首页|New Support Vector Machines Study Findings Have Been Reported from Bhubaneswar ( Feature Extraction and Classification of Power Quality Disturbances Using Optimi zed Tunable-Q Wavelet Transform and Incremental Support Vector Machine)
New Support Vector Machines Study Findings Have Been Reported from Bhubaneswar ( Feature Extraction and Classification of Power Quality Disturbances Using Optimi zed Tunable-Q Wavelet Transform and Incremental Support Vector Machine)
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
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - A new study on is now available. Accor ding to news reporting out of Bhubaneswar, India, by NewsRx editors, research st ated, “The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effect ive monitoring, detection, and classification of power quality disturbances (PQD s) that is needed to take remedial measures and design planning of the system ar chitecture.”
Department of Computer Science and Engin eeringBhubaneswarIndiaAsiaEmerging TechnologiesMachine LearningSuppo rt Vector MachinesVector Machines
NETL
NSTL
