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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Wenchao Xue Rafal Madonski Zhiqiang Gao Yi Huang Krzysztof Lakomy Key laboratory of Systems and Control Chinese Academy of Sciences Beijing P. R. China Chair of Control and Systems Engineering Poznan University of Technology Poland Department of Electrical and Computer Engineering Cleveland State University Cleveland Ohio U.S.A.

ISBN: (纸本)9781479983988

Active Disturbance Rejection control (ADRC) as a standalone motion solution has been adopted by companies such as Texas Instruments and Danfoss and made available on various proprietary industrial platforms. The idea of ADRC, however, can be integrated with the existing control technologies seamlessly, as shown in this paper. It is shown a modularized ADRC design for set-point tracking of motion control such that better uncertainty rejection can be implemented without any change in the existing proportional-derivative (PD) control with linear observer. We prove that certain integration of the observer's error can serve as an estimation for the “total disturbance” in low frequency range. This enables the estimation and cancellation of the “total disturbance” to be incorporated into the existing control loop. Also, a comparison between the methods with and without such “module” is discussed. The proposed ADRC is implemented and validated with experimental results for a 1-degree of freedom robotic manipulator, where desired set-point tracking performance in position control is achieved under unknown mass variations and sudden external disturbances.

关键词： Observers Motion control Tracking Transient analysis

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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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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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Synchronization of Lienard-type oscillators in uniform electrical networks

Synchronization of Lienard-type oscillators in uniform elect...

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American control Conference

作者： Mohit Sinha Florian Dorfler Brian B. Johnson Sairaj V. Dhople Department of Electrical and Computer Engineering at the University of Minnesota (UMN) Minneapolis United States of America Automatic Control Laboratory at ETH Zurich Switzerland Power Systems Engineering Center at the National Renewable Energy Laboratory (NREL) Golden CO United States of America

ISBN: (纸本)9781467386838

This paper presents a condition for global asymptotic synchronization of Lienard-type nonlinear oscillators in uniform LTI electrical networks with series R-L circuits modeling interconnections. By uniform electrical networks, we mean that the per-unit-length impedances are identical for the interconnecting lines. We derive conditions for global asymptotic synchronization for a particular feedback architecture where the derivative of the oscillator output current supplements the innate current feedback induced by simply interconnecting the oscillator to the network. Our proof leverages a coordinate transformation to a set of differential coordinates that emphasizes signal differences and the particular form of feedback permits the formulation of a quadratic Lyapunov function for this class of networks. This approach is particularly interesting since synchronization conditions are difficult to obtain by means of quadratic Lyapunov functions when only current feedback is used and for networks composed of series R-L circuits. Our synchronization condition depends on the algebraic connectivity of the underlying network, and reiterates the conventional wisdom from Lyapunov- and passivity-based arguments that strong coupling is required to ensure synchronization.

关键词： Oscillators Synchronization Integrated circuit interconnections Integrated circuit modeling Lyapunov methods Limit-cycles Mathematical model Oscillators integrated circuit interconnections integrated circuit modelling Lyapunov methods Liapunov function Electric circuits Power grids Mathematical Model current feedback

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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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An AlN-based piezoelectric micro-machined ultrasonic transducer (pMUT) array

An AlN-based piezoelectric micro-machined ultrasonic transdu...

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IEEE Conference on Nanotechnology

作者： Qifeng Zhu Tao Chen Huicong Liu Lining Sun Tao Wang Chengkuo Lee Xianhao Le Jin Xie Jiangsu Provincial Key Labratory of Advanced Robotics Soochow University Suzhou China Department of Electrical and Computer Engineering National University of Singapore Singapore State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China

The development of wireless energy transfer is critical to the promotion of implantable medical devices (IMDs). In this paper, an aluminum nitride (AlN) based piezoelectric micromachined ultrasonic transducer (pMUT) array is designed, fabricated and characterized, which can be a potential power supply for IMDs. The pMUT array is ideally suitable for integration with wireless-powered system since it is fabricated from miniaturized AlN membrane array of 4×4 units. Both the simulation and experiment are conducted to characterize the pMUT device. The transmitting sensitivity of the fabricated pMUT array is measured as 83 nm/V at a resonant frequency of 527 kHz.

关键词： Aluminum nitride III-V semiconductor materials Resonant frequency Sensitivity Ultrasonic transducers Acoustics Cavity resonators

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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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An analysis of sampling effect on bilateral teleoperation system transparency 34

An analysis of sampling effect on bilateral teleoperation sy...

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34th Chinese control Conference, CCC 2015

作者： Ting, Yang Fu, Yi Li Tavakoi, Mahdi State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin Heilongjiang China Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada

ISBN: (纸本)9789881563897

The performances of continuous-time controlled and discrete-time controlled bilateral teleoperation systems are mathematically and experimentally studied and compared. The results show that under stability conditions, continuous-time controlled teleoperation systems have hybrid matrix parameters that are closer to their ideal values than their discrete-time controlled counterparts. This means better force tracking and position tracking performance under continuous-time control, which can lead to better task performance. © 2015 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Bilateral teleoperation continuous-time control discrete-time control hybrid parameters system performance

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