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2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics(EMCPE 2016)

作者： Xiao Yan Changliang Liu School of Control and Computer Engineering North China Electric Power University State Key Laboratory Of Alternate Electrical Power System With Renewable Energy Sources(NCEPU)

The condition detection of gearbox is of great significance to improve the operation level and reduce the maintenance cost of the wind turbine. Based on the nonlinear state estimation(NSET) method, the temperature model of the gear box bearing is set up to estimate the states of gear box and the estimated value of the gearbox bearing temperature is obtained. Having obtained the residual error of the estimated value and the actual value of the gearbox bearing temperature, the residual distribution characteristics are analyzed by sliding window error statistics method. The residual distribution characteristic of the model will be changed when the working condition of the gearbox is abnormal, and when the residual mean or standard deviation exceeds the set threshold, the warning is given.

关键词： Fault early warning NEST Grey Relational Analysis Residual characteristic analysis

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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990–2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet Infectious Diseases 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H.A. El Hafeez, Samar Abd Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Syed Shujait Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwar, Saleha Anwari, Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishnan, Senthi Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States 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 Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background: Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods: We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age–sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings: In 2021, diarrhoeal diseases caused an estimated 1·17 million (95% uncertainty interval 0·793–1·62) deaths globally, representing a 60·3% (50·6–69·0) decrease since 1990 (2·93 million [2·31–3·73] deaths). The most pronounced decline was in children younger than 5 years, with a 79·2% (72·4–84·6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147–221) in 1990 to 51·4 million (39·9–65·9) in 2021. In 2021, an e

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Maximizing higher derivatives of unknown maps with stochastic extremum seeking

Maximizing higher derivatives of unknown maps with stochasti...

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American control Conference

作者： Greg Mills Miroslav Krstic Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjin University of Technology Tianjin China College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781467386838

We present a stochastic generalization to the scalar Newton-based extremum seeking algorithm which through measurements of an unknown map, maximizes the map's higher derivatives. The proposed method perturbs the estimate of the optimal parameter with the sinusoid of Brownian motion about the boundary of a circle. Then by properly demodulating the map output of the randomly perturbed estimate, the extremum seeking algorithm maximizes the nth derivative only through measurements of the map. The Newton-based extremum seeking approach removes the dependence of the convergence rate on the unknown Hessian of the higher derivative, an effort to improve performance over standard gradient-based extremum seeking. Our design stems from the existing multivariable Newton-based extremum seeking algorithm where a differential Riccati equation estimates the inverse Hessian of the function to be maximized. Algebraically computing a direct estimate of the inverse Hessian is susceptible to singularity, where-as employing the Riccati filter removes that potential. We prove local stability of the algorithm for general nonlinear static maps via stochastic averaging theory developed for extremum seeking.

关键词： Heuristic algorithms Convergence Stability analysis Optimization Demodulation Markov processes Demodulation Heuristic algorithms extremum seeking algorithm Markov chain extremum values Stability analysis Converge maps Riccati equations Hessian RATE DEPENDENCE optimal parameter

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Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES

arXiv

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arXiv 2017年

作者： Ye, Yumeng Liu, Haichun Zhang, TianHong Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation and Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Electroencephalography is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. Among them, analysis of the risk of schizophrenia using EEG data is a relatively new research topic. However, there are many difficulties in analyzing EEG data, including its complex composition, low amplitude as well as low signal-to-noise ratio. Some of the existing methods of analysis are based on feature extraction and machine learning to differentiate the phase of schizophrenia (First-episode schizophrenia, Healthy controls or Clinical high-risk) that samples belong to. However, medical research requires the use of machine learning not only to give more accurate classification results, but also to give the results that can be applied to pathological studies. The main purpose of this study is to obtain the weight values as the representation of influence of each frequency band on the classification of schizophrenia phases on the basis of a more effective classification method using the LES feature extraction, and then the weight values are processed and applied to improve the accuracy of machine learning classification. We propose a method called weight-voting to obtain the weights of sub-bands features by using results of classification for voting to fit the actual categories of EEG data, and using weights for reclassification. Through this method, we can first obtain the influence of each band in distinguishing three schizophrenia phases, and analyze the effect of band features on the risk of schizophrenia contributing to the study of psychopathology. In addition, the weights applied to the original classifier can achieve the upgrade of the classification effect, which contributes to the BCI-assisted system of diagnosis and treatment. Our results show that there is a high correlation between the change of weight of low gamma band and the difference between HC, CHR an

