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arXiv 2017年

作者： Ye, Yumeng Liu, Haichun Zhang, TianHong Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation and Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Electroencephalography is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. Among them, analysis of the risk of schizophrenia using EEG data is a relatively new research topic. However, there are many difficulties in analyzing EEG data, including its complex composition, low amplitude as well as low signal-to-noise ratio. Some of the existing methods of analysis are based on feature extraction and machine learning to differentiate the phase of schizophrenia (First-episode schizophrenia, Healthy controls or Clinical high-risk) that samples belong to. However, medical research requires the use of machine learning not only to give more accurate classification results, but also to give the results that can be applied to pathological studies. The main purpose of this study is to obtain the weight values as the representation of influence of each frequency band on the classification of schizophrenia phases on the basis of a more effective classification method using the LES feature extraction, and then the weight values are processed and applied to improve the accuracy of machine learning classification. We propose a method called weight-voting to obtain the weights of sub-bands features by using results of classification for voting to fit the actual categories of EEG data, and using weights for reclassification. Through this method, we can first obtain the influence of each band in distinguishing three schizophrenia phases, and analyze the effect of band features on the risk of schizophrenia contributing to the study of psychopathology. In addition, the weights applied to the original classifier can achieve the upgrade of the classification effect, which contributes to the BCI-assisted system of diagnosis and treatment. Our results show that there is a high correlation between the change of weight of low gamma band and the difference between HC, CHR an

关键词： Electroencephalography

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The Euler's discretization effect on sliding mode control of perturbed double integrator

The Euler's discretization effect on sliding mode control of...

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International Workshop on Variable Structure Systems, VSS

作者： Yan Yan Chaoli Wang Xinghuo Yu Changyin Sun School of Automation Southeast University Nanjing Jiangsu China Control Science and Engineering Department University of Shanghai for Science and Technology Shanghai China School of Electrical and Computer Engineering RMIT University Melbourne VIC Australia

This paper addresses the problem of sliding mode control (SMC) for the perturbed double integrator system. First, the stability conditions of the continuous-time system is analyzed. Moreover, the Euler's discretization of the perturbed double integrator under SMC is introduced and the upper bounds of the discretized system steady states are established. Simulations are presented to illustrate the theoretical results.

关键词： Variable structure systems integrators Continuous time systems SELECTION AND MONITORING CHASSIS Small Magellanic Cloud Sheet molding compounds Discretization Upper bound DOUBLE time system simulating

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Optimal disturbance rejection via feedforward-PD for bilinear systems with external sinusoidal disturbances

Advances in Modelling and Analysis C

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Advances in Modelling and Analysis C 2016年第1期71卷 125-137页

作者： Liu, Guitao Fu, Mingyue Du, Houpeng School of Computer and Information Science Hubei Engineering University Xiaogan Hubei432000 China Department of Automatic Control Northwestern Polytechnical University Xi'an710072 China ZaoZhuang HongYi Automation Technology Co.LTD ZaoZhuang ShanDong277100 China

The optimal control problem is considered for bilinear systems affected by sinusoidal disturbances. A feedforward-PD optimal disturbance rejection control law based on output feedback of LQR is developed. By analysis the optimal disturbances rejection algorithms of bilinear systems based on time-domain state-space representation and the PD-control algorithms based on frequency-domain output feedback, the optimal PD tuning parameters are derived to achieve the purpose of optimal disturbances rejection. Finally, simulation results show that the algorithm is high in efficiency, easy to implement and well robust with respect to external sinusoidal disturbances.

关键词： Disturbance rejection

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Policy iteration for optimal control of weakly coupled nonlinear systems with completely unknown dynamics

Policy iteration for optimal control of weakly coupled nonli...

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American control Conference

作者： Chao Li Ding Wang Derong Liu Haibo He The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Automation and Electrical Engineering University of Science and Technology Beijing 100083 China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston 02881 USA

ISBN: (纸本)9781467386838

In this paper, an online learning algorithm based on policy iteration is established to solve the optimal control problem for weakly coupled nonlinear continuous-time systems. Using the weak coupling theory, the original problem is transformed into three reduced-order optimal control problems. To obtain the optimal control laws without system dynamics, we construct an online data-based integral policy iteration algorithm which is used to solve the decoupled optimal control problems. The actor-critic technique based on neural networks and the least squares method are used to implement the model-free learning algorithm. A simulation example is given to verify the applicability of the developed algorithm.

关键词： Optimal control Heuristic algorithms Mathematical model System dynamics Nonlinear systems Partitioning algorithms Computational modeling

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control reconfiguration of full-state linearizable systems by a virtual actuator 9

Control reconfiguration of full-state linearizable systems b...

