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Online Tool Wear Monitoring via Long Short-Term Memory (LSTM) Improved Particle Filtering and Gaussian Process Regression by Hui Xu, Hui Xie, Guangxian Li

“…To address these challenges, this paper presents an innovative tool wear prediction method that integrates a nonlinear mean function and a multi-kernel function-optimized GPR model combined with an LSTM-enhanced particle filter algorithm. …”
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Optimal protection coordination for directional overcurrent relays in radial distribution networks with inverter-based distributed energy resources by Jie Li, Jingtong Feng, Min Huang, Shenquan Liu, Gang Wang

“…The Pareto-front of the proposed optimal protection coordination problem is obtained by the improved multi-objective particle swarm optimization, where a time–space section evaluation utilizes the K-means clustering method to improve the computational efficiency. …”
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Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion by Li Wan, Wenzhi Cheng, Jie Yang

“…The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. …”
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Tone mapping based on variational model in gradient domain by IZhi-hong X, Lan-fei ZHAO, Chi ZHANG, Zhong-min ZHANG

“…Due to the low dynamic range image which was generated by the gradient domain high dynamic range compression algorithm contain the artificial boundaries and local detail distortions,a variational model in gradient domain was proposed to improve the performance of the traditional algorithm.First of all,a variational model in gradient domain was introduced to compress dynamic range,meanwhile details and edges were preserved simultaneously.Afterwards,the rate of convergence was improved by introducing the ideology of Gibbs sampler.Eventually,the improved method was employed to obtain the optimal solution of the variational model.Experimental results demonstrate that proposed algorithm reduces the degree of artificial boundaries,meanwhile low dynamic range image represents excellent capacity of detail preservation.Moreover,the real-time performance is guaranteed by the improved steepest descent method.…”
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The Optimal Dispatch for a Flexible Distribution Network Equipped with Mobile Energy Storage Systems and Soft Open Points by Yu Ji, Ying Zhang, Lei Chen, Juan Zuo, Wenbo Wang, Chongxin Xu

“…In this paper, a case study uses a regional road network in Chengdu coupled with an IEEE 33-node standard grid, and the model is solved using the non-dominated sorting genetic algorithm III (NSGA-III) algorithm. …”
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Tone mapping based on variational model in gradient domain by IZhi-hong X, Lan-fei ZHAO, Chi ZHANG, Zhong-min ZHANG

“…Due to the low dynamic range image which was generated by the gradient domain high dynamic range compression algorithm contain the artificial boundaries and local detail distortions,a variational model in gradient domain was proposed to improve the performance of the traditional algorithm.First of all,a variational model in gradient domain was introduced to compress dynamic range,meanwhile details and edges were preserved simultaneously.Afterwards,the rate of convergence was improved by introducing the ideology of Gibbs sampler.Eventually,the improved method was employed to obtain the optimal solution of the variational model.Experimental results demonstrate that proposed algorithm reduces the degree of artificial boundaries,meanwhile low dynamic range image represents excellent capacity of detail preservation.Moreover,the real-time performance is guaranteed by the improved steepest descent method.…”
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Optimizing Energy Efficiency in Vehicular Edge-Cloud Networks Through Deep Reinforcement Learning-Based Computation Offloading by Ibrahim A. Elgendy, Ammar Muthanna, Abdullah Alshahrani, Dina S. M. Hassan, Reem Alkanhel, Mohamed Elkawkagy

“…Given the complexity of this problem in large-scale vehicular networks, the study formulates an equivalent reinforcement learning model and employs a deep learning algorithm to derive optimal solutions. …”
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