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An image Denoising Method Based on Deep Residual GAN

Journal of Physics: Conference Series 2020年第3期1550卷

作者： Ziyuan Wang Lidan Wang Shukai Duan Yunfei Li Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing 400715 China Schoolof Electronic and information engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing 400715 China Braininspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects.

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Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

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Journal of Physics: Conference Series 2019年第1期1229卷

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education China

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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Simulating evolutional symport/antiport by evolution-communication and vice versa in tissue p systems with parallel communication 18th

Simulating evolutional symport/antiport by evolution-communi...

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18th International Conference on Membrane Computing, CMC 1018

作者： Adorna, Henry Alhazov, Artiom Pan, Linqiang Song, Bosheng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China University of the Philippines Diliman Quezon City1101 Philippines Institute of Mathematics and Computer Science Academy of Science of Moldova Academiei 5 Chişinău2028 Moldova School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

We aim to compare functionality of symport/antiport with embedded rewriting to that of symport/antiport accompanied by rewriting, by two-way simulation, in case of tissue P systems with parallel communication. A simul... 详细信息

ISBN: (纸本)9783319733586

关键词： Tissue

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Interdependent order allocation in the two-echelon supply chain using agent-based negotiation 36

Interdependent order allocation in the two-echelon supply ch...

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第36届中国控制会议

作者： Jian Wang Huimin Miao System Engineering Institute School of AutomationHuazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan University of Science and Technology

ISBN: (纸本)9781538629185

Nowadays,many researches study the negotiation-based order allocation problem in the supply chain environment,aiming to improve the efficiency of the allocation and the profits of the supply chain *** competition and cooperation in supplier’s side are mainly addressed because of independent *** this paper,we analyze an interdependent order allocation problem in a two-echelon supply *** supply chain consists of a manufacturer echelon and a supplier *** to the interdependence,both the competition and cooperation in the manufacturer echelon are *** agent-based negotiation algorithm is developed to support the order allocation process and the conflicts *** show in experiments that orders under various supply chain contexts can be successfully allocated through the algorithm.

关键词： Supply chain system order allocation agent-based negotiation

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SPATIALLY ATTENTIVE CORRELATION FILTERS FOR VISUAL TRACKING

SPATIALLY ATTENTIVE CORRELATION FILTERS FOR VISUAL TRACKING

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IEEE International Conference on image processing

作者： Huai Qin Zhixiong Pi Changqian Yu Changxin Gao Jin-Gang Yu Nong Sang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Huazhong University of Science and Technology Wuhan Hubei CN South China University of Technology Guangzhou China

Although correlation filter based trackers have recently demonstrated excellent performance, they still suffer from the boundary effects. The cosine window is introduced to alleviate the boundary affects, which however may result in poor performance in case of occlusion or fast motion. To address this problem, we propose a simple yet effective framework, which builds a spatially attentive model with multiple features to guide the detection of the correlation filter based trackers. The proposed method not only can breakthrough the spatial extent of cosine window, but also can provides prior information about the target object. Moreover, to model a robust object prior, we propose a generic strategy for adaptive fusion and update of multiple features. Extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our methods compared to the state-of-the-art trackers.

关键词： Target tracking Correlation Adaptation models Color Training Mathematical model

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Varying control intensity of synchronized chaotic system with time delay

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Babatunde O. Onasanya Shiping Wen Yuming Feng Wei Zhang Ning Tang Adeleke T. Ademola Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing 404100 China Department of Mathematics University of Ibadan Ibadan Nigeria Australian AI Institute University of Technology Sydney Ultimo NSW 2007 Australia School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing 404100 China Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing 404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing 404100 China Department of Mathematics Obafemi Awolowo University Post Code 220005 Ile-Ife Nigeria

Hyperchaotic system is a very useful tool in secure and encrypted communications. But situations arise when engineers and scientists seek to synchronize two hyperchaotic systems. This gives another (error) system. The goal is to minimize the error as much as can be in order to make one system look like the other by synchronization. This is a particularly challenging situation. In this paper, two hyperchaotic systems are synchronized by impulsive control. Also, the condition for uniform asymptotic stability of the synchronized error system was given. Finally, the simulation results to justify the reliability of this method is also presented.

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A double-integrator system for distributed optimization of convex cost functions 29

A double-integrator system for distributed optimization of c...

