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arXiv 2017年

作者： Li, Xun Ni, Yuan-Hua Zhang, Ji-Feng Department of Applied Mathematics Hong Kong Polytechnic University Hunghom Kowloon Hong Kong College of Computer and Control Engineering Nankai University Tianjin300350 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences China

In this paper, we investigate a class of time-inconsistent discrete-time stochastic linear-quadratic optimal control problems, whose time-consistent solutions consist of an open-loop equilibrium control and a linear feedback equilibrium strategy. The open-loop equilibrium control is defined for a given initial pair, while the linear feedback equilibrium strategy is defined for all the initial pairs. Maximum-principle-type necessary and sufficient conditions containing stationary and convexity are derived for the existence of these two time-consistent solutions, respectively. Furthermore, for the case where the system matrices are independent of the initial time, we show that the existence of the open-loop equilibrium control for a given initial pair is equivalent to the solvability of a set of nonsymmetric generalized difference Riccati equations and a set of linear difference equations. Moreover, the existence of linear feedback equilibrium strategy is equivalent to the solvability of another set of symmetric generalized difference Riccati equations.49N10, 49N35, 93E20 Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Accelerating time series shapelets discovery with key points

Accelerating time series shapelets discovery with key points

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Lecture Notes in computer science

作者： Zhang, Zhenguo Zhang, Haiwei Wen, Yanlong Yuan, Xiaojie College of Computer and Control Engineering Nankai University 38 Tongyan Road Tianjin300350 China Department of Computer Science and Technology Yanbian University 977 Gongyuan Road Yanji133002 China

ISBN: (纸本)9783319458168

Shapelets are discriminative subsequences in a time series dataset, which provide good interpretability for time series classification results. For this reason, time series shapelets have attracted great interest in time series data mining community. Although time series shapelets have satisfactory performance on many time series datasets, how to fast discover them is still a challenge because any subsequence in a time series may be a shapelet candidate. There are several methods to speed up shapelets discovery in recent years. However, these methods are still time-consuming when dealing with the large datasets or long time series. In this paper, we propose a preprocessing step with time series key points for shapelets discovery which make full use of the prior knowledge of shapelets. Combining with shapelets discovery method based on SAX(Fast-Shaplets), we can find shapelets quickly on all benchmark datasets of UCR archives, while the classification accuracy is almost the same as the current methods. © Springer International Publishing Switzerland 2016.

关键词： Time series

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An improved OMP application for airborne radar space-time clutter spectrum estimation

An improved OMP application for airborne radar space-time cl...

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Cunxiao Ji Mingwei Shen Chao Liang Di Wu Daiyin Zhu College of Computer & Information Engineering Hohai University Nanjing China Science and Technology on Electronic Information Control Laboratory Chengdu China Key Laboratory of Radar Imaging and Microwave Photonics & Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China

In this paper, an improved orthogonal matching pursuit (OMP) approach is investigated in the context of space-time clutter spectrum estimation, where an iterative estimation strategy is employed to approximate the sparsity of the clutter vector. Instead of using OMP directly, the improved application can achieve a linear approximation of the sparsity of the clutter signal, and obtain the corresponding support set sequentially. Consequently, the scattering coefficients of the space-time clutter spectrum can be recovered efficiently. With respect to the conventional convex optimization, the presented algorithm is computationally much more efficient and meanwhile is able to achieve comparable reconstruction precision. The presented implementation is validated by Monte Carlo simulations.

关键词： Clutter Matching pursuit algorithms Doppler effect Airborne radar Algorithm design and analysis Image reconstruction Scattering

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Revealing dynamic communities in networks using genetic algorithm with merging and splitting operators

arXiv

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arXiv 2017年

作者： Zhan, Weihua Deng, Lei Guan, Jihong Niu, Jun College of Control Science and Engineering Zhejiang University 38 Zheda Road Hangzhou310058 China School of Electronics and Computer Science Zhejiang Wanli University 8 Qianhu Road Ningbo315100 China School of Software Central South University Changsha410075 China Department of Computer Science and Technology Tongji University 4800 Cao'an Road Shanghai201804 China Department of Computer Science and Technology Ningbo University 8 Feng'Hua Road Ningbo315211 China

