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Optimal control of quantum systems with SU(1,1)dynamical symmetry

control Theory and Technology 2015年第3期13卷 211-220页

作者： Wenbin DONG Rebing WU Jianwu WU Chunwen LI Tzyh-Jong TARN Department of Automation Tsinghua University Center for Quantum Information Science and Technology Tsinghua National Laboratory for Information Science and Technology(TNlist) Beijing Aerospace Automatic Control Institute Electrical and Systems Engineering Department Washington University in St.Louis

SU（1,1） dynamical symmetry is of fundamental importance in analyzing unbounded quantum systems in theoretical and applied physics. In this paper, we study the control of generalized coherent states associated with quantum systems with SU（1,1） dynamical symmetry. Based on a pseudo Riemannian metric on the SU（1,1） group, we obtain necessary conditions for minimizing the field fluence of controls that steer the system to the desired final state. Further analyses show that the candidate optimal control solutions can be classified into normal and abnormal extremals. The abnormal extremals can only be constant functions when the control Hamiltonian is non-parabolic, while the normal extremals can be expressed by Weierstrass elliptic functions according to the parabolicity of the control Hamiltonian. As a special case, the optimal control solution that maximally squeezes a generalized coherent state is a sinusoidal field, which is consistent with what is used in the laboratory.

关键词： Quantum control optimal control dynamical symmetry

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Variable selection in identification of a high dimensional nonlinear non-parametric system

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control Theory and Technology 2015年第1期13卷 1-16页

作者： Er-Wei BAI Wenxiao ZHAO Weixing ZHENG Department of Electrical and Computer Engineering University of Iowa School of Electronics Electrical Engineering and Computer Science Queen's UniversityBelfast U.K. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences School of Computing Engineering and Mathematics University of Western Sydney

The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.

关键词： System identification variable selection nonlinear non-parametric system curse of dimensionality

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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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Event-based state estimation of discrete-state hidden Markov models

Event-based state estimation of discrete-state hidden Markov...

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作者： Shi, Dawei Elliott, Robert J. Chen, Tongwen State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Mathematical Sciences University of Adelaide SA5005 Australia Haskayne School of Business University of Calgary CalgaryAB Canada Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada

The state estimation problem for hidden Markov models subject to event-based sensor measurement updates is considered in this work, using the change of probability approach. We assume the measurement updates are transmitted through wired or wireless communication networks. For the scenarios with reliable and unreliable communication channels, analytical expressions for the probability distributions of the states conditioned on all the past point- and set-valued measurement information are obtained. Also, we show that the scenario with a lossy channel, but without the event-trigger, can be treated as a special case of the reliable channel results. Based on these results, closed-form expressions for the estimated communication rates under the original probability measure are presented, which are shown to be the ratio between a weighted 1-norm and the 1-norm of the unnormalized conditional probability distributions of the states under the new probability measures constructed. Implementation issues are discussed, and the effectiveness of the results is illustrated by numerical examples and comparative simulations. © 2015 Elsevier Ltd. All rights reserved.

关键词： Hidden Markov models

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control on a molecular scale: A perspective

Control on a molecular scale: A perspective

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American control Conference

作者： Rolf Findeisen Martha A. Grover Christian Wagner Michael Maiworm Ruslan Temirov F. Stefan Tautz Murti V. Salapaka Srinivasa Salapaka Richard D. Braatz S.O. Reza Moheimani Otto-von-Guericke-Universit?t Magdeburg Laboratory for Systems Theory and Automatic Control Germany School of Chemical & Biomolecular Engineering Georgia Institute of Technology USA Peter Gruenberg Institute (PGI-3) Juelich Research Center Germany Electrical and Computer Engineering Department University of Minnesota USA Massachusetts Institute of Technology USA Department of Mechanical Engineering The University of Texas at Dalla USA

