检索结果-内蒙古大学图书馆

作者： Magyar, Attila Hangos, Katalin M. Department of Electrical Engineering and Information Systems University of Pannonia Egyetem street 10. Veszprém H-8200 Hungary Process Control Research Group Control and Automation Research Institute HAS Kende street 13-17 BudapestH-1111 Hungary

Using translated X-factorable phase space transformations and nonlinear variable transformations a dynamically similar linear ODE model is associated to the Lotka-Volterra system models with a positive equilibrium point. This enables to use a linear full state feedback controller to stabilize the system in controllable cases, that leaves the open loop equilibrium point unchanged. The linear state feedback controller design problem in the general case has also been formulated to ensure the compartmental property of the closed loop system from which the existence of a diagonal Lyapunov function follows. Further extension has been obtained by using the time re-parametrization transformation defined for quasi-polynomial models. © 2015

关键词： Closed loop systems

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Sensor placement for leak location in water distribution networks using the leak signature space 9

Sensor placement for leak location in water distribution net...

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

作者： Casillas, Myrna V. Garza-Castañón, Luis E. Puig, Vicenç Supervision and Advanced Control Department Tecnológico de Monterrey Campus Monterrey Av. Eugenio Garza Sada 2501 Monterrey64849 Mexico Advanced Control Systems Research Group Technical University of Catalonia Ramhla Sant Nehridi 10 Terrassa08222 Spain

In this paper, a sensor placement approach to improve the leak location in water distribution networks is proposed. The sensor placement problem is formulated as an integer optimization problem where the criterion to minimize is the number of overlapping signature domains computed from the leak signature space (LSS) representation. A stochastic optimization process is proposed to solve this problem, based on either a Genetic Algorithms (GA) or a Particle Swarm Optimization (PSO) approach. Experiments on two different DMAs are used to evaluate the performance of the resolution methods as well as the efficiency achieved in the leak location when using the sensor placement results. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Genetic algorithms

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Robust Decentralized control of Interconnected Nonlinear systems Subject to Uncertainties and Disturbances via Generalized Extended State Observers 34

Robust Decentralized Control of Interconnected Nonlinear Sys...

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第三十四届中国控制会议

作者： ZHANG Chuanlin YANG ning HE Shuaipeng JIA Ruting Shanghai Engineering Research Center of Intelligent Management and Control for Power Process College of Automation EngineeringShanghai University of Electric Power Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Electrical and Computer Engineering Department California State University

ISBN: (纸本)9789881563897

This paper studies the problem of robust decentralized control for a class of large-scale interconnected systems subject to uncertainties and disturbances via a generalized active disturbance rejection control method. First, a novel extended state observer(ESO) is constructed separately for each subsystem with less dependence of the precise system information and structure. Second, an explicit formula of the decentralized active disturbance rejection control(DADRC) law is presented by utilizing the output feedback domination approach. It is shown that by a delicate analysis procedure, the closed-loop system can be rendered semi-globally asymptotically stable only using decentralized output feedback anti-disturbance controller. Numerical simulations show the efficiency of the proposed method.

关键词： output feedback control global stabilization disturbance rejection control extended state observer

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Analysis and control of Assembly Precision in Different Assembly Sequences

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Procedia CIRP 2015年 27卷 117-123页

作者： Z. Zhu L. Qiao Advanced Manufacturing Technology and Systems Research Centre Department of Industrial and Manufacturing Systems Engineering School of Mechanical Engineering and Automation Beihang University Beijing 100191 PRC

In the assembly process, the tolerance of mating parts or products, especially geometric tolerance, may cause geometric deviations in the assembly. These deviations must be kept in a reasonable range to ensure the precision and stability of assembly. The paper analyzes the influence caused by the difference in assembly sequences on the geometric deviation of key features in an assembly, which results in the influences on assembly precision. The research is done on an example model with two approaches: the mathematical method based on homogenous transformation matrix (HTM) and the simulation-based method. In the mathematical method, a transformation matrix is used to describe the geometric deviations of mating features caused by the tolerance and assembly clearance, and the resulting propagations in different assembly sequences. In the other method, the geometric deviations are measured with a CAT package based on the Monte-Carlo technique. Through the results of the methods, assembly precision could be identified and corresponding assembly sequences could be evaluated, which may serve as the evidence for the optimization assembly process planning.