关键词： Electroencephalography

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Attitude synchronization of multiple rigid bodies in SE(3) over proximity networks

Attitude synchronization of multiple rigid bodies in SE(3) o...

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第35届中国控制会议

作者： Juan Deng Zhixin Liu Lin Wang John S.Baras Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceCAS Department of Automation Shanghai Jiao Tong Universityand the Key Laboratory of System Control and Information ProcessingMinistry of Education Department of Electrical and Computer Engineering and the Institute for Systems Research University of MarylandCollege Park

ISBN: (纸本)9781509009107

This paper investigates the attitude synchronization of a group of moving rigid bodies whose states including positions and rotation matrices evolve in the Lie group SE(3).The commonly used circular neighborhood is adopted,and the neighbor graphs are described by proximity *** order to avoid introducing discussions on the abrupt changes of neighbor graphs,a dwell time is *** distributed sampled-data control law for the angular velocity is *** the assumptions on the initial states,we provide sufficient conditions for the attitude synchronization of multiple rigid body *** with the existing results,our condition depends only on the neighborhood radius,the moving velocity and the dwell time.

关键词： rigid body SE(3) attitude synchronization sampled-data control proximity network

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Design and implementation of natural language processing with syntax and semantic analysis for extract traffic conditions from social media data 5

Design and implementation of natural language processing wit...

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5th IEEE International Conference on system Engineering and Technology, ICSET 2015

作者： Aziz, Mochamad Vicky Ghani Prihatmanto, Ary Setijadi Henriyan, Diotra Wijaya, Rifki Control and Computer System Laboratory School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

ISBN: (纸本)9781467367134

Traffic congestion is still a crucial issue because it has a huge impact from the waste of time, the fuel to air pollution. Search information on traffic conditions have been widely available such as through Twitter and the website of CCTV, but the rapid development of online information services resulted in the lack of time to read the complete information. By utilizing the Twitter data, then created a summary regarding the traffic, the summary can improve time efficiency and effectiveness in obtaining information about traffic conditions. However, the structure of the language of Twitter tweets unstructured and raw cause difficult to process information computer, to solve these problems then summarize it using Natural Language Processing approach the syntactic and semantic analysis to improve the structure of words and sentence parsing so that it can classify traffic conditions tweet of the sentence. © 2015 IEEE.

关键词： Traffic congestion

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Visual tracking via an ensemble of random classifiers

Visual tracking via an ensemble of random classifiers

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Yichun Shi Hesheng Wang Department of Computer Science Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (纸本)9781467389600

One largest problem for tracking-by-detection methods is the incomplete and noisy training set. Occlusion, illumination and many other problems could lead to this problem. Models that are not adaptive enough would fail to track the target when drastic appearance change takes place. Adaptive ones, although keep tracking at first, could lose the target because of learning too many incorrect samples. In this paper, we present an ensemble model of random classifiers updated on different dataset. When the appearance of the target changes drastically and some sub-models are confused, the others could help correct the tracking result. A latent variable is added for choosing sub-models and it naturally leads to predicting the new samples with a weighted sum of sub-models. To calculate the weight, we add a generative model to each random classifier. Experiments show that our method could track the target robustly and accurately.