ISBN: (纸本)9781467386838

Imaging and manipulating objects down to the molecular level plays a crucial role in many disciplines. It allows to unravel molecular phenomena, to form materials with new chemical and physical properties, or to build objects on an atomic scale. Science on the nanoscale is inherently interdisciplinary. It requires knowledge and insight into many fields, spanning from modeling, measuring, imaging, actuation to control. Often feedback control facilitates operation with molecular precision, despite the fact that many physical phenomena at the molecular level are still not well understood, and that stochastic and nonlinear effects are inherently present. This work provides an insight into some of the current control related research activities on a molecular scale. This is done considering examples from different fields: control related to scanning probe microscopy such as atomic force microscopy, controlled self assembly on a molecular scale, control aspects of molecular transport, and the use of control for manipulation of single molecules using macroscopic probe tips. The hope is that control related researchers, who are not experts in these fields, become aware of the opportunities present, which could also drive new theoretical developments.

关键词： Computational modeling Mathematical model Force Atomic force microscopy Numerical models Computational modeling Atomic Force Microscopy numerical models Mathematical Model Force Standing Orders Scale formation Insight Transport Process Physical Phenomena Scanning Probe Microscopy Hopefulness Research Activities chemical and physical properties feedback control

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ACP-based social computing and parallel intelligence： Societies 5.0 and beyond

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CAAI Transactions on Intelligence Technology 2016年第4期1卷 377-393页

作者： XiaoWang Lingxi Li Yong Yuan Peijun Ye Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Qingduo Academy of Intelligent Industries Qingduo 266100 China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN 46202 USA

Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.

关键词： Social computing Societies 5.0 Parallel intelligence Knowledge automation Cyber-physical-social system Artificial societies Computational ex-periments Parallel execution

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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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Nadzoru: A Software Tool for Supervisory control of Discrete Event systems

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IFAC-PapersOnLine 2015年第7期48卷 182-187页

作者： Pinheiro, Lucas Preischadt Lopes, Yuri Kaszubowski Leal, André Bittencourt Rosso Junior, Roberto Silvio Ubertino Department of Computer Science Santa Catarina State University Joinville-SC Brazil Department of Automatic Control and Systems Engineering Sheffield Robotics University of Sheffield Sheffield South Yorkshire United Kingdom Department of Electrical Engineering Santa Catarina State University Joinville-SC Brazil

This article presents a software tool for the synthesis of supervisory control based on untimed finite state automata. The tool has a friendly graphical interface for the creation of models and, among other features, allows: automata simulation;analysis of problems related to the implementation of the control logic;and code generation for multiple devices. The code generation is based on a template system that allows the easy inclusion of support for new devices. Currently the tool is able to generate code for some microcontrollers and programmable logic controllers. Another feature of the tool is the capability of being easily extended with new algorithms and modules. © 2015

关键词： Discrete event simulation

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Corrigendum to “Road detection algorithm for Autonomous Navigation systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

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Robotics and Autonomous systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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Recursive Identification of Hammerstein With Noisy Observations

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IFAC-PapersOnLine 2015年第28期48卷 1017-1022页

作者： Mu, Biqiang Han-Fu, Chen Wang, Le Yi Yin, George Key Laboratory of Systems and Control Institute of Systems Science AMSS Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Wayne State University Detroit MI48202 United States Department of Mathematics Wayne State University Detroit MI48202 United States

The paper considers the recursive identification of Hammerstein systems with noisy output, while its input may or may not be corrupted by noise. The system in the latter case is usually called as EIV system. The conditions required in this paper are considerably weaker than those used in previous works, e.g., the orders of the linear subsystem are allowed to be unknown and no additional conditions are imposed on its moving average part. The nonlinearity is general in the sense that a certain class of functions is out of consideration in some previous papers. In the paper, the almost sure convergence together with convergence rate are established for the estimates for coefficients of the linear part, and then the almost sure convergence for the estimates for the nonlinearity at any given points are derived by using kernel functions. The convergence rate for the nonlinearity is also obtained for the case where the system input is available without noise. A numerical example is provided, and the simulation results are consistent with the theoretical analysis. © 2015

关键词： control nonlinearities Nonlinear systems Numerical methods Almost sure convergence Convergence rates Errors in variables Hammerstein system Kernel function Linear subsystems Noisy observations Recursive identification

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