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18th International Conference on Membrane Computing, CMC 1018

作者： Alhazov, Artiom Freund, Rudolf Ivanov, Sergiu Institute of Mathematics and Computer Science Academy of Sciences of Moldova Academiei 5 Chişinău2028 Moldova 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 Faculty of Informatics TU Wien Favoritenstraße 9–11 Vienna1040 Austria LACL Université Paris Est – Créteil Val de Marne 61 av. Général de Gaulle Créteil94010 France TIMC-IMAG/DyCTiM Faculty of Medicine of Grenoble 5 avenue du Grand Sablon La Tronche38700 France

ISBN: (纸本)9783319733586

P systems are a model of hierarchically compartmentalized multiset rewriting. We introduce a novel kind of P systems in which rules are dynamically constructed in each step by non-deterministic pairing of left-hand and right-hand sides. We define three variants of right-hand side randomization and compare each of them with the power of conventional P systems. It turns out that all three variants enable non-cooperative P systems to generate exponential (and thus non-semi-linear) number languages. We also give a binary normal form for one of the variants of P systems with randomized rule right-hand sides. © 2018, Springer International Publishing AG.

关键词： Hierarchical systems

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Motion deblurring based on convolutional neural network 12th

Motion deblurring based on convolutional neural network

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12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Tan, Yunfei Zhang, Di Xu, Fei Zhang, Danyang Department of Physical Education Huazhong University of Science and Technology WuhanHubei430074 China Key Laboratory of Image Information Processing Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China JamaicaNY11451 United States

ISBN: (纸本)9789811071782

Object motion blur results when the object in the scene moves during the recording of a single exposure, either due to too rapid movement or long exposure, leaving streaks of the moving object in the image and thus degrading its quality. In this paper, we present a method to solve the object motion blur problem in images with clear static background. Specifically, we propose an object motion deblurring algorithm that uses a convolutional neural network with six convolutional layers to deblur the image. Taking advantages of the strong ability of feature learning in convolutional neural networks, our method can remove the blurring effect of fast-moving object while keeping the clear background untouched. It is well known that neural networks are best driven by large data sets and more data means more benefits for training convolutional neural networks;therefore, we generated training set of 144,000 images and test set of 32,400 images. Through carefully designed training process, our model learned the ability of deblurring the blurred object while keeping the clear background. The experiment results show that our approach can generate superior results to a representative image deblurring algorithm that treats the same blurred object and clear background. © Springer Nature Singapore Pte Ltd 2017.

关键词： image enhancement

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Proxy-based Sliding Mode Stabilization of a Class of Second-order Nonlinear System

Proxy-based Sliding Mode Stabilization of a Class of Second-...

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Asian control Conference

作者： Guangzheng Ding Jian Huang Bin Hu Zhi-Hong Guan Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei P.R. China

ISBN: (纸本)9781509015740;9781509015733

The essential feature of conventional proxy-based sliding mode control (PSMC) method is the introduction of a proxy, which is controlled by a normal sliding mode control (SMC) approach to track the desired trajectory. Both the safety problem in conventional stiff position control and the chattering problem in the SMC are overcome by the PSMC strategy. Meanwhile, the stability problem of PSMC is not well addressed for general nonlinear systems. In this paper, a new PSMC method is proposed for robust tracking control of a class of second-order nonlinear systems. A PD type virtual coupling is used and a specified sliding mode controller is designed in the proposed PSMC method. Based on the model of a class of second-order nonlinear systems, the stability of the closed-loop PSMC system is proved by Lyapunov theorem. Numerical simulations were carried out to verify the propose method.

关键词： Nonlinear systems Force Couplings Stability analysis Robustness Sliding mode control Safety

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Region stability analysis for switched discrete-time recurrent neural network with multiple equilibria

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Neurocomputing 2017年 249卷 182-190页

作者： Gang Bao Zhigang Zeng Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control China Three Gorges University Yichang 443002 China School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China

This paper investigates a kind of switched discrete-time neural network. Such neural network is composed of multiple sub-networks and switched different sub-networks according to the states of neural network. There is no common equilibrium for all of sub-networks, i.e., multiple equilibria coexist. Firstly, a bounded condition is presented for the switched discrete-time neural network. And then sufficient conditions are derived to ensure region stability of the equilibrium points of such neural network by mathematical analysis and nonsingular M-matrix theory. Four examples are presented to verify the validity of our results.

