J
Jiuwen Cao
Researcher at Hangzhou Dianzi University
Publications - 185
Citations - 4340
Jiuwen Cao is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Extreme learning machine & Computer science. The author has an hindex of 29, co-authored 151 publications receiving 3029 citations. Previous affiliations of Jiuwen Cao include University of Electronic Science and Technology of China & Nanyang Technological University.
Papers
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Journal ArticleDOI
Delay-dependent condition for absolute stability of Lurie control systems with multiple time delays and nonlinearities
TL;DR: In this paper, the authors derived a delay-dependent sufficient condition for the absolute stability of the Lurie system with multiple time delays and nonlinearities, and derived the maximum upper bound of the allowable delay by solving a convex optimization problem.
Journal ArticleDOI
Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction
Xuegang Song,Feng Zhou,Alejandro F. Frangi,Jiuwen Cao,Xiaohua Xiao,Yi Lei,Tianfu Wang,Baiying Lei +7 more
TL;DR: This paper proposes three mechanisms to improve GCN, namely similarity-aware adaptive calibrated GCN (SAC-GCN), for predicting SMC and MCI, and devise a calibration mechanism to fuse functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) information into edges.
Journal ArticleDOI
Maximum Correntropy Kalman Filter With State Constraints
TL;DR: A new filter, called the MCKF with state constraints, is developed, which combines the advantages of the MCC and constrained estimation technology, and addresses the problem of state estimation under equality constraints.
Proceedings ArticleDOI
Fast online learning algorithm for landmark recognition based on BoW framework
Jiuwen Cao,Tao Chen,Jiayuan Fan +2 more
TL;DR: A fast online sequential learning framework based on the recent extreme learning machine (ELM) which can update the classifier by learning the new images one- by-one or chunk-by-chunk is developed for the landmark recognition.
Journal ArticleDOI
Urban noise recognition with convolutional neural network
Jiuwen Cao,Min Cao,Jianzhong Wang,Chun Yin,Danping Wang,Danping Wang,Pierre-Paul Vidal,Pierre-Paul Vidal +7 more
TL;DR: Experimental results show that the proposed deep neural network based urban noise recognition method generally outperforms conventional shallow structure based classifiers.