Y
Yi Jin
Researcher at University of Science and Technology of China
Publications - 38
Citations - 1071
Yi Jin is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Photovoltaic system & Maximum power principle. The author has an hindex of 14, co-authored 36 publications receiving 565 citations. Previous affiliations of Yi Jin include Zhengzhou University.
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Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review
TL;DR: A comprehensive review of the bio-inspired algorithms used for global maximum power point tracking and the modified and combined forms of these methods found to have better performance than original algorithms.
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CNN based automatic detection of photovoltaic cell defects in electroluminescence images
M. Waqar Akram,M. Waqar Akram,Guiqiang Li,Yi Jin,Xiao Chen,Changan Zhu,Xudong Zhao,Abdul Khaliq,Muhammad Faheem,Ashfaq Ahmad,Ashfaq Ahmad +10 more
TL;DR: A novel approach using light convolutional neural network architecture for recognizing defects in EL images which achieves state of the art results of 93.02% on solar cell dataset of EL images.
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Research and current status of the solar photovoltaic water pumping system – A review
TL;DR: In this article, solar photovoltaic water pumping system (SPVWPS) is proposed as a promising alternative to the conventional pumping systems and a cost-effective application especially in remote off-grid areas of developing countries.
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Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning
TL;DR: Different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies are reviewed.
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Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy
TL;DR: New face cropping and rotation strategies and simplification of the convolutional neural network (CNN) to make data more abundant and only useful facial features can be extracted and compete with existing methods in terms of training time, testing time, and recognition accuracy.