Corona fault detection in switchgear with extreme learning machine
نویسندگان
چکیده
منابع مشابه
Efficient smile detection by Extreme Learning Machine
Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning M...
متن کاملTest Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine
This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation alg...
متن کاملFault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods
Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...
متن کاملVehicle detection in driving simulation using extreme learning machine
Automatically driving based on computer vision has attracted more and more attentions from both research and industrial fields. It has two main challenges, high road and vehicle detection accuracy and real-time performance. To study the two problems, we developed a driving simulation platform in a virtual scene. In this paper, as the first step of final solution, the Extreme Learning Machine (E...
متن کاملExtreme Learning Machine
Slow speed of feedforward neural networks has been hampering their growth for past decades. Unlike traditional algorithms extreme learning machine (ELM) [5][6] for single hidden layer feedforward network (SLFN) chooses input weight and hidden biases randomly and determines the output weight through linear algebraic manipulations. We propose ELM as an auto associative neural network (AANN) and i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bulletin of Electrical Engineering and Informatics
سال: 2020
ISSN: 2302-9285,2089-3191
DOI: 10.11591/eei.v9i2.2058