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Reaching Non-Negative Edge Consensus of Networked Dynamical Systems

IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2712-2722页

作者： Wang, Xiao Ling Su, Housheng Chen, Michael Z. Q. Wang, Xiao Fan Chen, Guanrong College of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Automation Nanjing University of Science and Technology Nanjing China Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic Engineering City University of Hong Kong Hong Kong

In this paper, the problem of non-negative edge consensus of undirected networked linear time-invariant systems is addressed by associating each edge of the network with a state variable, for which a distributed algorithm is constructed. Sufficient conditions referring only to the number of edges are derived for non-negative edge consensus of the networked systems. Subsequently, the linear programming method and a low-gain feedback technique are introduced to simplify the design of the feedback gain matrix for achieving the non-negative edge consensus. It is found that the low-gain feedback technique has a good effect on the non-negative edge consensus of the networked systems subject to input saturation. Numerical simulations are presented to verify the effectiveness of the theoretical results.

关键词： Distributed algorithm line graph low-gain feedback networked system non-negative edge consensus

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Automatic Monitoring of Driver's Physiological Parameters Based on Microarray Camera

Automatic Monitoring of Driver's Physiological Parameters Ba...

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IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Jiancheng Zou Zhengzheng Li Peizhou Yan Institute of Image Processing and Pattern Recognition North China University of Technology Beijing Key Laboratory of Urban Rod Traffic Intelligent Control Technology North China University of Technology Shijingshan District Beijing China

Driver's physical and mental states are very important factors affecting the driving states. Traffic accidents are occurred by accompanying abnormal physiological parameters. So how to monitor automatically driver's physiological parameters is an important problem. A real-time driver's physiological parameters monitoring method based on microarray camera is proposed in this paper. Firstly, we use a microarray camera to acquire videos in real time without contact. Secondly, the videos are registered and fused into a high quality video. Thirdly, deep learning framework is used to capture driver's facial data quickly. Then based on the principle of imaging photoplethysmography (IPPG) algorithm, physiological parameters such as heart rate (HR) and oxygen saturation (SpO 2 ) are monitored in real time. Results of expression analysis can further support the physiological parameters monitoring results. The experimental results show that the method proposed in this paper can monitor the physiological parameters of drivers in real time and accurately, and it is hopeful to play an active role in improving traffic safety.

关键词： Biomedical monitoring Monitoring Cameras Videos Heart rate Real-time systems

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Development of an MEMS based biomimetic whisker sensor for tactile sensing

Development of an MEMS based biomimetic whisker sensor for t...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Zihou Wei Qing Shi Chang Li Shurui Yan Guanglu Jia Zhigang Zeng Qiang Huang Toshio Fukuda Key Laboratory of Biomimetic Robots and Systems Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education Beijing China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

It has been demonstrated that biological whisker sensing system has excellent capacity to achieve the pattern recognition, and whisker sensor based robot tactile perception has caused widespread concern. However, most of current existed whisker sensors are too large in size, and are difficult to be integrated into a small robot. In this paper, we proposed a MEMS based biomimetic whisker sensor inspired by rats, which integrates four sensing units on a square silicon wafer with a side length of 6.8mm and makes it possible to be installed on a small robot with similar size to a real rat. Our sensor measure the bending moment at the root of the whisker based on the piezoresistive effect. We have explored the range of functions that whisker arrays can serve on and it is shown that our whisker sensor can determine obstacle distance, and is also capable of gathering surface information including shape and texture.

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Multiple phase transitions in networks of directed networks

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Physical Review E 2019年第1期99卷 012312-012312页

作者： Xueming Liu Linqiang Pan H. Eugene Stanley Jianxi Gao Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Department of Physics Center for Polymer Studies Boston University Boston Massachusetts 02215 USA Department of Computer Science Rensselaer Polytechnic Institute Troy New York 12180 USA

The robustness in real-world complex systems with dependency connectivities differs from that in isolated networks. Although most complex network research has focused on interdependent undirected systems, many real-world networks—such as gene regulatory networks and traffic networks—are directed. We thus develop an analytical framework for examining the robustness of networks made up of directed networks of differing topologies. We use it to predict the phase transitions that occur during node failures and to generate the phase diagrams of a number of different systems, including treelike and random regular (RR) networks of directed Erdős-Rényi (ER) networks and scale-free networks. We find that the the phase transition and phase diagram of networks of directed networks differ from those of networks of undirected networks. For example, the RR networks of directed ER networks show a hybrid phase transition that does not occur in networks of undirected ER networks. In addition, system robustness is affected by network topology in networks of directed networks. As coupling strength q increases, treelike networks of directed ER networks change from a second-order phase transition to a first-order phase transition, and RR networks of directed ER networks change from a second-order phase transition to a hybrid phase transition, then to a first-order phase transition, and finally to a region of collapse. We also find that heterogenous network systems are more robust than homogeneous network systems. We note that there are multiple phase transitions and triple points in the phase diagram of RR networks of directed networks and this helps us understand how to increase network robustness when designing interdependent infrastructure systems.

