检索结果-内蒙古大学图书馆

40th Chinese Control Conference, CCC 2021

作者： Xie, Yuan Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing School of Automation Guangzhou510006 China Ministry of Education of the P.R.C. Guangdong Key Laboratory of IoT Information Processing Jt. Intl. Research Laboratory of Intelligent Information Processing and System Integration of IoT Guangzhou510006 China Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Guangzhou510006 China

ISBN: (纸本)9789881563804

Separation of heart sound signals from the recorded mixtures has become a hot research topic in the clinical diagnosis of heart diseases. To better understand the current health status of patients, it is of great significance to develop intelligent auscultation methods to improve the effectiveness of hearing and assist clinicians in practice. In this paper, an optimization convolutive blind source separation algorithm utilizing independent vector analysis is proposed for separation of the heart sound mixtures. In the algorithm, a denoising approach is firstly used to reduce the impact of the additive white Gaussian noise. Then, using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. Afterwards, the unmixng matrix is updated using Newton's method, and the heart sound sources are reconstructed based on the estimated unmixing matrix. Experimental results show that the algorithm obtains better separation performance than the baseline methods. Especially, it has better superiority in the strong reverberant environment. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Time domain analysis

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Multi-view data classification with a label-driven auto-weighted strategy

arXiv

引用

arXiv 2022年

作者： Yu, Yuyuan Zhou, Guoxu Huang, Haonan Xie, Shengli Zhao, Qibin School Of Automation Guangdong University Of Technology Guangzhou510006 China Key Laboratory Of Intelligent Detection And The Internet Of Things In Manufacturing Ministry Of Education Guangzhou510006 China Jt. Intl. Research Laboratory Of Intelligent Information Processing And System Integration Of IoT Ministry Of Education Guangdong University Of Technology Guangzhou510006 China Guangdong-Hong Kong-Macao Joint Laboratory For Smart Discrete Manufacturing Guangdong University Of Technology Guangzhou510006 China RIKEN Tokyo103-0027 Japan

Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by low-quality views then leading to poor performance. In this paper, by establishing a link between labeled data and the importance of different views, we propose an auto-weighted strategy to evaluate the importance of views from a label perspective to avoid the negative impact of unimportant or low-quality views. Based on this strategy, we propose a transductive semi-supervised auto-weighted multi-view classification model. The initialization of the proposed model can be effectively determined by labeled data, which is practical. The model is decoupled into three small-scale sub-problems that can efficiently be optimized with a local convergence guarantee. The experimental results on classification tasks show that the proposed method achieves optimal or sub-optimal classification accuracy at the lowest computational cost compared to other related methods, and the weight change experiments show that our proposed strategy can distinguish view importance more accurately than other related strategies on multi-view datasets with low-quality views. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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