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Chinese control Conference

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai China The Center for Bioinformatics and Computational Biology East China Normal University Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and high-dimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment decision. However, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model (SAE-SVM), for breast cancer diagnosis. The stacked auto-encoders with the greedy layer-wise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Breast cancer Feature extraction Machine learning Support vector machines Electronic mail Principal component analysis

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Probabilistic learning of target density distribution

Probabilistic learning of target density distribution

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AIAA Information Systems-Infotech At Aerospace Conference, 2017

作者： Cao, Yongcan Casbeer, David Ahmed, Nisar Kingston, Derek Department of Electrical and Computer Engineering University of Texas San AntonioTX78249 United States Control Science Center of Excellence Air Force Research Laboratory WPAFB OH45433 United States Aerospace Engineering Sciences University of Colorado BoulderCO80309 United States Control Science Center of Excellence Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (纸本)9781624104497

Localization of multiple moving targets is a fundamental issue in many military and civilian applications, such as search and rescue, military intelligence, surveillance, and reconnaissance, and force protection. The main challenge is to identify the location information of targets based on measurements from spatially distributed sensors. In this paper, we aim to address the problem of estimating the distribution of moving targets, i.e., density of targets, on a road network via a probabilistic learning approach. Our focus here is on the development of a probabilistic model to predict how the target density will evolve given the probabilistic dynamic motion of the targets on the road network. By discretizing the road network into small cells and considering the possible number of targets in each cell, we construct a state-dependent Hidden Markov models (HMM) based learning model that describes the state transition probability. The target density distribution is then estimated by computing the expected number of targets in each cell. The proposed formal method can also address the birth/death of targets. Finally, we discuss the accuracy of the models with respect to a few assumptions used in deriving the probabilistic model and some future research directions. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Formal methods

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Stability of Kalman filtering with a random measurement equation: Application to sensor scheduling with intermittent observations

arXiv

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arXiv 2018年

作者： Marelli, Damián Sui, Tianju Rohr, Eduardo Fu, Minyue School of Automation Guandong University of Technology Guangzhou China French-Argentinean International Center for Information and Systems Sciences National Scientific and Technical Research Council Rosario2000 Argentina School of Control Science and Engineering Dalian University of Technology Dalian China ABB Corporate Research Center Dättwil5405 Switzerland School of Electrical Engineering and Computer Science University of Newcastle University Drive CallaghanNSW2308 Australia

Studying the stability of the Kalman filter whose measurements are randomly lost has been an active research topic for over a decade. In this paper we extend the existing results to a far more general setting in which the measurement equation, i.e., the measurement matrix and the measurement error covariance, are random. Our result also generalizes existing ones in the sense that it does not require the system matrix to be diagonalizable. For this general setting, we state a necessary and a sufficient condition for stability, and address their numerical computation. An important application of our generalization is a networking setting with multiple sensors which transmit their measurements to the estimator using a sensor scheduling protocol over a lossy network. We demonstrate how our result is used for assessing the stability of a Kalman filter in this multi-sensor setting. Copyright © 2018, The Authors. All rights reserved.

关键词： Networked control systems

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A hybrid systems framework for multi agent task planning and control

A hybrid systems framework for multi agent task planning and...

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American control Conference

作者： Maria Guinaldo Dimos V. Dimarogonas Department of Computer Sciences and Automatic Control School of Computer Engineering UNED Madrid Spain ACCESS Linneaeus Center School of Electrical Engineering KTH Stockholm Sweden

ISBN: (纸本)9781509045839

In this paper, we investigate the coordination of a multi-agent system to achieve a high level plan. In, particular, we assume that this planning is decomposed in a sequence of simple control objectives. The coordination is carried out through event-based communications between the agents, and distributed control laws are synthesized to achieve each control objective according to a time-constraint. All the elements (planning, motion, and communication) are integrated in a hybrid systems framework, which also allows to conclude about the achievement of the high level plan.

关键词： hybrid systems Agents Multi-agent systems Communication Research Planning Standing Orders Frameworks communication between animals Event-based

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Consensus Problem in General Linear Multiagent Systems Under Stochastic Topologies

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IFAC-PapersOnLine 2016年第13期49卷 13-18页

作者： Rezaee, Hamed Abdollahi, Farzaneh The Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Concordia University MontrealQuebec Canada

Consensus control of a class of multiagent systems with general linear dynamics is studied. Based on solution of some linear matrix inequalities, a protocol is obtained which guarantees achieving consensus among agents in the presences of stochastically switching topologies. By invoking the concept super-martingales, it is shown that if the probability of the network connectivity is not zero, the agents reach almost sure consensus upon their state vectors. Despite existing results in the literature for consensus control of general linear multiagent systems under stochastic networks, the proposed consensus protocol in this paper requires no knowledge on the set of feasible topologies, and this issue significantly decreases the design computational costs. Simulation results for a diving consensus problem among a team of autonomous underwater vehicles validate the proposed consensus protocol. © 2016

关键词： Multi agent systems Autonomous underwater vehicles Linear matrix inequalities Stochastic systems Topology Almost sure consensus Computational costs Consensus problems Consensus protocols Linear dynamics Network connectivity Of autonomous underwater vehicles Stochastic networks

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Active disturbance rejection control： between the formulation in time and the understanding in frequency

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control Theory and Technology 2016年第3期14卷 250-259页

作者： Qing ZHENG Zhiqiang GAO Department of Electrical and Computer Engineering Gannon University Erie PA 16541 U.S.A. Center for Advanced Control Technologies Department of Electrical and Computer Engineering Cleveland State University Cleveland OH 44115 U.S.A.

