Q
Qianru Zhang
Researcher at Southeast University
Publications - 13
Citations - 432
Qianru Zhang is an academic researcher from Southeast University. The author has contributed to research in topics: Convolutional neural network & Computer science. The author has an hindex of 4, co-authored 9 publications receiving 163 citations.
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Journal ArticleDOI
Recent advances in convolutional neural network acceleration
TL;DR: In this paper, the authors present a taxonomy of CNN acceleration methods in terms of three levels, i.e. structure level, algorithm level, and implementation level, for CNN architectures compression, algorithm optimization and hardware-based improvement.
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Recent Advances in Convolutional Neural Network Acceleration
TL;DR: This paper summarizes the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers, and proposes a taxonomy in terms of three levels, i.e. structure level, algorithm level, and implementation level, for acceleration methods.
Journal ArticleDOI
Robust deep auto-encoding Gaussian process regression for unsupervised anomaly detection
TL;DR: A novel hybrid unsupervised AD method is proposed that first integrates convolutional auto-encoder and Gaussian process regression to extract features and to remove anomalies from noisy data as well, which behaves more effectively at modeling high-dimension data and more robust to variation of the anomaly rate in dataset.
Journal ArticleDOI
Deep learning based solder joint defect detection on industrial printed circuit board X-ray images
Qianru Zhang,Meng Zhang,Chinthaka Gamanayake,Chau Yuen,Zehao Geng,Hirunima Jayasekara,Chia-wei Woo,Jenny Chen Ni Low,Xiang Liu,Yong Liang Guan +9 more
TL;DR: In this paper , four joint defect detection models based on artificial intelligence are proposed and compared, and the effectiveness of the proposed models is verified by experiments on real-world 3D X-ray dataset, which saves the specialist inspection workload greatly.
Proceedings ArticleDOI
Electricity Theft Detection Using Generative Models
TL;DR: This paper focuses on energy theft that results in customer usage pattern change in utility database and proposes an anomaly detection framework called semi-supervised generative Gaussian mixture model, which can be controlled with detection indicator thresholds to adjust the intensity of detection.