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    Unsupervised Anomaly Detection for Power Batteries: A Temporal Convolution Autoencoder Framework 

    Source: Journal of Electrochemical Energy Conversion and Storage:;2024:;volume( 022 ):;issue: 001:;page 11009-1
    Author(s): Wang, Juan; Ye, Yonggang; Wu, Minghu; Zhang, Fan; Cao, Ye; Zhang, Zetao; Chen, Ming; Tang, Jing
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on a temporal convolutional ...
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    Quantitative Evaluation on the Evolution of Water Cone Behavior in a Heavy Oil Reservoir With Bottom Water 

    Source: Journal of Energy Resources Technology:;2020:;volume( 142 ):;issue: 006
    Author(s): Wang, Kai; Li, Ke; Zhou, Wensheng; Zhu, Guojin; Pan, Yue; Fan, Hongjun; Gao, Yufei; Tang, Jing; Xu, Lei; You, Qing
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In order to solve the problem of the unclear understanding of the water cone behavior and its influencing factors of horizontal well in a heavy oil reservoir with bottom water, in this paper, a series of physical models ...
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