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International Workshop on Variable Structure Systems, VSS

作者： M. Reichhartinger S. Koch H. Niederwieser S.K. Spurgeon Institute of Automation and Control Graz University of Technology Graz Austria Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Graz University of Technology Graz Austria Information and Computer Engineering Graz University of Technology Graz Austria Department of Electronic & Electrical Engineering University College London London UK

This paper presents a new release of A Robust Exact Differentiator Toolbox for Matlab ® /Simulink ® proposed in [1]. This release features a new discrete-time realization of the continuous-time robust exact differentiator. The implemented discretization scheme is less sensitive to gain overestimation and does not suffer from the discretization chattering effect. Hence, the single tuning parameter of the new version of the implemented differentiator is more intuitive to tune. Furthermore, it shows superior estimation performance in the case of large sampling times in comparison to the previous release. This is confirmed by the presented results obtained by numerical simulations and a real world application.

关键词： Eigenvalues and eigenfunctions Tuning Estimation error Robustness Mathematical model Real-time systems

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Midterm periodicity analysis of the mount wilson magnetic indices using the Synchrosqueezing transform

arXiv

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arXiv 2018年

作者： Feng, Song Yu, Lan Wang, Feng Deng, Hui Yang, Yunfei Yunnan Key Lab. of Computer Technology Application/Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Cas Key Laboratory of Solar Activity National Astronomical Observatories Beijing100012 China Department of Mechanical and Electrical Engineering Yunnan Land and Resources Vocational College Kunming650217 China

A novel time-frequency technique, called the synchrosqueezing transform (SST), is used to investigate the midterm periodic variations of magnetic fields on the solar surface. The Magnetic Plage Strength Index (MPSI) and the Mount Wilson Sunspot Index (MWSI), measured daily by the Mount Wilson Observatory between 1970 January 19 and 2012 January 22, are selected. The short-, mid, and longer-term periodicities are represented and decomposed by the SST with hardly any mode mixing. This demonstrates that the SST is a useful time-frequency analysis technique to characterize the periodic modes of helioseismic data. Apart from the fundamental modes of the annual periodicity, ∼27 day rotational cycle and ∼11 year solar cycle, the SST reveals several midterm periodicities in the two magnetic activity indices, specifically, ∼157 day (i.e., Rieger-type periodicity), and ∼1.3 and 1.7 years. The periodic modes, with 116.4 and 276.2 day periodicity in the MPSI, with 108.5 and 251.6 day periodicity in the MWSI, and the 157.7 day periodicity in the two indices, are in better accord with those significant periodicities derived from the Rossby waves theoretical model. This study suggests that the modes are caused by the Rossby waves. For the 1.30 and 1.71 year periodicity of the MPSI, and the 1.33 and 1.67 year periodicity of the MWSI, our analysis infers that they are related to those periodicity with the same timescale in the interior of the Sun and in the high atmospheric layers. Copyright © 2018, The Authors. All rights reserved.

关键词： Mechanical waves

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Erratum to “A two-stage evaluation and optimization method for renewable energy development based on data envelopment analysis” [Appl. Energy 262 (2020) 114363]

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Applied Energy 2020年 267卷

作者： Yuan Zeng Waiying Guo Hongmei Wang Fengbin Zhang Key Laboratory of Smart Grid of Ministry of Education Tianjin University 92 Weijin Road Tianjin 300072 China State Grid Nanchang Electric Power Supply Company Nanchang 330096 China School of Electrical Computer Engineering Cornell University Ithaca NY 14850 USA School of Basic Medical Sciences Tianjin Medical University 22 Qixiangtai Road Tianjin 300070 China Power Automation Department China Electric Power Research Institute Beijing 100192 China

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Distributed Least Squares Solver for Network Linear Equations

arXiv

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arXiv 2018年

作者： Yang, Tao George, Jemin Qin, Jiahu Yi, Xinlei Wu, Junfeng State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China U.S. Army Research Laboratory AdelphiMD20783 United States Department of Automation University of Science and Technology of China Hefei230027 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In this paper, we study the problem of finding the least square solutions of over-determined linear algebraic equations over networks in a distributed manner. Each node has access to one of the linear equations and holds a dynamic state. We first propose a distributed least square solver over connected undirected interaction graphs and establish a necessary and sufficient on the step-size under which the algorithm exponentially converges to the least square solution. Next, we develop a distributed least square solver over strongly connected directed graphs and show that the proposed algorithm exponentially converges to the least square solution provided the step-size is sufficiently small. Moreover, we develop a finite-time least square solver by equipping the proposed algorithms with a finite-time decentralized computation mechanism. The theoretical findings are validated and illustrated by numerical simulation examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Linear equations

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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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Multi-view hybrid embedding: A divide-and-conquer approach

arXiv

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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A Novel Autonomous 5-D Hyperjerk RC Circuit with Hyperbolic Sine Function

Scientific World Journal

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Scientific World Journal 2018年第1期2018卷 1260325页

