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A novel computational approach to predict activity of nuclear export signals

Journal of Computational and Theoretical Nanoscience 2015年第12期12卷 6016-6019页

作者： Chen, Zhihua Zhang, Pan Wu, Tingfang Shi, Xiaolong Wang, Xun Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology WuhanHubei430074 China College of Computer and Communication Engineering China University of Petroleum QingdaoShandong266580 China

A nuclear export signal (NES) is a sequence of amino acids, which is a protein localization signal, and contributes to regulate localization of cellular proteins. In recent peering works, activity of NESs were introduced in computational methods, which perform significantly well in identifying unknown NESs even from huge datasets, but suffer from a shortage that only a small number of NESs are associated with experimental verified active scores. In this work, we propose a novel computational method to predict active score of NESs. Specifically, we firstly abstract relevant features to NES activity, and then construct a mathematical model based on principal component analysis (PCA) to find each feature's contribution to activity. In this way, a method to calculate active score of unknown NESs is achieved. In data experiments, it is obtained that our method can achieve accuracy rate 80%. The activity of an unknown NES candidate can give us a preliminary understanding of it, and can be used to identify positive NESs from the negative ones. To the best acknowledge of the authors, this is the first time computational method is proposed to predict NESs' activity. Copyright © 2015 American Scientific Publishers All rights reserved.

关键词： Computational methods

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Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays

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Neural networks : the official journal of the International Neural Network Society 2016年 79卷 117-27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: hustzgzeng@***. Department of Computer Science College of Science and Engineering City University of Hong Kong Kowloon Tong Hong Kong. Electronic address: jwang.cs@cityu.edu.hk.

This paper addresses the multistability for a general class of recurrent neural networks with time-varying delays. Without assuming the linearity or monotonicity of the activation functions, several new sufficient conditions are obtained to ensure the existence of (2K+1)(n) equilibrium points and the exponential stability of (K+1)(n) equilibrium points among them for n-neuron neural networks, where K is a positive integer and determined by the type of activation functions and the parameters of neural network jointly. The obtained results generalize and improve the earlier publications. Furthermore, the attraction basins of these exponentially stable equilibrium points are estimated. It is revealed that the attraction basins of these exponentially stable equilibrium points can be larger than their originally partitioned subsets. Finally, three illustrative numerical examples show the effectiveness of theoretical results.

关键词： Cohen–Grossberg neural networks Multistability Non-monotonic activation functions Time-varying delays

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Tissue P Systems with Look-ahead Mode

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Chinese Journal of Electronics 2014年第1期23卷 81-86页

作者： JIANG Yun SONG Tao ZHANG Zheng School of Computer Science and Information Engineering Chongqing Technology and Business University Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a spacetime tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： Tissue P system Look-ahead mode Recursively enumerable language Cell separation 3-Coloring

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Small universal simple spiking neural P systems with weights

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Science China(Information Sciences) 2014年第9期57卷 19-29页

作者： ZENG XiangXiang PAN LinQiang PREZ-JIMNEZ Mario J. Key Laboratory of Image Processing and Intelligent Control School of AutomationHuazhong University of Science and Technology Department of Computer Science and Artificial Intelligence University of SevillaAvda. Reina Mercedes s/nSevilla 41012Spain

Spiking neural P systems with weights(WSN P systems,for short)are a new variant of spiking neural P systems,where the rules of a neuron are enabled when the potential of that neuron equals a given *** is known that WSN P systems are universal by simulating register ***,in these universal systems,no bound is considered on the number of neurons and *** this work,a restricted variant of WSN P systems is considered,called simple WSN P systems,where each neuron has only one *** complexity parameter,the number of neurons,to construct a universal simple WSN P system is *** is proved that there is a universal simple WSN P system with 48 neurons for computing functions;as generator of sets of numbers,there is an almost simple(that is,each neuron has only one rule except that one neuron has two rules)and universal WSN P system with 45 neurons.

关键词： bio-inspired computing membrane computing P system spiking neural P system universal computing device

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Lag Synchronization of Switched Neural Networks via Neural Activation Function and Applications in image Encryption

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IEEE Transactions on Neural Networks and Learning Systems 2015年第7期26卷 1493-502页

作者： Wen, Shiping Zeng, Zhigang Huang, Tingwen Meng, Qinggang Yao, Wei School of Automation Huazhong University of Science and Technology China and the Key Laboratory of Image Processing and Intelligent Control Wuhan 430074 China Texas A and M University at Qatar Doha 23874 Qatar Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom School of Computer Science South-Central University for Nationalities Wuhan 430074 China

This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption.