关键词： assembly precision assembly sequence tolerance analysis homogenous transformation matrix simulation

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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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A direct uncertainty minimization framework in model reference adaptive control

A direct uncertainty minimization framework in model referen...

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AIAA Guidance, Navigation, and control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015

作者： Yucelen, Tansel Gruenwald, Benjamin Muse, Jonathan A. Mechanical and Aerospace Engineering Department Advanced Systems Research Laboratory Missouri University of Science and Technology RollaMO65409 United States Air Force Research Laboratory Autonomous Control Branch WPAFB OH45433 United States

ISBN: (纸本)9781510801097

This paper considers stabilization and command following of uncertain dynamical systems and presents a new adaptive control approach with improved system performance. The proposed framework consists of a novel architecture involving modification terms in the adaptive controller and the update law. Specifically, these terms are activated when the system error between an uncertain dynamical system and a given reference model, which captures a desired closed-loop dynamical system behavior, is nonzero and vanishes as the system reaches its steady-state. This key feature of our framework allows to suppress the effect of system uncertainty on the transient system response through a gradient minimization procedure, and hence, leads to improved system performance. We further show that by automatically adjusting the design parameter of the added terms in response to system variations, we can enforce system error to approximately stay in a priori given, user- defined error performance bounds. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach. © 2015, E-flow American Institute of Aeronautics and Astronautics (AIAA). All rights reserved.

关键词： Dynamical systems

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Interval observer for a class of uncertain nonlinear singular systems

Interval observer for a class of uncertain nonlinear singula...

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Automatica

作者： Zheng, Gang Efimov, Denis Bejarano, Francisco Javier Perruquetti, Wilfrid Wang, Haoping School of Automation Nanjing University of Science and Technology Nanjing210094 China Non-A INRIA Lille-Nord Europe 40 Avenue Halley Villeneuve d'Ascq59650 France CRIStAL CNRS UMR 9189 Ecole Centrale de Lille BP 48 Villeneuve d'Ascq59651 France Department of Control Systems and Informatics National Research University 49 Kronverkskiy av. St.-Petersburg197101 Russia SEPI ESIME Ticomán IPN Av. San José Ticomán 600 Mexico CityC.P. 07340 Mexico

This paper investigates the problem of observer design for a general class of linear singular time-delay systems, in which the time delays are involved in the state, the output and the known input (if there exists). The involvement of the delay could be multiple which however is rarely studied in the literature. Sufficient conditions are proposed which guarantee the existence of a Luenberger-like observer for the general system. © 2016 Published by Elsevier Ltd.

关键词： Delay control systems Time delay Commensurate delays Delayed singular systems General systems Interval observers Nonlinear singular systems Observer Observer design Singular time delay system

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The status of KAGRA underground cryogenic gravitational wave telescope

