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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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Programmable Medicine: Autonomous, Ingestible, Deployable Hydrogel Patch and Plug for Stomach Ulcer Therapy

Programmable Medicine: Autonomous, Ingestible, Deployable Hy...

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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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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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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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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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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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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Traffic parameters prediction using a three-channel convolutional neural network 2nd

Traffic parameters prediction using a three-channel convolut...

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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: (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 2nd

Using convolutional neural network with asymmetrical kernels...

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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: (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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Robust ${\mathscr {H}}_{\infty }$ Filtering for a Class of Two-Dimensional Uncertain Fuzzy systems With Randomly Occurring Mixed Delays

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IEEE Transactions on Fuzzy systems 2017年第1期25卷 70-83页

作者： Yuqiang Luo Zidong Wang Guoliang Wei Fuad E. Alsaadi Shanghai Key Laboratory of Modern Optical System Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Brunel University London Uxbridge U.K

This paper is concerned with the robust H ∞ filtering problem for a class of 2-D uncertain fuzzy systems with randomly occurring mixed delays (ROMDs). The underlying 2-D systems are described by the Fornasini-Marchesini model, and the uncertainty is expressed in a linear fraction form. An improved Takagi-Sugeno (T-S) fuzzy model corresponding to the spatial promise variables is adopted to represent the complicated 2-D nonlinear system. The mixed delays consisting of both discrete and distributed delays are allowed to appear in a random manner governed by two sets of Bernoulli distributed white sequences with known probability. A full-order fuzzy filter is constructed to estimate the output signal such that, in the presence of parameter uncertainties and ROMDs, the dynamics of the estimation errors is asymptotically stable with a prescribed H ∞ disturbance attenuation level. Based on the stochastic analysis technique and the Lyapunov-like functional, sufficient conditions are established to ensure the existence of the desired filters, and the explicit expressions of such filters are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. A numerical example is provided to demonstrate the effectiveness of the developed filter design algorithms, and the filter performances with and without fuzzy rules are also compared.

关键词： Delays Fuzzy systems Uncertainty Symmetric matrices Robustness Uncertain systems Stochastic processes

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