摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-调查人员讨论工程学的新发现。根据NewsR X Corporters从瑞典Umea发回的新闻报道，研究表明：“本文研究了基于机器学习(ML)的电压暂降源定位(VSSL)在电力系统中的应用，为克服传统机器学习方法在特征选择方面的挑战，为深度学习方法提供更有意义的序列特征，提出了三种基于时间样本的特征形式，即基于时间样本的特征形式，即基于时间样本的特征形式，基于时间样本的并评估现有的G特征形式。这项研究的财政支持者包括Kempe基金会（Kempestiftelser NA）、瑞典、ARIS、斯洛文尼亚。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Engineering. According to news reporting originating from Umea, Sweden, by NewsR x correspondents, research stated, “The paper investigates the application of ma chine learning (ML) for voltage sag source localization (VSSL) in electrical pow er systems. To overcome feature -selection challenges for traditional ML methods and provide more meaningful sequential features for deep learning methods, the paper proposes three time -sample -based feature forms, and evaluates an existin g feature form.” Financial supporters for this research include Kempe Foundation (Kempestiftelser na), Sweden, ARIS, Slovenia.
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