School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Summary
Gongjian Zhou was born in China, in 1979. He received the B.E., M.E., and Ph.D. degrees in information and communication engineering from Harbin Institute of Technology, Harbin, China, in 2000, 2002, and 2008, respectively. From February 2009 to March 2011, he held a Postdoctoral Fellowship at the Department of Aerospace Engineering, Harbin Institute of Technology. From April 2011 to May 2012, he was a Visiting Professor with the Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada. He is currently a Professor with the Department of Electronic Engineering, Harbin Institute of Technology. He is also a Longjiang Young Scholar. His research interests include estimation, tracking, detection, information fusion, and signal processing.
In the realm of industrial defect detection, unsupervised anomaly detection methods draw considerable attention as a result of their exceptional accomplishments. Among these, knowledge distillation-based methods have emerged as a prominent research focus, favored for their streamlined architecture, precision, and efficiency. However, the challenge of characterizing the variability in anomaly samples hinders the accuracy of detection. To address this issue, our research presents a novel approach for anomaly detection and localization, leveraging the concept of inverse knowledge distillation as its cornerstone. We employ the encoder as the guiding teacher model and designate the decoder as the... More >
Graphical Abstract
Free Access
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Research Article
| Feature Paper
| 27 May 2024
| Cited: 5
Bias estimation of sensors is an essential prerequisite for accurate data fusion. Neglect of temporal bias in general real systems prevents the existing algorithms from successful application. In this paper, both spatial and temporal biases in asynchronous multisensor systems are investigated and two novel methods for simultaneous spatiotemporal bias compensation and data fusion are presented. The general situation that the sensors sample at different times with different and varying periods is explored, and unknown time delays may exist between the time stamps and the true measurement times. Due to the time delays, the time stamp interval of the measurements from different sensors may be di... More >
Graphical Abstract
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