Community structure is pervasive in various real-world networks, portraying the strong local clustering of nodes. Unveiling the community structure of a network is deemed to a crucial step towards understanding the dynamics on the network. Actually, most of the real-world networks are dynamic and their community structures are evolutionary over time accordingly. How to revealing the dynamical communities has recently become a pressing issue. Here, we present an evolutionary method for accurately identifying dynamical communities in the networks. In this method, we first introduced a fitness function that is a compound of asymptotic surprise values on the current and previous snapshots of the network. Second, we developed ad hoc merging and splitting operators, which allows for large-scale searching while preserving low cost. Third, this large-scale searching coupled with local mutation and crossover enhanced revealing a better solution to each snapshot of the network. This method does not require specifying the number of communities advanced, and free from resolution limit while satisfying temporal smooth constraint. Experimental results on both model and real dynamic networks show that the method can find a better solution compared with state-of-art approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Genetic algorithms

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Publisher Correction: A vaccine targeting the RBD of the S protein of SARS-CoV-2 induces protective immunity

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Nature 2021年第7844期590卷 E23页

作者： Jingyun Yang Wei Wang Zimin Chen Shuaiyao Lu Fanli Yang Zhenfei Bi Linlin Bao Fei Mo Xue Li Yong Huang Weiqi Hong Yun Yang Yuan Zhao Fei Ye Sheng Lin Wei Deng Hua Chen Hong Lei Ziqi Zhang Min Luo Hong Gao Yue Zheng Yanqiu Gong Xiaohua Jiang Yanfeng Xu Qi Lv Dan Li Manni Wang Fengdi Li Shunyi Wang Guanpeng Wang Pin Yu Yajin Qu Li Yang Hongxin Deng Aiping Tong Jiong Li Zhenling Wang Jinliang Yang Guobo Shen Zhiwei Zhao Yuhua Li Jingwen Luo Hongqi Liu Wenhai Yu Mengli Yang Jingwen Xu Junbin Wang Haiyan Li Haixuan Wang Dexuan Kuang Panpan Lin Zhengtao Hu Wei Guo Wei Cheng Yanlin He Xiangrong Song Chong Chen Zhihong Xue Shaohua Yao Lu Chen Xuelei Ma Siyuan Chen Maling Gou Weijin Huang Youchun Wang Changfa Fan Zhixin Tian Ming Shi Fu-Sheng Wang Lunzhi Dai Min Wu Gen Li Guangyu Wang Yong Peng Zhiyong Qian Canhua Huang Johnson Yiu-Nam Lau Zhenglin Yang Yuquan Wei Xiaobo Cen Xiaozhong Peng Chuan Qin Kang Zhang Guangwen Lu Xiawei Wei Laboratory of Aging Research and Cancer Drug Target State Key Laboratory of Biotherapy and Cancer Center National Clinical Research Center for Geriatrics West China Hospital Sichuan University Chengdu China. National Kunming High-level Biosafety Primate Research Center Institute of Medical Biology Chinese Academy of Medical Sciences and Peking Union Medical College Yunnan China. Key Laboratory of Human Disease Comparative Medicine Chinese Ministry of Health Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases Institute of Laboratory Animal Science Chinese Academy of Medical Sciences and Comparative Medicine Center Peking Union Medical College Beijing China. National Institutes for Food and Drug Control (NIFDC) Beijing China. National Chengdu Center for Safety Evaluation of Drugs State Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University Collaborative Innovation Center for Biotherapy Chengdu China. Shanghai Key Laboratory of Chemical Assessment and Sustainability School of Chemical Science & Engineering Tongji University Shanghai China. Treatment and Research Center for Infectious Diseases The Fifth Medical Center of PLA General Hospital National Clinical Research Center for Infectious Diseases Beijing China. Center for Biomedicine and Innovations Faculty of Medicine Macau University of Science and Technology Macau China. Department of Computer Science and Technology Tsinghua University Beijing China. Department of Applied Biology and Chemical Technology Hong Kong Polytechnic University Hong Kong China. Sichuan Provincial Key Laboratory for Human Disease Gene Study and Institute of Laboratory Medicine Sichuan Provincial People's Hospital University of Electronic Science and Technology of China Chengdu China. State Key Laboratory of Medical Molecular Biology Chinese Academy of Medical Sciences School of Basic Medicine Peking Union Medical College Beijing China. Center for Biomedicine and Innovation

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Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient

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Journal of Medical engineering and Technology 2016年第3期40卷 87-98页

作者： Moridani, Mohammad Karimi Setarehdan, Seyed Kamaledin Motie Nasrabadi, Ali Hajinasrollah, Esmaeil Department of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Departments of Biomedical Engineering Shahed University Tehran Iran Loghman Medical Center Shahid Beheshti University of Medical Sciences Tehran Iran

Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patients ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patients death is very informative for diagnosing the patients condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics. © 2016 Taylor & Francis.

