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20th IEEE International Conference on intelligent Transportation systems (ITSC)

作者： Chen, Yuanyuan Lv, Yisheng Wang, Xiao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao Qingdao 266109 Shandong Peoples R China

ISBN: (纸本)9781538615263

Mining social media data to obtain traffic relevant information is an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific issue in social media mining that concerns to extract traffic relevant microblogs from the SinaWeibo platform, which is the first and essential step to further extract detailed traffic information, such as the location of a traffic incident. It is transformed into a machine learning problem of short text classification. We employ deep neural networks to classify microblogs into traffic relevant and traffic irrelevant ones. More specifically, we firstly adopt the continuous bag-of-word (CBOW) model to learn word embedding representations based on the dataset of three billion unlab.led microblogs. Next we use a convolutional neural network (CNN) to learn the abstract features of traffic relevant and traffic irrelevant microblogs. The key advances in this paper are: use of semantics of words and deployment of deep neural networks to extract traffic information from social media text. Experiments show that the proposed deep learning method has superior performance over support vector machine (SVM) based method and multi-layer perceptron (MLP) based method.

关键词： Data mining Social network services Real-time systems Feature extraction Predictive models Semantics Vocabulary

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An Operation management Cloud Ecosystem for Smart Buildings Based on Internet of Things

An Operation Management Cloud Ecosystem for Smart Buildings ...

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

作者： Ming Wang Shi Qiu Guiqing Zhang Junzhi Yu Yuling Wang School of Information and Electrical Engineering Shandong Jianzhu University State Key Lab. of Management and Control for Complex Systems Institution of Automation Chinese Academy of Sciences

ISBN: (纸本)9781538604915

It is a key issue in building operation management how to design an open and ubiquitous information system for diversity buildings of smart cities. This paper presents an operation management cloud ecosystem for smart buildings based on the technologies of Internet of Things (IoT) and cloud computing. It contains four levels: operation management application level, Internet of things platform, networking level, node level. The IoT platform provides hosting and computing, cloud storage, data storage for smart buildings. Networking level takes the responsibility for networking for intelligent hardware and smart terminals from the device level. With the building operation management applications, the cloud ecosystem is able to provide services of monitoring, controlling, management, and real-time optimization for smart buildings in cities.

关键词： intelligent buildings Internet of things organisation and administration Computer based information systems Thing Internets Clouds Building construction Networking Cloud Computing

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Short-term Traffic Flow Prediction with LSTM Recurrent Neural Network 20

Short-term Traffic Flow Prediction with LSTM Recurrent Neura...

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20th IEEE International Conference on intelligent Transportation systems (ITSC)

作者： Kang, Danqing Lv, Yisheng Chen, Yuan-yuan Harbin Univ Sci & Technol Harbin 150080 Heilongjiang Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China

ISBN: (纸本)9781538615263

Accurate and timely short-term traffic flow prediction plays an important role in intelligent transportation management and control. Traffic flow prediction has a long history and is still a difficult problem due to intrinsically highly nonlinear and stochastic characteristics of complex transportation systems. In this paper, we employ the long shortterm memory (LSTM) recurrent neural network to analyze the effects of various input settings on the LSTM prediction performances. Flow, speed, and occupancy at the same detector station are used as inputs to predict traffic flow. The results show that the inclusion of occupancy/speed information may help to enhance the performance of the model overall. Further, we include as inputs traffic variables from the upstream and/or downstream detector stations for traffic flow prediction. The evaluation of such spatial-temporal input interactions show that the inclusion of both downstream and upstream traffic information is useful in improving prediction accuracy.

