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    A Multi-Scale Time–Frequency Complementary Load Forecasting Method for Integrated Energy Systems by Enci Jiang, Ziyi Wang, Shanshan Jiang

    Published 2025-06-01
    “…Robust in long-step (96 h) forecasting, it reduces MAE by 30% compared to conventional methods, offering an efficient tool for real-time IES scheduling and risk decision-making.…”
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  3. 1283

    Towards Intelligent Unmanned Adversarial Games: A Reinforcement Learning Framework with the PHP-ROW Method by Guoqing Shi, Yi Cao, Dinghan Wang, Qiming Yang, Jiandong Zhang, Zhuoyong Shi

    Published 2025-04-01
    “…This study introduces a novel framework for intelligent unmanned BVR maneuver control within the context of adversarial games. The emphasis lies on three pivotal aspects: situational awareness, maneuver decision-making, and precise maneuver control. …”
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  4. 1284

    An Approach to Truck Driving Risk Identification: A Machine Learning Method Based on Optuna Optimization by Zhaofei Wang, Hao Li, Qiuping Wang

    Published 2025-01-01
    “…First, the risk characterization indicators were extracted from the natural driving data of trucks, and the threshold value of each indicator was determined using a box plot-based method. Second, the truck driving risk was quantified into three categories of low level, medium level, and high level risk, and the unbalanced data were processed using a hybrid sampling algorithm. …”
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  5. 1285

    A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding by Fulin Wei, Xueyuan Xu, Tianyuan Jia, Daoqiang Zhang, Xia Wu

    Published 2023-01-01
    “…Different from previous MSTL methods in MI, our methods align the data distribution for each pair of subjects, and then integrate the results by decision fusion. …”
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  6. 1286

    Anomaly Detection Method for Hydropower Units Based on KSQDC-ADEAD Under Complex Operating Conditions by Tongqiang Yi, Xiaowu Zhao, Yongjie Shi, Xiangnan Jing, Wenyang Lei, Jiang Guo, Yang Meng, Zhengyu Zhang

    Published 2025-06-01
    “…Validation experiments using 14-month actual operational data from a 550 MW unit at a hydropower station in Southwest China show that the KSQDC algorithm achieves a silhouette coefficient of 0.64 in condition recognition, significantly outperforming traditional methods, and the KSQDC-ADEAD algorithm achieves comprehensive scores of 0.30, 0.34, and 0.23 for anomaly detection at three key monitoring points, effectively improving the accuracy and reliability of anomaly detection. …”
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    Nomogram for Predicting 1‐, 3‐, and 5‐Year Survival in Hemodynamically Significant Aortic Regurgitation: The ARISE Score by Kang Liu, Meng‐Han Tsai, Wei‐Jyun Wang, Jui Wang, Seanson Chance Ju, Masashi Amano, Chisato Izumi, Yi‐Lwun Ho, Masaaki Takeuchi, Li‐Tan Yang

    Published 2025-05-01
    “…We aimed to develop and validate a nomogram‐based score to predict survival and identify high‐risk patients for timely aortic valve surgery referral. Methods and Results From 2008 to 2022, 1229 patients (derivation data set: 764 Taiwanese; validation data set: 465 Japanese; age: 64±17 years) with isolated chronic moderately severe to severe aortic regurgitation from 3 centers were included. …”
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    Sleep Stage Classification using Laplacian Score Feature Selection Method by Single Channel EEG by Mahtab Vaezi, Mehdi Nasri

    Published 2024-02-01
    “…Simulation results confirms the superiority of the proposed method based on the classification results. With the proposed algorithm, 2, 3, 4, 5 and 6 stages of sleep were classified by SVM and decision tree with 98.0%, 98.0%, 97.3%, 96.6%, and 95.0% accuracy, which are more superior to previous methodâs results.…”
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