With the rapid deployments of the active disturbance rejection control （ADRC） as a bonafide industrial technology in the background, this paper summarizes some recent results in the analysis of linear ADRC and offers explanations in the frequency response language with which practicing engineers are familiar. Critical to this endeavor is the concept of bandwidth, which has been used in a more general sense. It is this concept that can serve as the link between the otherwise opaque state space formulation of the ADRC and the command design considerations and concerns shared by practicing engineers. The remarkable characteristics of a simple linear ADRC was first shown in the frequency domain, followed by the corresponding analysis in time domain, where the relationship between the tracking error and the ADRC bandwidth is established. It is shown that such insight is only possible by using the method of solving linear differential equations, instead of the more traditional techniques such as the Lyapunov methods, which tend to be more conservative and difficult to grasp by engineers. The insight obtained from such analysis is further demonstrated in the simulation validation.

关键词： Active disturbance rejection control extended state observer uncertain systems stability bandwidth

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Topological-darkness-assisted phase regulation for atomically thin meta-optics

arXiv

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arXiv 2019年

作者： Wang, Yingwei Deng, Zi-Lan Hu, Dejiao Yuan, Jian Ou, Qingdong Qin, Fei Zhang, Yinan Ouyang, Xu Peng, Bo Cao, Yaoyu Guan, Bai-Ou Zhang, Yupeng He, Jun Qiu, Chengwei Bao, Qiaoliang Li, Xiangping Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics Technology Jinan University Guangzhou510632 China Hunan Key Laboratory for Super-microstructure and Ultrafast Process School of Physics and Electronics Central South University 932 South Lushan Road Changsha Hunan410083 China College of Physics and Electronic Information Huaibei Normal University Huaibei235000 China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Electronic Science and Technology Shenzhen University Shenzhen518060 China Monash University ClaytonVIC3800 Australia National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Films and Integrated Devices School of Microelectronics and Solid State Electronics University of Electronic Science and Technology of China Chengdu610054 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore

Two-dimensional (2D) noble-metal dichalcogenides 1, 2 have emerged as a new platform for the realization of versatile flat optics with a considerable degree of miniaturization. However, light field manipulation at the atomic scale is widely considered unattainable since the vanishing thickness and intrinsic losses of 2D materials completely suppress both resonances and phase accumulation effects. Empowered by conventionally perceived adverse effects of intrinsic losses, we show that the structured PtSe2 films integrated with a uniform substrate can regulate nontrivial singular phase and realize atomic-thick meta-optics in the presence of topological darkness 3, 4. We experimentally demonstrate a series of atomic-thick binary meta-optics that allows angle-robust and high unit-thickness diffraction efficiency of 0.96%/nm in visible frequencies, given its thickness of merely 4.3 nm. Our results unlock the potential of a new class of 2D flat optics for light field manipulation at an atomic thickness. Copyright © 2019, The Authors. All rights reserved.

关键词： Platinum compounds

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New results in global stabilization for stochastic nonlinear systems

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control Theory and Technology 2016年第1期14卷 57-67页

作者： Tao BIAN Zhong-Ping JIANG Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University 5 Metrotech Center Brooklyn NY 11201 U.S.A.

This paper presents new results on the robust global stabilization and the gain assignment problems for stochastic nonlinear systems. Three stochastic nonlinear control design schemes are developed. Furthermore, a new stochastic gain assignment method is developed for a class of uncertain interconnected stochastic nonlinear systems. This method can be combined with the nonlinear small-gain theorem to design partial-state feedback controllers for stochastic nonlinear systems. Two numerical examples are given to illustrate the effectiveness of the proposed methodology.

关键词： Stochastic nonlinear systems stochastic input-to-output stability stabilization gain assignment small-gain

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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第37届中国控制会议

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of Education The Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Charles L.Brown Department of Electrical and Computer Engineering University of Virginia School of Biomedical Engineering Shanghai Jiao Tong University

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and highdimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment ***, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model（SAE-SVM）, for breast cancer diagnosis. The stacked auto-encoders with the greedy layerwise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Deep learning auto-encoder feature extraction breast cancer diagnosis unlabeled data

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Benchmarking quantum processors with a single qubit

arXiv

引用

arXiv 2019年

作者： Göktaş, Oktay Tham, W.K. Bonsma-Fisher, Kent Brodutch, Aharon Agnostiq Labs 100 College St TorontoONM5G 1L5 Canada Department of Physics and Center for Quantum Information and Quantum Control University of Toronto 60 St George St TorontoONM5S 1A7 Canada National Research Council of Canada 100 Sussex Dr. OttawaONK1A 0R6 Canada Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto 10 King's College Road TorontoONM5S 3G4 Canada

The first generation of small noisy quantum processors have recently become available to non-specialists who are not required to understand specifics of the physical platforms and, in particular, the types and sources of noise. As such, it is useful to benchmark the performance of such computers against specific tasks that may be of interest to users, ideally keeping both the circuit depth and width as free parameters. Here we benchmark the IBM Quantum Experience using the Deterministic Quantum Computing with 1 qubit (DQC1) algorithm originally proposed by Knill and Laflamme in the context of liquid state NMR. In the first set of experiments we use DQC1 as a trace estimation algorithm to produce visibility plots. In the second set we use this trace estimation algorithm to distinguish between knots, a classically difficult task which is known to be complete for DQC1. Our results indicate that the main limiting factor is the length of the circuit, and that both random and systematic errors become an issue when the gate count increases. Surprisingly, we find that at the same gate count wider circuits perform better, probably due to randomization of coherent errors. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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