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parallel SIMULATION OF LARGE-SCALE ARTIFICIAL SOCIETY WITH GPU AS COPROCESSOR

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International Journal of Modeling, Simulation, and Scientific Computing 2013年第2期4卷 130-141页

作者： GANG GUO BIN CHEN XIAOGANG QIU research center of computational experiments and parallel system technology National University of Defense Technology ChangshaHunanChina 410073P.R.China

Large-scale artificial societies with millions or billions of agents call for high-performance parallel *** supercomputers with thousands of CPUs and GPUs make it possible to carry out such *** key is to distribute large-scale agents to massive cores of CPUs and GPUs properly for parallel computing with efficient communication and *** simplicity and efficiency,a modified discrete event system specification(DEVS)is proposed for large-scale artificial society modeling and parallelism is exploited in agent models because similar agents usually share similar *** phased synchronization,a two-tier parallel simulation engine is designed with support of MPI and OpenCL where GPU is used as ***-sided communication is used for reflection of remote simulation objects and message passing between processes.A general kernel function prototype is elaborately designed and conditionally compiled for execution on both CPU and *** artificial society for epidemic study is used to test the performance on a supercomputer with 1024 CPU cores and 1792 GPU *** speedup reaches 3512 for even 2 billion agents with GPU acceleration which is far over 701 when only CPUs are *** turns out feasible for parallel simulation of large-scale artificial society with GPU as coprocessor.

关键词： Artificial society parallel simulation agent-based modeling GPU phased synchronization DEVS.

Mathematical and computational approaches to epidemic modeling： a comprehensive review

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Frontiers of Computer Science 2015年第5期9卷 806-826页

作者： Wei DUAN Zongchen FAN Peng ZHANG Gang GUO Xiaogang QIU center of computational experiments and parallel systems technology College of Information Systems and Management National University of Defense Technology Chang sha 410073 China

Mathematical and computational approaches are important tools for understanding epidemic spread patterns and evaluating policies of disease control. In recent years, epidemiology has become increasingly integrated with mathematics, sociology, management science, complexity science, and computer science. The cross of multiple disciplines has caused rapid development of mathematical and computational approaches to epidemic modeling. In this article, we carry out a comprehensive review of epidemic models to provide an insight into the literature of epidemic modeling and *** introduce major epidemic models in three directions, including mathematical models, complex network models, and agent-based models. We discuss the principles, applications, advantages, and limitations of these models. Meanwhile, we also propose some future research directions in epidemic modeling.

关键词： mathematics complex networks agent-basedmodels epidemic modeling human dynamics infectious diseases

Determination of Polynomial Degree in the Regression of Drug Combinations

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 41-47页

作者： Boqian Wang Xianting Ding Fei-Yue Wang School of Biomedical Engineering Shanghai Jiao Tong University IEEE State Key Laboratory of Management and Control for Complex systems Institute of Automation Chinese Academy of Science (SKL-MCCS CASIA) research center of computational experiments and parallel systems National University of Defense Technology

Studies on drug combinations are becoming more and more popular in the past few decades, with the development of computer and algorithms. One of the most common methods in optimizing drug combinations is regression of a polynomial model based on certain number of experimental observations. In this paper, we study how to determine the degree of polynomials in different circumstances of drug combination optimization. Using cross-validation, we have found that in most cases, a high degree results in failures of accurate prediction, named overfitting. An anti-noise test has also revealed that polynomial model with high degree tends to be less resistant to random errors in the observations. © 2014 Chinese Association of Automation.

关键词： Acoustics Random errors Regression analysis

On social computing research collaboration patterns： a social network perspective

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Frontiers of Computer Science 2012年第1期6卷 122-130页

作者： Tao WANG Qingpeng ZHANG Zhong LIU Wenli LIU Ding WEN Science and technology on Information systems Engineering Lab College of Information Systems and ManagementNational University of Defense and TechnologyChangsha 410073China Department of systems and Industrial Engineering University of ArizonaTucsonAZ 85721USA Military computational experiments and parallel systems research center National University of Defense and TechnologyChangsha 410073China

The field of social computing emerged more than ten years ago. During the last decade, researchers from a vari- ety of disciplines have been closely collaborating to boost the growth of social computing research. This paper aims at iden- tifying key researchers and institutions, and examining the collaboration patterns in the field. We employ co-authorship network analysis at different levels to study the bibliographic information of 6 543 publications in social computing from 1998 to 2011. This paper gives a snapshot of the current re- search in social computing and can provide an initial guid- ance to new researchers in social computing.