关键词： Target tracking Training Visualization Noise measurement Robustness Lighting Adaptation models

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A Data Driven Approach for Resting-state EEG signal Classificition of Schizophrenia with control Participants Using Random Matrix Theory

arXiv

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arXiv 2017年

作者： Liu, Haichun Zhang, TianHong Ye, Yumeng Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Resting state electroencephalogram (EEG) abnormalities in clinically high-risk individuals (CHR), clinically stable first-episode patients with schizophrenia (FES), healthy controls (HC) suggest alterations in neural oscillatory activity. However, few studies directly compare these anomalies among each types. Therefore, this study investigated whether these electrophysiological characteristics differentiate clinical populations from one another, and from non-psychiatric controls. To address this question, resting EEG power and coherence were assessed in 40 clinically high-risk individuals (CHR), 40 first-episode patients with schizophrenia (FES), and 40 healthy controls (HC). These findings suggest that resting EEG can be a sensitive measure for differentiating between clinical *** paper proposes a novel data-driven supervised learning method to obtain identification of the patients mental status in schizophrenia research. According to Marchenko-Pastur Law, the distribution of the eigenvalues of EEG data is divided into signal subspace and noise subspace. A test statistic named LES that embodies the characteristics of all eigenvalues is adopted. different classifier and different feature(LES test function) are selected for experiments, we have shown that using von Neumann Entropy as LES test function combine with SVM classifier could obtain the best average classification accuracy during three classification among HC, FES and CHR of Schizophrenia group with EEG signal. It is worth noting that the result of LES feature extraction with the highest classification accuracy is around 90% in two classification(HC compare with FES) and around 70% in three classification. Where the classification accuracy higher than 70% could be used to assist clinical diagnosis. Copyright © 2017, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Robust Semi-global Leader-following Consensus of Linear Multi-agent systems with Imperfect Actuators

Robust Semi-global Leader-following Consensus of Linear Mult...

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第35届中国控制会议

作者： Liangren Shi Zhiyun Zhao Zongli Lin Department of Automation and Key Laboratory of System Control and Information Processing of the Ministry of EducationShanghai Jiao Tong University Charles L.Brown Department of Electrical and Computer Engineering University of Virginia

ISBN: (纸本)9781509009107

This paper deals with the robust semi-global leader-following consensus problem for a linear multi-agent system with imperfect *** actuators of the follower agents have imperfect input output characteristics,such as those of saturation and dead-zone,and those characteristics are not precisely *** agents are also subject to input additive disturbances.A low-and-high gain based linear state feedback control law is constructed for each follower *** is shown that these control laws achieve robust semi-global leader-following consensus when the graph representing the communication topology among the followers is undirected and connected and the leader is a neighbor of at least one *** simulation illustrates the theoretical results.

关键词： Consensus leader-following actuator saturation dead-zone low-and-high gain feedback

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Primary Frequency Regulation for Multi-area Interconnected Power system with Wind Turbines Based on DMPC

Primary Frequency Regulation for Multi-area Interconnected P...

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第35届中国控制会议

作者： Miaomiao Ma Chunyu Zhang Liyang Shao Yushen Sun State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University School of Control & Computer Engineering North China Electric Power University

ISBN: (纸本)9781509009107

This paper integrates the wind turbines(WTs) into the interconnected power system and designs a distributed model predictive control(DMPC) controller for the primary frequency regulation *** WTs have been treated as a part of the power system to take part in primary frequency regulation with load disturbances.A frequency response model of multi-area power system including WTs is introduced,in which the physical constraints of the governors and turbines are *** distributed model predictive controller is designed by posing the primary frequency regulation problem as disturbance rejection problem of large system with state and input *** and simulation results for a two-area interconnected power system with WTs show possible improvements on closed-loop performance,while respecting physical hard *** have been made made between the DMPC controller with and without *** results confirm the superiority of the proposed DMPC scheme with the participation of the wind turbines in the primary frequency regulation.

关键词： Primary frequency regulation distributed model predictive control generation rate constraint(GRC) interconnected power system wind turbine

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