关键词： Cryptography

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Circuit design and exponential stabilization of memristive neural networks

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Neural networks : the official journal of the International Neural Network Society 2015年 63卷 48-56页

作者： Shiping Wen Tingwen Huang Zhigang Zeng Yiran Chen Peng Li Department of Control Science and Engineering 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: wenshiping226@***. Texas A & M University at Qatar Doha 5825 Qatar. Electronic address: tingwen.huang@qatar.tamu.edu. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng527@***. Electrical and Computer Engineering Department University of Pittsburgh Pittsburgh PA 15261 USA. Electronic address: yic52@pitt.edu. Department of Electrical and Computer Engineering Texas A & M University College Station TX 77843 USA. Electronic address: pli@tamu.edu.

This paper addresses the problem of circuit design and global exponential stabilization of memristive neural networks with time-varying delays and general activation functions. Based on the Lyapunov-Krasovskii functional method and free weighting matrix technique, a delay-dependent criteria for the global exponential stability and stabilization of memristive neural networks are derived in form of linear matrix inequalities (LMIs). Two numerical examples are elaborated to illustrate the characteristics of the results. It is noteworthy that the traditional assumptions on the boundness of the derivative of the time-varying delays are removed.

关键词： Memristor Neural networks Stabilization

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Temperature Control for Nano-Scale Films by Spatially-Separated Atomic Layer Deposition Based on Generalized Predictive Control

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IEEE Transactions on Nanotechnology 2015年第6期14卷 1094-1103页

作者： He, Wen-Jie Zhang, Hai-Tao Chen, Zhiyong Lin, Ji-Long Tian, Kan Shan, Bin Chen, Rong State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science University of Newcastle CallaghanNSW2308 Australia China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

This paper established a closed-loop temperature control system for a spatially-separated atomic layer deposition (S-ALD) reactor using generalized predictive control (GPC) algorithm. The GPC-based closed-loop control system rapidly and precisely stabilized the reactor temperature in the presence of thermal field disturbances. Compared with the proportion-integration differentiation (PID) control commonly used for S-ALD, the closed-loop GPC system attenuated reaction temperature oscillations when producing two-element nano-scale thin films. Furthermore, the proposed GPC system demonstrated the superiority in multi-element nano-laminates depositing efficiency by reducing the settling time with a substrate moving between different reactors. Electrical and optical properties of films verified the feasibility of the proposed GPC system. Finally, experimental results presented that the microstructure of the deposited ALD nanometer thin film was improved with the developed GPC system, compared with the PID strategy. © 2002-2012 IEEE.

关键词： Atomic layer deposition

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On string languages generated by sequential spiking neural P systems based on maximum spike number 1

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13th International Conference on Unconventional Computation and Natural Computation, UCNC 2014

作者： Jiang, Keqin Zhang, Yuzhou Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China School of Computer and Information Anqing Normal University Anqing 246133 Anhui China

ISBN: (数字)9783319081236

ISBN: (纸本)9783319081229

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, we consider SN P systems with the restriction: at each step the neuron with the maximum number of spikes among the neurons that can spike will fire (if there is a tie for the maximum number of spikes stored in the active neurons, only one of the neurons containing the maximum is chosen non-deterministically). We investigate the computational power of such sequential SN P systems that are used as language generators. We prove that recursively enumerable languages can be characterized as projections of inverse-morphic images of languages generated by that sequential SN P systems. The relationships of the languages generated by these sequential SN P systems with finite and regular languages are also investigated. © 2014 Springer International Publishing Switzerland.

关键词： Neurons

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Multiple neural networks switched prediction for landslide displacement

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Engineering Geology 2014年 186卷 91-99页

作者： Lian, Cheng Zeng, Zhigang Yao, Wei Tang, Huiming 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 School of Computer Science South-Central University for Nationalities Wuhan 430074 China Faculty of Engineering China University of Geosciences Wuhan 430074 China

An accurate prediction of landslide displacement is challenging and of great interest to governments and researchers. In order to reduce the risk of selecting the types of influencing factors and artificial neural networks (ANNs), a multiple ANNs switched prediction method is proposed for landslide displacement forecasting. In the first stage, a set of individual neural networks are developed based on different environmental factors and/or different training algorithms. In the second stage, a switched prediction method is used to select the appropriate individual neural network for prediction purpose. For verification and testing, three typical landslides in Three Gorges Reservoir, namely Baishuihe landslide, Bazimen landslide and Shiliushubao landslide, are presented to test the effectiveness of our method. Application results demonstrate that the proposed method can significantly improve model generalization and perform similarly to, or better than, the best individual ANN predictor. © 2014 Elsevier B.V.

关键词： Forecasting

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