首页|New Machine Learning Findings from University of Tennessee Described (Practical Methods of Defective Input Feature Correction To Enable Machine Learning In Power Systems)
New Machine Learning Findings from University of Tennessee Described (Practical Methods of Defective Input Feature Correction To Enable Machine Learning In Power Systems)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Researchers detail new data in Machine Learning. According to news originating from Knoxville, Tennessee, by NewsRx correspondents, research stated, “In this research work, three practical correction methods are proposed to mitigate the impact of defective input features in power system data measurement for machine learning (ML) applications. A well-trained ML tool may become ineffective due to defective input features, which may originate from measurement issues, such as monitor malfunction, cyberattack, communication failure, or others.” Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of Tennessee, “It is crucial to correct defective input features to enable ML tools with desirable performances. This letter first introduces the mechanism of three correction methods, i.e., statistical-value-based method, minimalerror-based method, and DNN-based adaptive method. Then, the methods are validated via a deep neural network (DNN) case for power system stability enhancement. Validation results demonstrate that the adaptive method achieves the best performance, enabling the well-trained ML tool with a similar accuracy level to the case of no data defects.
KnoxvilleTennesseeUnited StatesNorth and Central AmericaCybersecurityCyborgsEmerging TechnologiesMachine LearningUniversity of Tennessee
NETL
NSTL