关键词： Forecasting

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Sampled-data stability criterion for T-S fuzzy systems with time-varying delay 3

Sampled-data stability criterion for T-S fuzzy systems with ...

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3rd International Conference on Informative and Cybernetics for Computational Social Systems, ICCSS 2016

作者： Du, Zhenbin Jia, Qiang Qin, Zhenku Qu, Zifang School of Computer and Control Engineering Yantai University Yantai China Faculty of Science Jiangsu University Zhenjiang China College of Mathematics and Information Science Shandong Institute of Business and Technology Yantai China

ISBN: (纸本)9781509033676

According to the existing researches, a new Takagi-Sugeno fuzzy systems under the time-varying delay were constructed. To reaching a superior sampled-data stability, the approach of input delay with free-weighting matrix is used. New criterion of stability is proposed. Lyapunov-Krasovskii function's derivative also included, it means a feasible condition. Fuzzy sampled-data controller is designed with asymptotically stability. Truck-trailer system as a simulation illustrated the superiority of fuzzy sampled-data control design. © 2016 IEEE.

关键词： Stability criteria

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Learning policies for Markov decision processes from data

arXiv

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arXiv 2017年

作者： Hanawal, Manjesh K. Liu, Hao Zhu, Henghui Paschalidis, Ioannis Ch. Industrial Engineering and Operations Research IIT-Bombay PowaiMH 400076 India Center for Information and Systems Engineering Boston University BostonMA02215 United States College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang310027 China Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States

We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-actions pairs followed by the policy. We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs. The features are known a priori, however, only an unknown subset of them could be relevant. The policy parameters that correspond to an observed target policy are recovered using `1-regularized logistic regression that best fits the observed state-action samples. We establish bounds on the difference between the average reward of the estimated and the original policy (regret) in terms of the generalization error and the ergodic coefficient of the underlying Markov chain. To that end, we combine sample complexity theory and sensitivity analysis of the stationary distribution of Markov chains. Our analysis suggests that to achieve regret within order O(√Ε), it suffices to use training sample size on the order of Ω(log n · poly(1/Ε)), where n is the number of the features. We demonstrate the effectiveness of our method on a synthetic robot navigation example.90C40 (Primary) 90C39, 93E35, 60J10 (Secondary) Copyright © 2017, The Authors. All rights reserved.

关键词： Markov processes

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Determine Q-V characteristics of grid connected wind farms for voltage control using data driven analytics approach

Determine Q-V characteristics of grid connected wind farms f...

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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Chowdhury Andalib-Bin-Karim Xiaodong Liang Nahidul Khan Huaguang Zhang Department of Electrical and Computer Engineering Memorial University of Newfoundland St John's NL Canada New found land and Labrador Hydro System Operation & Planning Energy Control Center St John's NL Canada College of Information Science and Engineering North eastern University Shenyang Liaoning China

ISBN: (纸本)9781509057252

Due to varying and intermittent nature of wind resource, grid connected wind farms pose significant technical challenges to power grid on power quality and voltage stability. Wind farm Q-V characteristic curve at the point of interconnection (POI) can offer valuable information for voltage control actions and provide essential indication about voltage stability. Data driven analytics is a promising approach to determine characteristics of a large complex system, physical model of which is difficult to obtain. In this paper, the data driven analytics is used to determine Q-V curve of grid connected wind farms based on measurement data recorded at the POI. Different curve fitting models, such as Polynomial, Gaussian and Rational, are evaluated and best fit is determined based on different graphical and numerical evaluation metrics. A case study is conducted using field measurement data at two grid connected wind farms currently in operation in Newfoundland and Labrador, Canada. It is found that the Gaussian (degree 2) model describes the Q-V relationship most accurately for the two wind farms. The obtained functions and processed data can be used in the voltage controller design. The plotted QV curve can also be used to determine the reactive margin at the POI for voltage stability evaluation. As a generic method, the proposed approach can be employed to determine Q-V characteristic curve of any grid connected large wind farms.

关键词： Mathematical model Wind farms Voltage control Curve fitting Stability analysis Power system stability Reactive power

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