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23rd IEEE International Conference on Automation and Computing, ICAC 2017

作者： Wang, Licheng Wang, Zidong Wei, Guoliang Shanghai Key Lab of Modern Optical System Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Computer Science Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom

ISBN: (纸本)9780701702618

In this paper, the variance-constrained finite-horizon state estimation problem is studied for a class of discrete time-varying systems with an event-triggering mechanism. An event-based transmission scheme is employed during the data communication from the sensor to the estimator with hope to reduce the network burden and energy consumption. Furthermore, redundant channels are utilized to enable the data delivery service to be more reliable in a networked environment. Attention is fixed on the design of a time-varying state estimator such that, in the presence of external disturbances and probabilistic packet dropouts, the estimation error variance achieves the prescribed constraint over a finite-horizon. By means of the mathematical induction method, sufficient conditions are put forward to ensure the error variance is bounded within a prescribed upper bound at each sampling instant. A numerical example is provided to illustrate the usefulness of the proposed estimator design scheme. © 2017 Chinese Automation and Computing Society in the UK - CACSUK.

关键词： State estimation

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An optimized energy management strategy for fuel cell hybrid vehicles

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IOP Conference Series: Materials Science and engineering 2019年第4期612卷

作者： Wenxin Xiao Lei Wang Dongyin Liu Wenwen Zhang Chinese-German College Tongji University Shanghai China Department of Electrical and Computer Engineering Technische Universität München Munich Germany Research Center of Control Science and Intelligent System University of Electronic Science and Technology of China Chengdu China College of Electronics and Information Engineering Tongji University Shanghai China

Fuel cell hybrid vehicles are environmentally friendly and have good development prospects. Their energy management is very important, but there are currently few studies on this topic. In this paper, an optimized energy management strategy specifically applied to fuel cell hybrid vehicles is proposed. Fuel cell hybrid vehicles have some different characteristics from ordinary hybrid vehicles, which need to be considered in energy management. For the fuel cell to remain activated during driving, its output power must remain above a minimum power. In addition, it takes some time for the fuel cell to start up. These characteristics are considered in the vehicle model. The energy management strategy proposed in this paper maximizes energy use efficiency by controlling the SOC value of the battery. The SOC value of the battery is in the proper range during driving. At high speeds, fuel is consumed as a source of energy and the battery is charged to keep its SOC at a high level. When approaching the destination or when the vehicle is traveling at a low speed, the battery is discharged to reduce fuel consumption. Applying this strategy to the ECE-15 and EUDCL environments, the simulation results show that this optimized energy management strategy improves the energy efficiency of the fuel cell and enables the battery to operate at the appropriate power.

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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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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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Author Correction: Enhancing untargeted metabolomics using metadata-based source annotation

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Nature biotechnology 2023年第11期41卷 1656页

作者： Julia M Gauglitz Kiana A West Wout Bittremieux Candace L Williams Kelly C Weldon Morgan Panitchpakdi Francesca Di Ottavio Christine M Aceves Elizabeth Brown Nicole C Sikora Alan K Jarmusch Cameron Martino Anupriya Tripathi Michael J Meehan Kathleen Dorrestein Justin P Shaffer Roxana Coras Fernando Vargas Lindsay DeRight Goldasich Tara Schwartz MacKenzie Bryant Gregory Humphrey Abigail J Johnson Katharina Spengler Pedro Belda-Ferre Edgar Diaz Daniel McDonald Qiyun Zhu Emmanuel O Elijah Mingxun Wang Clarisse Marotz Kate E Sprecher Daniela Vargas-Robles Dana Withrow Gail Ackermann Lourdes Herrera Barry J Bradford Lucas Maciel Mauriz Marques Juliano Geraldo Amaral Rodrigo Moreira Silva Flavio Protasio Veras Thiago Mattar Cunha Rene Donizeti Ribeiro Oliveira Paulo Louzada-Junior Robert H Mills Paulina K Piotrowski Stephanie L Servetas Sandra M Da Silva Christina M Jones Nancy J Lin Katrice A Lippa Scott A Jackson Rima Kaddurah Daouk Douglas Galasko Parambir S Dulai Tatyana I Kalashnikova Curt Wittenberg Robert Terkeltaub Megan M Doty Jae H Kim Kyung E Rhee Julia Beauchamp-Walters Kenneth P Wright Maria Gloria Dominguez-Bello Mark Manary Michelli F Oliveira Brigid S Boland Norberto Peporine Lopes Monica Guma Austin D Swafford Rachel J Dutton Rob Knight Pieter C Dorrestein Collaborative Mass Spectrometry Innovation Center University of California San Diego La Jolla CA USA. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Beckman Center for Conservation Research San Diego Zoo Wildlife Alliance Escondido CA USA. Center for Microbiome Innovation Joan and Irwin Jacobs School of Engineering University of California San Diego La Jolla CA USA. Division of Biological Sciences University of California San Diego La Jolla CA USA. Department of Pediatrics School of Medicine University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Division of Rheumatology Allergy & Immunology Department of Medicine University of California San Diego La Jolla CA USA. Division of Epidemiology and Community Health School of Public Health University of Minnesota Minneapolis MN USA. Department of Integrative Physiology University of Colorado Boulder Boulder CO USA. Department of Population Health Sciences University of Wisconsin-Madison Madison WI USA. Servicio Autónomo Centro Amazónico de Investigación y Control de Enfermedades Tropicales Simón Bolívar Puerto Ayacucho Amazonas Venezuela. Department of Pediatrics Billings Clinic Billings MT USA. Department of Animal Science Michigan State University East Lansing MI USA. Department of Pharmacology Ribeirão Preto Medicinal School Center of Research in Inflammatory Diseases University of São Paulo Ribeirão Preto Sao Paolo Brazil. Multidisciplinary Health Institute Federal University of Bahia Vitória da Conquista Bahia Brazil. NPPNS Department of Biomolecular Sciences School of Pharmaceutical Sciences of Ribeirão Preto University of São Paulo Ribeirão Preto Sao Paolo Brazil. Department of Internal Medicine Ribeirão Preto Medical School Center of Research in Inflammatory Diseases University of São Paulo Ribeirão Preto Sao Paolo Brazil. Department

