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IEEE International Conference on Robotics and Automation

作者： Alexis du Plessis d'Argentre Samuel Perry Yoshitaka Iwata Haruna Iwasaki Eiji Iwase Assunta Fabozzo Iain Will Daniela Rus Dana D. Damian Shuhei Miyashita Ecole Supérieure d'Electronique de l'Ouest France Department of Electronic Engineering University of York York UK Department of Applied Mechanics Waseda University Japan Hospital of Padua Italy Computer Science and Artificial Intelligence Laboratory MIT USA Department of Automatic Control and System Engineering University of Sheffield UK

Gastric ulcer is a chronic and complex (and often complete) erosion of the stomach wall that happens as a complication of a previous chronic, inflammatory process. It represents a catastrophic situation in which the patient is critical and its conditions need to be treated fast. This study presents a remotely navigatable and deployable ingestible patch and plug for gastric ulcer treatment. The patch/plug structure is made of agarose hydrogel that can change rigidity through hydration and dehydration. When dehydrated, it is rigid and can maintain a folded configuration so it can be ingested as a "pill". This can be guided to the targeted location by a magnetic field, and be deployed instantly by hydration, namely by supplying water from the mouth. Due to the deployable origami design, it exhibits an expansion of 10 times its initial surface area, making the device suitable for the use of dressing a surface as a patch, and filling a hole as a plug.

关键词： Plugs Stomach Navigation Shape Fabrication Robot sensing systems

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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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Arc-support line segments revisited: An efficient high-quality ellipse detection

arXiv

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arXiv 2018年

作者： Lu, Changsheng Xia, Siyu Shao, Ming Fu, Yun Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China School of Automation Southeast University Nanjing210096 China Department of Computer and Information Science University of Massachusetts DartmouthMA02747 United States Department of ECE Northeastern University BostonMA02115 United States

Over the years many ellipse detection algorithms spring up and are studied broadly, while the critical issue of detecting ellipses accurately and efficiently in real-world images remains a challenge. In this paper, we propose a valuable industry-oriented ellipse detector by arc-support line segments, which simultaneously reaches high detection accuracy and efficiency. To simplify the complicated curves in an image while retaining the general properties including convexity and polarity, the arc-support line segments are extracted, which grounds the successful detection of ellipses. The arc-support groups are formed by iteratively and robustly linking the arc-support line segments that latently belong to a common ellipse. Afterward, two complementary approaches, namely, locally selecting the arc-support group with higher saliency and globally searching all the valid paired groups, are adopted to fit the initial ellipses in a fast way. Then, the ellipse candidate set can be formulated by hierarchical clustering of 5D parameter space of initial ellipses. Finally, the salient ellipse candidates are selected and refined as detections subject to the stringent and effective verification. Extensive experiments on three public datasets are implemented and our method achieves the best F-measure scores compared to the state-of-the-art methods. The source code is available at https://***/AlanLuSun/High-quality-ellipse-detection. Copyright © 2018, The Authors. All rights reserved.

关键词： Geometry

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Regional pole placement and feedback linearization of nonlinear wind turbine control system

Regional pole placement and feedback linearization of nonlin...

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American control Conference

作者： Zhongwei Lin Zhenyu Chen Xiaoming Li the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing 102206 P. R. China the School of Automation Engineering Northeast Electric Power University Jilin 132012 P. R. China

ISBN: (纸本)9781509045839

This paper combines the regional pole placement technique with the geometric control theory to design the feedback control for the nonlinear wind turbine generator system (WTGS), which can improve the dynamic response efficiently. Through constructing the appropriate diffeomorphism and feedback linearization, the key variables of the original nonlinear WTGS can be remained in the transformed linear-form system, which sustains that the dynamic response could be adjusted in the transformed linear framework. Due to the multi-input multi-output (MIMO) and high-order characteristics, the regional pole placement technique is then applied on the feedback linearized WTGS, which can guarantee the limitation of several performances related to the dynamic response. In such a way, the dynamic response of WTGS can be adjusted through the feedback linearization and regional pole placement techniques. Simulation results show the effectiveness of our results.