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9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015

作者： Vey, D. Lunze, J. Žáceková, E. Pcolka, M. Šebek, M. Institute for Automation and Computer Control Ruhr-Universität Bochum Bochum Germany Department of Control Engineering Czech Technical Uiversity in Prague Prague Czech Republic

This paper presents a new method for control reconfiguration of nonlinear, inputaffine systems subject to actuator failures. For full-state linearizable systems it is shown that after the occurrence of an actuator failure the linearizing feedback has to be adapted to the failure case. In a second step, the reconfiguration of the re-linearized system can be accomplished by means of a static linear virtual actuator. An interesting observation is the fact that the faulty system stays reconfigurable as long as there is one actuator left in the faulty plant. This paper illustrates the method by considering a fluid process. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Actuators

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Matrix flat splicing systems 11th

Matrix flat splicing systems

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Ceterchi, Rodica Pan, Linqiang Song, Bosheng Subramanian, K.G. Faculty of Mathematics and Computer Science University of Bucharest 14 Academiei Street Bucharest010014 Romania School of Electric and Information Engineering Zhengzhou University of Light Industry ZhengzhouHenan450002 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China Department of Mathematics Madras Christian College Chennai600059 India

ISBN: (纸本)9789811036101

In dealing with the problem of modelling DNA recombination, the operation of splicing on linear and circular strings of symbols was introduced. Inspired by splicing on circular strings, the operation of flat splicing on a pair of strings (u, v) was considered. This operation involves "cutting" u at a specified position and "inserting" v into it, with v having a pre-specified prefix as well as suffix defined by a flat splicing rule. In this work, we consider a well-known technique in formal language theory, known as "matrix of rules", and introduce matrix of flat splicing rules and thus define matrix flat splicing system (MFSS). Some results on the language generative power of MFSS are provided. An application of MFSS in the generation of chain code pictures is also pointed out. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Formal languages

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A structural synthesis of information computer networks of automated control systems based on genetic algorithms 6

A structural synthesis of information computer networks of a...

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6th FTRA International Conference on computer Science and its Applications, CSA 2014

作者： Marakhimov, Avazjon R. Siddikov, Isomiddin H. Nasridinov, Aziz Byun, Jeong-Yong Department of Automation and Control Tashkent State Technical University Uzbekistan School of Computer Engineering Dongguk University at Gyeongju Korea Republic of

ISBN: (纸本)9783662454015

This paper provides a new approach for solving the problems of modeling and structural syntheses of information networks of automated control systems by applying fuzzy sets theory, fuzzy logic and genetic algorithms. Modified genetic operators of crossovering, mutation, and algorithms of evolutionary syntheses of information network systems are developed. The results of computational experiments on the influence of using the crossovering and mutation operators are demonstrated. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Soft computing

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Renewable-load matching dispatch for isolated power systems with intermittent renewable sources

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International Journal of Simulation and Process Modelling 2016年第3-4期11卷 270-280页

作者： Shen, Ying Zhao, Qianchuan Li, Mingyang Center for Intelligent and Networked Systems Department of Automation TNList Tsinghua University Beijing100084 China School of Control and Computer Engineering State Key Laboratory of Alternate Power Systems with Renewable Energy Sources North China Electric Power University Beijing102206 China

This paper proposes a renewable-load matching approach for dispatching generation resources in power systems with high renewable power penetrations. In order to utilise the capability of renewable resources to follow the load variations, the proposed matching approach is to restrict the output of renewable generators by a load-related bound in each dispatch time period. The dispatch problem is formulated to minimise the system cost comprising fuel, emission, and regulation costs as well as renewable power curtailment penalty. Compared with the traditional approach of treating renewable resources as negative loads, the proposed matching approach can significantly reduce the system cost by including the dynamic control of renewable power output. Moreover, the multi-objective function provides a way to study the trade-off among different costs in power systems with renewable resources. Solution is obtained by transforming the optimisation problem into its equivalent form and using local search algorithms. Simulations are implemented on a specific isolated power system with four generators to demonstrate the advantage of proposed approach. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Costs

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Preface

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Advances in Intelligent Systems and Computing 2017年 617卷 v-vi页

作者： Vittorini, Pierpaolo Gennari, Rosella Di Masciow, Tania Rodríguez, Sara De la Prieta, Fernando Ramos, Carlos Silveira, Ricardo Azambuja Department of Life Health and Environmental Sciences University of L’Aquila L’Aquila Italy Computer Science Faculty Free University of Bozen-Bolzano Bolzano Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Department of Computer Science and Automation Control University of Salamanca Salamanca Spain Politécnico do Porto GECAD Instituto Superior de Engenharia do Porto Porto Portugal Department of Computer Science and Statistics Federal University of Santa Catarina FlorianópolisSC Brazil

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