arXiv

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arXiv 2017年

作者： Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Barton, M.A. Cannon, K. Craig, K. Creus, W. Doi, K. Eda, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Furuhata, T. Haino, S. Hasegawa, K. Hashino, K. Hayama, K. Hirobayashi, S. Hirose, E. Hsieh, B.H. Inoue, Y. Ioka, K. Itoh, Y. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kambara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawai, N. Kawamura, S. Kim, C. Kim, H. Kim, J. Kim, Y. Kimura, N. Kinugawa, T. Kirii, S. Kitaoka, Y. Kojima, Y. Kokeyama, K. Komori, K. Kotake, K. Kumar, R. Lee, H. Lee, H. Liu, Y. Luca, N. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Miyakawa, O. Miyamoto, T. Miyamoto, A. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Narikawa, T. Nguyen Quynh, L. Ni, W.-T. Ochi, T. Oh, J. Oh, S. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Peña Alleano, F.E. Pinto, I. Sago, N. Saijo, M. Saito, Y. Sakai, K. Sakai, Y. Sasaki, Y. Sasaki, M. Sato, S. Sato, T. Sekiguchi, Y. Seto, N. Shibata, M. Shimoda, T. Shinkai, H. Shoda, A. Somiya, K. Son, E. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tatsumi, D. Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueki, S. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Vocca, H. Wada, S. Wakamatsu, T. Yamada, T. Yamamoto, S. Yamamoto, T. Yamamoto, K. Yamamoto, A. Yokoyama, J. Yokozawa, T. Yoon, T.H. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. National Astronomical Observatory of Japan Mitaka Tokyo181-8588 Japan Department of Physics University of Tokyo Bunkyo Tokyo113-0033 Japan Earthquake Research Institute University of Tokyo Bunkyo Tokyo113-0032 Japan Department of Advanced Physics Hirosaki University Hirosaki Aomori036-8561 Japan National Astronomical Observatory of Japan Hida Gifu506-1205 Japan Department of Physics Tokyo Institute of Technology Meguro Tokyo152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Hida Gifu506-1205 Japan Research Center for the Early Universe University of Tokyo Bunkyo Tokyo113-0033 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa Chiba277-8582 Japan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama Toyama930-8555 Japan Faculty of Engineering University of Toyama Toyama Toyama930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Sakyo Kyoto606-8502 Japan Department of Physics Osaka City University Sumiyosi Osaka558-8585 Japan Department of Physics Osaka University Toyonaka Osaka560-0043 Japan Korea Institute of Science and Technology Information Yuseong Daejeon34141 Korea Republic of Daejeon Yuseong34055 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Computer Simulation Inje University Gimhae Gyeongsangnam50834 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul151-742 Korea Republic of High Energy Accelerator Research Organization Tsukuba Ibaraki305-0801 Japan Department of Physical Science Hiroshima University Higashihiroshima Hiroshima903-0213 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka814-0180 Japan Department of Advanced Materials Science University of Tokyo Kashiwa Chiba277-8582 Japan Istituto Nazionale di Fisica Nucleare Sapienza University Roma00185 Italy D

KAGRA is a 3-km interferometric gravitational wave telescope located in the Kamioka mine in Japan. It is the first km-class gravitational wave telescope constructed underground to reduce seismic noise, and the first km-class telescope to use cryogenic cooling of test masses to reduce thermal noise. The construction of the infrastructure to house the interferometer in the tunnel, and the initial phase operation of the interferometer with a simple 3-km Michelson configuration have been completed. The first cryogenic operation is expected in 2018, and the observing runs with a full interferometer are expected in 2020s. The basic interferometer configuration and the current status of KAGRA are described. Copyright © 2017, The Authors. All rights reserved.

关键词： Interferometers

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A Method of SMS Spam Filtering Based on AdaBoost Algorithm

A Method of SMS Spam Filtering Based on AdaBoost Algorithm

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World Congress on Intelligent control and automation

作者： Xipeng Zhang Gang Xiong Yuexiang Hu Fenghua Zhu Xisong Dong Timo R. Nyberg State Key Laboratory of Management and Control for Complex Systems Institute of Automation of Chinese Academy of Science Beijing CO 100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan CO 523000 China Hainan Zhongke Flower Ocean Cloud Commerce Technology Co. Ltd Haikou CO570311 Hainan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing CO 100190 China Qingdao Academy of Intelligent Industries Qingdao CO 266061 China Department of Industrial Engineering and Management Aalto University Aalto CO FI-00076 Finland

ISBN: (纸本)9781467384155

Short message is one of the most common communication media for mobile subscribers, so major mobile operators are devoted to improve their Short Message Service (SMS). However, the annoying and undesired messages, also named message spam or simply spam, not only worsen the users' experience, but also cause their complaints on SMS. In this paper, we present a novel Chinese SMS spam filtering framework based on AdaBoost algorithm to provide accurate and effective short messages classification. Three content-based weak filters are introduced to boost the performance of final classification decision. Results from Receiver Operating Characteristics (ROC) analysis prove the proposed method has such advantages as higher efficiency and fewer parameters over those established SMS spam filtering methods. The application of the proposed method is expected to block the most spam for mobile subscribers and improve the service quality of SMS. With simple data processing and few training parameters, the proposed method can be applied into the practice of short text classification.

关键词： Filtering Mobile communication Training Unsolicited electronic mail Classification algorithms Text categorization Bayes methods

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