关键词： Feedback Linearization Nonlinear

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Structural Performance Analysis of Wall Climbing Robot

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IOP Conference Series: Materials Science and Engineering 2019年第4期569卷

作者： Li Huang Shangchun Liao College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan 430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan 430081 China

With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life. However, the development of wall climbing robot technology is still in the laboratory research and small-scale application. We live in a world where progress is continuing. Large-scale buildings, bridges and ships are becoming more and more common. In these places, it is inevitable to involve the construction, maintenance and clarity of high-rise buildings and ships. In the case of dangerous and inefficient manpower work, the application of wall climbing robots can play a very good role. Therefore, the development of wall climbing robots is of vital importance both now and in the future. Starting from the performance characteristics of wall climbing robot, this paper studies and summarizes the moving mode, control mode, conditions to be satisfied and various adsorption forms of wall climbing robot, and introduces the basic research situation in the field of wall climbing robot.

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Synchronized Cell-Balancing Charging of Supercapacitors

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IFAC-PapersOnLine 2017年第1期50卷 3338-3343页

作者： Li H. Peng J. He J. Huang Z. Pan J. School of Information Science and Engineering Central South University Changsha 410087 China Department of Computer Science University of Victoria Victoria V8P5C2 Canada Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai 200240 China

As a classical cell balancing solution in low-power supercapacitor applications, the switched resistor circuit is vulnerable to resistance deviation effects with existing cell balancing methods. In this paper, we propose a synchronized cell balancing method for switched resistor circuits based on the consensus theory and a cyber-physical approach, with the aim of synchronizing cell voltages before they are fully charged. It is shown that the proposed cell-balancing algorithm is robust to resistance deviation effects. Extensive simulations are provided to show the effectiveness of the proposed method. © 2017

关键词： Cell balancing Charging Consensus Cyber-physical systems Supercapacitors

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Corrigendum to “On Fixed Point Property under Lipschitz and Uniform Embeddings”

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Journal of Function Spaces 2019年第1期2019卷

作者： Jichao Zhang Lingxin Bao Lili Su School of Science Hubei University of Technology Wuhan 430068 *** School of Computer and Information Fujian Agriculture and Forestry University Fuzhou 350002 *** Key Laboratory of System and Control Academy of Mathematics and Systems Science Academia Sinica Beijing 100190 *** Engineering University of The Chinese People’s Armed Police Force Xian 710086 China

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Design of Process control system in Water Cooling Processing Interaction Tank using Integral control and Robust control in The Tire Industry

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IOP Conference Series: Materials Science and Engineering 2018年第1期384卷

作者： D Handaya P Bakti Y P Nugraha Electronic Engineering Politeknik Gajah Tunggal Jl. Gatot Subroto KM. 7 Tangerang 15135 Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Science Gedung 417 Jl. Kawasan Puspitek Muncul Setu Tangerang Selatan 15314 Indonesia Control System and Computer Laboratory School of Electrical Engineering and Informatics Bandung Institute of Technology Jl. Ganesha no. 10 Bandung 40132 Indonesia

This laboratory based research build a process control system as water level scanner in interaction tank used for water cooling. The control valve parameter based on water level between tanks are interacted. The control method of tank valve ratio choosed in this reasearch is integral control and robust control. The desired control specification for maximum overshoot is 10%, peak time is 1 second, and error steady state is 0. The design process executed in physic system modelling step and system design criteria. Experiment executed using Matlab Script and Matlab Simulink. The simulation result will be compared to see an effective performance for processing of water cooling interaction tank control method. The design result for integral control can tracking reference accord to system design criteria and robust control can tracking reference without different experience either the system without disturbance or the system with disturbance.

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Data Stream Online Clustering Based on Fuzzy Expectation-Maximization Approach

Data Stream Online Clustering Based on Fuzzy Expectation-Max...

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IEEE International Conference on Data Stream Mining & Processing (DSMP)

作者： Anastasiia O. Deineko Polina Ye. Zhernova Boris Gordon Oleksandr O. Zayika Iryna Pliss Nelya Pabyrivska Artificial Intelligence Department Kharkiv National University of Radio Electronics Kharkiv Ukraine System Engineering Department Kharkiv National University of Radio Electronics Kharkiv Ukraine Computer Systems Department Tallinn University of Technology Tallinn Estonia Control Systems Research Laboratory Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Mathematics Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9781538628751

In the paper the online fuzzy clustering recurrent procedure has been introduced that allows the forming of hyperellipsoidal clusters with an arbitrary orientation of the axes is proposed. Such clustering system is the generalization of a number of known algorithms, it is intended to solve tasks within the general problems of Data Stream Mining (DSM) and Dynamic Data Mining (DDM), when information is sequentially fed to processing in online mode.

关键词： Data mining Clustering algorithms Task analysis Linear programming Probabilistic logic Minimization Neural networks

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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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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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