关键词： Attacks on networks Cascades Continuous phase transition Discontinuous phase transition Network phase transitions Percolation phase transition Random attacks Robustness Topological phase transition

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Multi-task deep learning with dynamic programming for embryo early development stage classification from time-lapse videos

arXiv

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arXiv 2019年

作者： Liu, Zihan Huang, Bo Cui, Yuqi Xu, Yifan Zhang, Bo Zhu, Lixia Wang, Yang Jin, Lei Wu, Dongrui Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Reproductive Medicine Center Huazhong University of Science and Technology Tongji Hospital Wuhan430074 China

Time-lapse is a technology used to record the development of embryos during in-vitro fertilization (IVF). Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF. This paper proposes a multi-task deep learning with dynamic programming (MTDL-DP) approach for this purpose. It first uses MTDL to pre-classify each frame in the time-lapse video to an embryo development stage, and then DP to optimize the stage sequence so that the stage number is monotonically non-decreasing, which usually holds in practice. Different MTDL frameworks, e.g., oneto-many, many-to-one, and many-to-many, are investigated. It is shown that the one-to-many MTDL framework achieved the best compromise between performance and computational cost. To our knowledge, this is the first study that applies MTDL to embryo early development stage classification from time-lapse videos. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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An improved artificial bee colony algorithm and its taguchi analysis 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Ni, Yudong Li, Yuanyuan Shen, Yindong School of Mathematics Hefei University of Technology Hefei230009 China School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan430074 China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

The artificial bee colony (ABC) algorithm is one of well-known evolutionary algorithms, which has been successfully applied to many continuous or combinatorial optimization problems. To increase further its convergence speed and avoid being trapped in local optimum, this paper proposes an improved ABC algorithm (IABC), which aims to enhance diversification of search at each stage of the ABC algorithm. Firstly, a chaotic mapping rule is established by introducing a chaos operator into the initial position generation rules in order to ensure the ergodicity of initial positions. Then, an isometric contraction parallel search rule is devised, based on which a neighborhood search on initial positions is performed to enhance the convergence speed and the local search ability. Next, a parallel selection strategy is developed by using roulette and reverse roulette simultaneously, which allows selecting poor positions to escape from local optimum. Meanwhile, a global updating mechanism based on gravitational potential field is developed, which can guide the rejection and generation of positions to accelerate the convergence of the algorithm. The computational results show that the IABC can improve the convergence speed and solution quality without falling into the local optimum prematurely. Finally, a further analysis on the IABC is conducted using the Taguchi method, which focuses on the factor level setting related to the following key factors: chaotic mapping rules in the initial position generation rules, isometric contraction parallel search rules, parallel selection strategies and the update threshold in the global updating mechanism. The results display that the optimal combination of factor levels has been achieved in the IABC. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Taguchi methods

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Refrigerant capacity detection of dehumidifier based on time series and neural networks 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Peng, Gang Yang, Zuhuang Wang, Min School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan China Shenzhen Institute Huazhong University of Science and Technology Shenzhen518060 China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

Refrigerant leakage is common in the use of dehumidifier, leading to decrease of dehumidification ability. To detect refrigerant capacity effectively and maintain it timely, a neural network with deep learning skills was proposed based on time series. As input of networks, the time series consists of the operating parameters of the dehumidifier at multiple time points. To determine the refrigerant capacity, the proposed method combines the outputs on the neural networks of all the time series examples in a single run of dehumidifier. As the result suggests, the proposed method is capable of detecting the refrigerant capacity with a low missing rate and high accuracy, which improves the maintenance mechanism of the dehumidifier and is of great significance to ensuring a comfortable air environment for user. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Time series

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Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs

arXiv

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arXiv 2019年

作者： Liu, Zihan Meng, Lubin Zhang, Xiao Fang, Weili Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Design and Environment National University of Singapore 117566 Singapore Zhejiang Lab Hangzhou311121 China

Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs. Experimental results demonstrated the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verified the transferability of UAPs in EEG-based BCI systems. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a potentially critical security concern of BCIs. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

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Micro Channel for Analyzing Mechanical Adaption of Cancer Cell

Micro Channel for Analyzing Mechanical Adaption of Cancer Ce...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Pengyun Li Xiaoming Liu Xiaoqing Tang Masaru Kojima Jian Huang Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and the Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Osaka University 1-3 Machikaneyama Toyonaka Osaka Japan Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

It was reported that cancer cells could biologically respond and adapt to the confined environment during migration in extracellular matrix (ECM). But it is not clearly studied that how cancer cells respond and adapt to confined environment in mechanical way. Thus, in this study, we designed and fabricated a kind of one-layer micro channel to mimic confined environment for analyzing mechanical adaption of cell. This micro channel could apply sequential mechanical stresses on a number of cells with high throughput, and the time of cells passing through five rows of micro channels were used to evaluate their adaptability to the sequential and confined space. In experiments, each cell suffered 20 times of mechanical stresses after passing through 20 squeezing channels arranged in 5 rows. We observed the sequential squeezing processes under microscope and investigated the passing time using a high-speed camera. The preliminary results indicated that cancer cells could pass through and adapt to the confined environment faster than normal cells.

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EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

arXiv

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arXiv 2020年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine Ucsd La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia Zhaw DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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