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第29届中国控制与决策会议

作者： Ngoc-Tu Tran Xuan Feng Wen-Yuan Xu Na Zhan Xi Wang School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control(Huazhong University of Science and Technology) Ministry of Education Hubei Electric Power Company

ISBN: (纸本)9781509046584

In this paper,the distributed optimization problem is investigated under a second-order multi-agent *** the proposed algorithm,each agent solves the optimization via local computation and information exchange with its neighbors through the communication ***,in comparison with the existing second-order distributed optimization algorithms,the proposed algorithm is much simpler due to one coupled information exchange among the agents is *** achieve the optimization,the distributed algorithm is proposed based on the consensus method and the gradient *** optimal solution of the problem is thus obtained with the design of Lyapunov function and the help of LaSallel's Invariance Principle.A numerical simulation example and comparison of proposed algorithm with existing works are presented to illustrate the effectiveness of the theoretical result.

关键词： Second-order multi-agent systems Distributed optimization Consensus Lyapunov function Gradient algorithm

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Region stability analysis and tracking control of memristive recurrent neural network

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Neural networks : the official journal of the International Neural Network Society 2018年 98卷 51-58页

作者： Gang Bao Zhigang Zeng Yanjun Shen Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control Electrical Engineering & New Energy China Three Gorges University Yichang 443002 China. Electronic address: ctgugangbao@***. China School of Automation Huazhong University of Science and Technology Wuhan Hubei 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng@***. Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control Electrical Engineering & New Energy China Three Gorges University Yichang 443002 China. Electronic address: shenyj@***.

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result.

关键词： Memristive neural network Memristor Stability

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Protecting privacy of users in brain-computer interface applications

arXiv

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arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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A FAST RADIO BURST DISCOVERED IN FAST DRIFT SCAN SURVEY

arXiv

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arXiv 2020年

作者： Zhu, Weiwei Li, Di Luo, Rui Miao, Chenchen Zhang, Bing Spitler, Laura Lorimer, Duncan Kramer, Michael Champion, David Yue, Youling Cameron, Andrew Cruces, Marilyn Duan, Ran Feng, Yi Han, Jun Hobbs, George Niu, Chenhui Niu, Jiarui Pan, Zhichen Qian, Lei Shi, Dai Tang, Ningyu Wang, Pei Wang, Hongfeng Yuan, Mao Zhang, Lei Zhang, Xinxin Cao, Shuyun Feng, Li Gan, Hengqian Gao, Long Gu, Xuedong Guo, Minglei Hao, Qiaoli Huang, Lin Huang, Menglin Jiang, Peng Jin, Chengjin Li, Hui Li, Qi Li, Qisheng Liu, Hongfei Pan, Gaofeng Peng, Bo Qian, Hui Shi, Xiangwei Song, Jinyuo Song, Liqiang Sun, Caihong Sun, Jinghai Wang, Hong Wang, Qiming Wang, Yi Xie, Xiaoyao Yan, Jun Yang, Li Yang, Shimo Yao, Rui Yu, Dongjun Yu, Jinglong Zhang, Chengmin Zhang, Haiyan Zhang, Shuxin Zheng, Xiaonian Zhou, Aiying Zhu, Boqin Zhu, Lichun Zhu, Ming Zhu, Wenbai Zhu, Yan CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia Department of Physics and Astronomy University of Nevada Las Vegas Las VegasNV89154 United States National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Max-Planck-Institut fr Radioastronomie Auf dem Hgel 69 BonnD-53121 Germany Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology Chestnut Ridge Research Building MorgantownWV26505 United States Image Processing and Pattern Recognition Laboratory School of Artificial Intelligence Beijing Normal University Beijing100875 China School of Information Management Dezhou University Dezhou253023 China Institute for Astronomical Science Dezhou University Dezhou253023 China

We report the discovery of a highly dispersed fast radio burst, FRB 181123, from an analysis of ∼1500 hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0–1.5 GHz observing band. We measure the peak flux density to be > 0.065 Jy and the corresponding fluence > 0.2 Jy ms. Based on the observed dispersion measure of 1812 cm−3 pc, we infer a redshift of ∼ 1.9. From this, we estimate the peak luminosity and isotropic energy to be . 2 × 1043 erg s−1 and . 2 × 1040 erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences > 0.025 Jy ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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