关键词： social computing bibliographic analysis com-putational social science social network analysis

Control 5.0: From Newton to Merton in Popper's Cyber-Social-Physical Spaces

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IEEE/CAA Journal of Automatica Sinica 2016年第3期3卷 233-234页

作者： Fei-Yue Wang IEEE State Key Laboratory of Management and Control for Complex systems Institute of Automation Chinese Academy of Sciences (SKL-MCCS CASIA) research center of computational experiments and parallel systems The National University of Defense Technology

The future of control in cyberspace of parallel worlds is discussed. It argues for the coming age of Control 5.0,the control technology for the new IT capable of dealing with artificial worlds with VR, AR, AI and robotics. The discipline of automation needs a new interpretation of its core knowledge and skill set of modeling, analysis, and control for cyber-socialphysical systems, and a paradigm shift from Newtonian systems with Newton's Laws or Big Laws with Small Data to Mertonian systems with Merton's Laws or Small Laws with Big Data.

关键词： Control 5.0 parallel control knowledge automation CPS CSP CPSS Merton Wiener Popper

On Frequency Sensitivity and Mode Orthogonality of Flexible Robotic Manipulators

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IEEE/CAA Journal of Automatica Sinica 2016年第4期3卷 394-397页

作者： Fei-Yue Wang Yanqing Gao IEEE State Key Laboratory of Management and Control for Complex systems Institute of AutomationChinese Academy of Sciences research center of computational experiments and parallel systems the National University of Defense Technology Department of Manufacturing & Mechanical Engineering & technology Oregon Institute of Technology

This paper presents sensitivity analysis of vibration frequencies of flexible manipulators with respect to variations of systems parameters such as rotational inertia of hub, and mass, moment, and side of tip load. Both Euler-Bernoulli and Timoshenko dynamical models of flexible manipulators are discussed. By using variational method, sensitivity indices are obtained with explicit expressions for measuring the sensitivity of frequencies. Based on variational formulations, a novel method for deriving the orthogonal relations among vibration modal shape functions of flexible manipulators is introduced. With this method, the orthogonal relations can be derived easily without invoking the tedious process of differentiation and integration by part, as commonly used in their derivation. © 2014 Chinese Association of Automation.

关键词： Dielectric waveguides Flexible manipulators Manipulators Orthogonal functions Vibration analysis

parallel Learning:a Perspective and a Framework

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 389-395页

作者： Li Li Yilun Lin Nanning Zheng Fei-Yue Wang Department of Automation TNListTsinghua University IEEE State Key Laboratory of Management and Control for Complex systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Institute of Artificial Intelligence and Robotics(IAIR) Xi'an Jiaotong University research center for computational experiments and parallel systems technology National University of Defense Technology

The development of machine learning in complex system is hindered by two problems *** first problem is the inefficiency of exploration in state and action space,which leads to the data-hungry of some state-of-art data-driven *** second problem is the lack of a general theory which can be used to analyze and implement a complex learning *** this paper,we proposed a general methods that can address both two *** combine the concepts of descriptive learning,predictive learning,and prescriptive learning into a uniform framework,so as to build a parallel system allowing learning system improved by *** a new perspective of data,knowledge and action,we provide a new methodology called parallel learning to design machine learning system for real-world problems.

关键词： Descriptive learning machine learning parallel learning parallel systems predictive learning prescriptive learning

Training and Testing Object Detectors With Virtual Images

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 539-546页

作者： Yonglin Tian Xuan Li Kunfeng Wang Fei-Yue Wang Department of Automation University of Science and Technology of China Hefei 230027 China State Key Laboratory for Management and Control of Complex systems Institute of Automation Chinese Academy of Sciences Beijirtg 100190 China School of Automation Beijing Institute of Technology Beijing 100081. China Qingdao Academy of Intelligent Industries Qingdao 266000 China research center for computational experiments and parallel systems technology National University of Defense Technology Changsha 410073 China IEEE

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.

关键词： Index Terms--Deep learning object detection parallel vision virtual dataset

From parallel Plants to Smart Plants:Intelligent Control and Management for Plant Growth

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 161-166页

作者： Mengzhen Kang Fei-Yue Wang State Key Laboratory of Management and Control for Complex systems Institute of AutomationChinese Academy of Sciences (SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Beijing Engineering research center of Intelligent systems and technology IEEE State Key Laboratory of Management and Control for Complex systems (SKL-MCCS) Institute of AutomationChinese Academy of Sciences (CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences research center for Military computational experiments and parallel systems technology National University of Defense Technology

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description U+0028 the crop model U+0029, prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. © 2017 Chinese Association of Automation.

关键词： Agriculture Artificial intelligence Decision support systems Expert systems Sustainable development