关键词： Switched neural network Region stability Multiple equilibria

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Predicting essential proteins based on gene expression data, subcellular localization and PPI data 1

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12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Lei, Xiujuan Wang, Siguo Pan, Linqiang School of Computer Science Shaanxi Normal University Xi’anShaanxi710119 China Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology WuhanHubei430074 China School of Electric and Information Engineering Zhengzhou University of Light Industry ZhengzhouHenan450002 China

ISBN: (数字)9789811071799

ISBN: (纸本)9789811071782

Predicting essential proteins is indispensable for understanding the minimal requirements of cellular survival and development. In recent years, many methods combined with the topological features of PPI networks have been proposed. However, most of these approaches ignored the intrinsic characteristics of biological attributes. This paper integrates Gene expression data, Subcellular localization and PPI networks to identify essential proteins, named GSP. We use local average connectivity and edge clustering coefficient unite with gene expression data to measure centralities of nodes. Compared with non-essential proteins, essential proteins appear more frequently in some subcellular localizations such as Nucleus and considering that different compartments play different roles, thus we integrate subcellular localization information to identify essential proteins. The computational experiment results on the yeast PPI networks show that the proposed method GSP outperforms other state-of-art methods including DC, EC, IC, SC, NC, LAC, PeC, WDC and UDoNC. © Springer Nature Singapore Pte Ltd 2017.

关键词： Proteins

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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A deep learning-based remaining useful life prediction approach for bearings

arXiv

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arXiv 2018年

作者： Cheng, Cheng Ma, Guijun Zhang, Yong Sun, Mingyang Teng, Fei Ding, Han Yuan, Ye The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The College of Control Science and Engineering Zhejiang University Hangzhou310007 China The Department of Electrical & Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this paper, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert-Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using a ǫ-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： Hilbert-Huang transform

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Complete stability of delayed recurrent neural networks with Gaussian activation functions

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Neural networks : the official journal of the International Neural Network Society 2017年 85卷 21-32页

作者： Peng Liu Zhigang Zeng Jun Wang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: liupengjz@***. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng@***. Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong. Electronic address: jwang.cs@cityu.edu.hk.

This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3 equilibrium points with 0≤k≤n, among which 2 and 3-2 equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i.e., the neural networks are completely stable. The derived conditions herein can be easily tested. Finally, a numerical example is given to illustrate the theoretical results.

关键词： Complete stability Gaussian functions Recurrent neural networks Time-varying delays

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Second-Order controllability of Multi-Agent Systems with Multiple Leaders

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Communications in Theoretical Physics 2016年第5期65卷 585-592页

作者：刘波史运涛苏厚胜韩潇 College of Science North China University of Technology Key Laboratory of Beijing for Field-Bus Technology & Automation North China University of Technology School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaHuazhong University of Science and Technology

This paper proposes a new second-order continuous-time multi-agent model and analyzes the controllability of second-order multi-agent system with multiple leaders based on the asymmetric *** paper considers the more general case:velocity coupling topology is different from location coupling *** sufficient and necessary conditions are presented for the controllability of the system with multiple *** addition,the paper studies the controllability of the system with velocity damping *** results are given to illustrate the correctness of theoretical results.

关键词： controllability second-order multi-agent systems multiple leaders asymmetric topology

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Anisotropic interaction rules in circular motions of pigeon flocks: An empirical study based on sparse Bayesian learning

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Physical Review E 2017年第2期96卷 022411-022411页

作者： Duxin Chen Bowen Xu Tao Zhu Tao Zhou Hai-Tao Zhang Key Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan 430074 P.R. China Web Sciences Center University of Electronic Science and Technology of China Chengdu 610054 P.R. China

Coordination shall be deemed to the result of interindividual interaction among natural gregarious animal groups. However, revealing the underlying interaction rules and decision-making strategies governing highly coordinated motion in bird flocks is still a long-standing challenge. Based on analysis of high spatial-temporal resolution GPS data of three pigeon flocks, we extract the hidden interaction principle by using a newly emerging machine learning method, namely the sparse Bayesian learning. It is observed that the interaction probability has an inflection point at pairwise distance of 3–4 m closer than the average maximum interindividual distance, after which it decays strictly with rising pairwise metric distances. Significantly, the density of spatial neighbor distribution is strongly anisotropic, with an evident lack of interactions along individual velocity. Thus, it is found that in small-sized bird flocks, individuals reciprocally cooperate with a variational number of neighbors in metric space and tend to interact with closer time-varying neighbors, rather than interacting with a fixed number of topological ones. Finally, extensive numerical investigation is conducted to verify both the revealed interaction and decision-making principle during circular flights of pigeon flocks.

关键词： Collective behavior Self-organization Birds Flocks Organisms

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