作者： Tsafack, N. Kengne, J. Research Unit of Laboratory of Automation and Applied Computer (LAIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Department of Physics Faculty of Sciences University of Dschang P.O. Box 67 Dschang Cameroon

A novel autonomous 5-D hyperjerk RC circuit with hyperbolic sine function is proposed in this paper. Compared to some existing 5-D systems like the 5-D Sprott B system, the 5-D Lorentz, and the Lorentz-like systems, the new system is the simplest 5-D system with complex dynamics reported to date. Its simplicity mainly relies on its nonlinear part which is synthetized using only two semiconductor diodes. The system displays only one equilibrium point and can exhibit both periodic and chaotic dynamical behavior. The complex dynamics of the system is investigated by means of bifurcation analysis. In particular, the striking phenomenon of multistability is revealed showing up to seven coexisting attractors in phase space depending solely on the system's initial state. To the best of author's knowledge, this rich dynamics has not yet been revealed in any 5-D dynamical system in general or particularly in any hyperjerk system. Pspice circuit simulations are performed to verify theoretical/numerical analysis. © 2018 N. Tsafack and J. Kengne.

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NANOROBOTS: Intracellular manipulation and measurement with multipole magnetic tweezers

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Science robotics 2019年第28期4卷

作者： Wang, X. Ho, C. Tsatskis, Y. Law, J. Zhang, Z. Zhu, M. Dai, C. Wang, F. Tan, M. Hopyan, S. McNeill, H. Sun, Y. Department of Mechanical and Industrial Engineering University of Toronto TorontoONM5S 3G8 Canada Institute of Biomaterials and Biomedical Engineering TorontoONM5S 3G9 Canada Lunenfeld-Tanenbaum Research Institute Mt. Sinai Hospital TorontoONM5G 1X5 Canada Program in Developmental and Stem Cell Biology Hospital for Sick Children TorontoONM5G 1X8 Canada HRG Central Institute of Robotics HIT Robot Group Harbin Heilongjiang150001 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Division of Orthopaedic Surgery Hospital for Sick Children and University of Toronto TorontoONM5G 1X8 Canada Department of Developmental Biology Washington University School of Medicine St. LouisMO63108 United States Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

The capability to directly interrogate intracellular structures inside a single cell for measurement and manipulation is important for understanding subcellular and suborganelle activities, diagnosing diseases, and developing new therapeutic approaches. Compared with measurements of single cells, physical measurement and manipulation of subcellular structures and organelles remain underexplored. To improve intracellular physical measurement and manipulation,we have developed amultipole magnetic tweezers system for micromanipulation involving submicrometer position control and piconewton force control of a submicrometermagnetic bead inside a single cell for measurement in different locations (spatial) and different time points (temporal). The beadwas three-dimensionally positioned in the cell using a generalized predictive controller that addresses the control challenge caused by the low bandwidth of visual feedback from high-resolution confocal imaging. The average positioning error was quantified to be 0.4 mm, slightly larger than the Brownianmotion-imposed constraint (0.31 mm). The system is also capable of applying a force up to 60 pN with a resolution of 4 pN for a period of time longer than 30 min. The measurement results revealed that significantly higher stiffness exists in the nucleus' major axis than in the minor axis. This stiffness polarity is likely attributed to the aligned actin filament. We also showed that the nucleus stiffens upon the application of an intracellularly applied force, which can be attributed to the response of structural protein lamin A/C and the intracellular stress fiber actin filaments. © 2019 The Authors.

关键词： Cells

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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...

引用

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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An Intelligent Gripper Design for Autonomous Aerial Transport with Passive Magnetic Grasping and Dual-Impulsive Release

An Intelligent Gripper Design for Autonomous Aerial Transpor...

引用

IEEE/ASME International Conference on Advanced Intelligent Mechatronics

作者： Usman A. Fiaz M. Abdelkader Jeff S. Shamma Department of Electrical & Computer Engineering University of Maryland(UMD) College Park. MD USA KAUST The Robotics Intelligent Systems & Control (RISC) Laboratory Thuwal KSA

We present a novel gripper design for autonomous aerial transport of ferrous objects with unmanned aerial vehicles (UAVs). The proposed design uses permanent magnets for grasping, and a novel dual-impulsive release mechanism, for achieving drop. The gripper can simultaneously lift up to four objects of arbitrary shape, in fully autonomous mode, with a 100% rate of successful drops. We optimize the system subject to realistic constraints, such as the simplicity of design and its sturdiness to aerial maneuvers, payload limits for multi-rotor UAVs, reliability of autonomous grasping irrespective of the environment of operation, active power consumption of the gripper, and its comparison with the existing technologies. We describe the design concepts, and the hardware, and perform extensive experiments in both indoor and outdoor environments, with two multi-rotor configurations. Several results, showcasing superior performance of the proposed system are provided as well.

关键词： Grasping Force Servomotors Grippers Payloads Torque Magnetic separation

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