scholarly journals Feature Vector Extraction and Classification Performance Comparison According to Various Settings of Classifiers for Fault Detection and Classification of Induction Motor

2011 ◽  
Vol 30 (8) ◽  
pp. 446-460 ◽  
Author(s):  
Myeong-Su Kang ◽  
Thu-Ngoc Nguyen ◽  
Yong-Min Kim ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Feature Vector ◽  
Classification Performance ◽  
Performance Comparison ◽  
Fault Detection And Classification

scholarly journals Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

2021 ◽  
Vol 29 (3) ◽  
pp. 869-883
Author(s):  
Majid Hussain ◽  
Dileep Kumar Soother ◽  
Imtiaz Hussain Kalwar ◽  
Tayab Din Memon ◽  
Zubair Ahmed Memon ◽  
...  
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Stator Winding ◽  
Fault Detection And Classification ◽  
Three Phase

scholarly journals Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Moath Alrifaey ◽  
Wei Hong Lim ◽  
Chun Kit Ang ◽  
Elango Natarajan ◽  
Mahmud Iwan Solihin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Learning Model ◽  
Photovoltaic System ◽  
Fault Detection And Classification ◽  
Deep Learning Model

Deep Learning Based Tobacco Products Classification (Preprint)

2019 ◽  
Author(s):  
Murat Taşkıran ◽  
Sibel Çimen Yetiş
Keyword(s):  
Young People ◽  
Feature Vector ◽  
Weighted Average ◽  
Classification Performance ◽  
Accuracy Rate ◽  
Tobacco Products ◽  
Internet Environment ◽  
Performance Rate ◽  
Fully Connected

BACKGROUND Various images and videos are uploaded every day or even every second on Instagram. These publicly available images are easily accessible as a result of uncontrolled Internet use by young people and children. Shared images include tobacco products and can be encouraging for young people and children when they are accessible. OBJECTIVE In this study, it is aimed to detect tobacco and tobacco products with various Convolutional Neural Networks (CNNs) and to limit the access of young users to these detected tobacco products over the Internet. METHODS A total of 1607 public images were collected from Instagram, and feature vectors were extracted with various CNNs, which proved to be successful in the competitions and CNN was determined to be proper for detect tobacco products. RESULTS MobileNet gave the highest results 99.1% as weighted average. The feature vector of the input images are extracted with CNNs and classified with the latest fully connected layer. CONCLUSIONS The classification of the tobacco products of 4 different classes was studied by using the networks and the classification performance rate was obtained as 100% for 322 test images via MobileNet. In this way, the content that is encouraging for children can be censored or filtered with a high accuracy rate and a secure Internet environment can be provided.

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