关键词： traffic flow prediction long short-term memory model (LSTM) deep Learning upstream traffic information downstream traffic information

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Social Energy:Mining Energy From the Society

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 466-482页

作者： Jun Jason Zhang David Wenzhong Gao Yingchen Zhang Xiao Wang Xiangyang Zhao Dongliang Duan Xiaoxiao Dai Jun Hao Fei-Yue Wang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of Denver IEEE National Renewable Energy Laboratory Qingdao Academy of Intelligent Industries(QAII) QingdaoShandongChinaand SKL-MCCSCASIA School of Automation Science and Electrical Engineering Beihang University Department of Electrical and Computer Engineering College of Engineering and Applied ScienceUniversity of Wyoming State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Research Center of Computational Experiments and Parallel Systems National University of Defense Technology

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social *** this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system *** recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and *** this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy *** the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

关键词： ACP distribution locational marginal pricing intelligent operation in power systems parallel control parallel intelligence parallel management social energy socio-technical energy systems

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Yield analysis as a function of stochastic plant architecture: Case of Spilanthes acmella in the wet and dry season

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 138卷 105-116页

作者： Vavitsara, Marie Elodie Sabatier, Sylvie Kang, MengZhen Ranarijaona, Hery Lisy Tiana de Reffye, Philippe Univ Mahajanga Doctoral Sch Genie Vivant & Modelisat EDGVM Mahajanga Madagascar CIRAD AMAP F-34398 Montpellier France Chinese Acad Sci Inst Automat LIAMA State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Univ Mahajanga Doctoral Sch Nat Ecosyst EDEN Mahajanga Madagascar

The number of organs produced by a plant varies among the individuals of a population. Taking these variations into account is an important step in understanding phenotypic variability. The aim of this study was to simulate stochastic development and growth in response to environmental change using Greenlab. an organ level functional-structural model. An annual herbaceous species, Spilanthes acmella L, was grown in pots in two climatic conditions corresponding to a wet and a dry season. Detailed records of plant development, plant architecture and organ growth were kept throughout the growing period. The concept of simple and compound organic series was introduced to target data for fitting. The model was calibrated using a mathematical model of stochastic plant development and growth. Here we describe (1) how a stochastic Functional Structural Plant Model is calibrated in two steps by first assessing the functioning parameters of meristems, and second the source-sink parameters of organs by fitting them on average organic series;(2) how dry conditions trigger the response of the plant both in the development of the inflorescence and in the allocation of biomass, quantified by model parameters. The calibration of a stochastic plant model opens a large window of opportunity to capture the common features of plant development and growth among stochastic individuals in a plant population, especially those with a branching structure. This extends the area of application of FSPM to analyzing food plants, or assisting breeding. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Greenlab Plant architecture Source-sink parameters Model calibration Stochastic method Functional-structural plant model

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Prediction of crop phenology - a component of parallel agriculture management

Prediction of crop phenology - a component of parallel agric...

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Chinese Automation Congress (CAC)

作者： Hua Jing Wang Xiujuan Wang Haoyu Fan Xingrong Kang Menzhen CAS1A LIAMA State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Sci Qingdao Peoples R China Chongqing Univ Posts & Telecommun Elect Informat & Networking Res Inst Chongqing 400065 Peoples R China

ISBN: (纸本)9781538635247

Accurate prediction of crop development stages (phenolgoy) plays an important role in crop production management. Many biochemical actions of plants change with their development stage, therefore, fertilizing the plants at plants' need can increase the fertilizer use efficiency and decrease the waste. For this purpose, this paper presents a prediction model of crop phenology, describing development stages of crops as a function of the most important environmental factors, temperature and photoperiod. Machine learning is used to calibrate the crop phenology according to the predicted temperature one month later. The results indicated that our method can improve the prediction precision. The resulting model will he incorporated in a crop production management system platform developed for agriculture.

关键词： Crop phenology model Prediction Machine learning Agriculture production management PDT

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Design of AI + Curriculum for Primary and Secondary Schools in Qingdao

Design of AI + Curriculum for Primary and Secondary Schools ...