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Traffic parameters prediction using a three-channel convolutional neural network 1

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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Wang, Dehai Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. PudongShanghai China

ISBN: (数字)9783319681214

ISBN: (纸本)9783319681207

Traffic three elements consisting of flow, speed and occupancy are very important parameters representing the traffic information. Prediction of them is a fundamental problem of Intelligent Transportation systems (ITS). Convolutional Neural Network (CNN) has been proved to be an effective deep learning method for extracting hierarchical features from data with local correlations such as image, video. In this paper, in consideration of the spatiotemporal correlations of traffic data, we propose a CNN-based method to forecast flow, speed and occupancy simultaneously by converting raw flow, speed and occupancy (FSO) data to FSO color images. We evaluate the performance of this method and compare it with other prevailing methods for traffic prediction. Experimental results show that our method has superior performance. © IFIP International Federation for Information Processing 2017.

关键词： Forecasting

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Using convolutional neural network with asymmetrical kernels to predict speed of elevated highway 1

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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Ling, Jiawei Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. Pudong Shanghai China

ISBN: (数字)9783319681214

ISBN: (纸本)9783319681207

In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway. In order to learn the temporal features of traffic speed data in a hierarchical way, an improved convolutional neural network (CNN) with asymmetric kernels is proposed. Speed data are collected from loop detectors of Yan’an elevated highway of Shanghai. To test the performance of the presented method, we compare it with some conventional approaches of traffic speed estimation. Experimental results demonstrate that our method outperforms all of them. © IFIP International Federation for Information Processing 2017.

关键词： Speed

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Variance-Constrained H∞ Filtering for Stochastic Parameter systems Under Event-Triggered Protocol

Variance-Constrained H∞ Filtering for Stochastic Parameter ...

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第36届中国控制会议

作者： Licheng Wang Zidong Wang Guoliang Wei Shanghai Key Lab of Modern Optical System Department of Control Science and EngineeringUniversity of Shanghai for Science and Technology Department of Computer Science Brunel University London

In this paper,the variance-constrained H filtering problem is investigated for a class of discrete-time stochastic parameter *** event-triggered communication protocol is adopted in order to reduce the communication burden where the measurement information required by the filter is updated only when a certain prescribed event is *** purpose of the addressed filtering problem is to design a recursive filter such that,in the presence of random parameter perturbations and external noise disturbances,both the prespecified H performance index and the variance constraints on the filtering errors are satisfied over a *** stochastic analysis is carried out and recursive matrix inequality technique is developed to obtain the filter *** a filter design scheme is based on the solution to a set of forward recursive matrix inequalities and therefore facilitates the on-line ***,the usefulness of the developed event-triggered recursive filter design scheme is demonstrated by a numerical simulation example.

关键词： Variance-constrained filtering H∞ performance event-triggered communication protocol recursive matrix inequality stochastic parameter system

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Reliable Code Generation and Test Environment demonstrated on a PI-controller Design

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IFAC-PapersOnLine 2016年第3期49卷 465-470页

作者： Schwarz, M.H. Sheng, H. Üstoglu, I. Chabaan, W. Börcsök, J. Safety Computer Technology University of Kassel Germany Control and Automation Engineering Department Yildiz Technical University Istanbul Turkey Computer Architecture and System Programming University of Kassel Germany

This paper focuses on the latest version of a tool developed within the department to guide developer through the various steps of requirements, test-procedures and documentations to finally download the derived algorithm on a safety programmable logic controller (PLC). Requirements on how to develop and how to test software are steadily increasing due to international standards, which have to be obeyed in industries in general, and in the process- and automation industries in particular. A simple model is derived to be used to develop a PI controller that has to pass several steps to be qualified for execution on a PLC. The paper explains the different steps and describes the various problems that might occur. © 2016

关键词： controllers Accident prevention Reliability Software testing Automated code generation Automation industry International standards P i controller designs PI control Programmable Logic controller (PLC) Simple modeling Test Environment

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An Incremental Learning-Based Mechanism for Object Recognition in Cloud Robotic system

An Incremental Learning-Based Mechanism for Object Recogniti...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Wenyu Li Zihao An Shurui Jiang Jeffrey Too Chuan Tan Feng Duan Chi Zhu Haoyong Yu College of Computer and Control Engineering Nankai University Tianjin China Department of System Life Engineering Maebashi Institute of Technology Maebashi Gunma Japan Department of Biomedical Engineering National University of Singapore Singapore

ISBN: (纸本)9781538604915

Cloud robotic system needs to learn real time data stream in order to adapt to the new environment for better performance. Incremental learning as a kind of dynamics classification method can avoid sample repeating learning, and develop model for updating itself. In this paper, we proposed an incremental learning-based cloud robot object recognition mechanism. It can learn new objects through human-robot interaction by speech recognition. The proposed incremental learning method was tested with small scale data in domestic indoor environment application, and the cloud robotic system was validated by basic conversation with different testing groups. The experimental results verify the feasibility of the incrementally learning new information algorithm and the proposed cloud robotic system.

关键词： Cloud computing Servers Speech recognition Object recognition Service robots Training

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