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Chinese Automation Congress (CAC)

作者： Xiao Han Fang Hu Gang Xiong Xiwei Liu Xiaoyan Gong Xiaojie Niu Wanruo Shi Xinzhu Wang Qingdao Institute of Education Sciences Qingdao China Qingdao No.2 Middle School of Shandong Province Qingdao China The Cloud Computing Center Chinese Academy of Sciences Dongguan China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Faculty of Education Beijing Normal University Beijing China

We are entering a new AI era, and the development of AI technology and popularization of AI education has become the national strategy of China. The primary and secondary school students are the builder of future society, and have strong curiosity and passion to learn new things; therefore, we should make a good preparation of AI education for them and cultivate their basic AI literacy and computational thinking to adapt to the future. In this paper, from the viewpoint of thinking mode and literacy and skill training, a curriculum including basic, project, and practical types is designed. Integrating with the iSTREAM educational system proposed by Chinese Academy of Sciences, exploratory works were shown with some case studies. The applications show that AI education is helpful to enrich the study of primary and secondary school students and to prompt comprehensive quality.

关键词： Artificial intelligence Education Industries Technological innovation Programming profession Task analysis

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Generative Adversarial Networks for Parallel Vision

Generative Adversarial Networks for Parallel Vision

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Chinese Automation Congress (CAC)

作者： Wang Kunfeng Li Xuan Yan Lan Wang Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Parallel Vis Innovat Technol Ctr Qingdao 266000 Peoples R China Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Natl Univ Def Technol Res Ctr Computat Expt & Parallel Syst Technol Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538635247

Video image dataset is playing an essential role in design and evaluation of traffic vision methods. However, there is a longstanding difficulty that manually collecting and annotating large-scale diversified dataset from real scenes is time-consuming and prone to error. In 2016, we proposed the parallel vision methodology to tackle the issues of conventional vision computing approach in data collection, model learning and evaluation. We built the ParallelEye dataset with virtual reality and the scene-specific virtual pedestrian dataset with augmented reality. In the dataset compiling process, the graphics rendering engine was used to render the artificial scenes and generate virtual images. However, the fidelity of virtual images is not satisfactory due to limitation of rendering engine, so that there is a distribution gap between virtual data and real data. In our opinion, Generative Adversarial Networks (GANs) can generate more realistic images for parallel vision research. We introduce some GANs and explain their utility in parallel vision.

关键词： Computer vision parallel vision parallel imaging Generative Adversarial Networks parallel system theory

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Reaching a consensus in networks of high-order integral agents under switching directed topologies

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INTERNATIONAL JOURNAL OF systems SCIENCE 2016年第8期47卷 1966-1981页

作者： Cheng, Long Wang, Hanlei Hou, Zeng-Guang Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Chinese Acad Space Technol Beijing Inst Control Engn Sci & Technol Space Intelligent Control Lab Beijing Peoples R China

Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent's full state is availab.e or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent's (estimated) state and the gain vector associated with the relative (estimated) states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. Also, the convergence of the transformed first-order integral agent's state indicates the convergence of the original high-order integral agent's state, if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative (estimated) states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be uniformly jointly quasi-strongly connected', which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.

关键词： multi-agent systems high-order integral agent linear dynamics switching directed topology consensus

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Research on Social Marketing Strategies with An Agent-based Propagation Model

Research on Social Marketing Strategies with An Agent-based ...

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20th World Congress of the International-Federation-of-Automatic-control (IFAC)

作者： Zeng, Shuai Li, Juanjuan Ni, Xiaochun Yuan, Yong Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Shandong Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha Hunan Peoples R China

Considering the increasing complexity of social networks and user behaviors, it is very challenging for advertisers to formulate their strategies of selecting proper initial seed users in their social marketing efforts. In this paper, we tackle this challenge by proposing an agent-based propagation model and injecting it into typical social networks with three types of structures, i.e., Erdos-Renyi random graph, Watt-Strogatz small world graph, and Barabasi-Albert scale-free graph. We instantiate this agent-based model with demographic characteristics extracted from real-world census data collected in China. By investigating the diffusion process of advertising infounation in these social networks, we can analyze and compare the perfounance of advertisers' targeting and influencer strategies. Our experimental results indicate that advertisers adopting influencer strategies should manipulate the initial well-connected seeds to deliver infounation only to the potential customers instead of a wide range of generic users. (C) 2017, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： social marketing infounation diffusion agent-based propagation model seeding strategy

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