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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Li, Xuan Wang, Yutong Wang, Kunfeng Yan, Lan Wang, Fei-Yue School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Innovation Center for Parallel Vision Qingdao Academy of Intelligent Industries Qingdao266000 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

Offline training and testing are playing an essential role in design and evaluation of intelligent vehicle vision algorithms. Nevertheless, long-term inconvenience concerning traditional image datasets is that manually collecting and annotating datasets from real scenes lack testing tasks and diverse environmental conditions. For that virtual datasets can make up for these regrets. In this paper, we propose to construct artificial scenes for evaluating the visual intelligence of intelligent vehicles and generate a new virtual dataset called 'ParallelEye-CS'. First of all, the actual track map data is used to build 3D scene model of Chinese Flagship intelligent Vehicle Proving Center Area, Changshu. Then, the computer graphics and virtual reality technologies are utilized to simulate the virtual testing tasks according to the Chinese intelligent Vehicles Future Challenge (IVFC) tasks. Furthermore, the Unity3D platform is used to generate accurate ground-truth labels and change environmental conditions. As a result, we present a viable implementation method for constructing artificial scenes for traffic vision research. The experimental results show that our method is able to generate photorealistic virtual datasets with diverse testing tasks. © 2018 IEEE.

关键词： intelligent vehicle highway systems

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Collaborative Scheduling of Computing Tasks for Edge Computing

Collaborative Scheduling of Computing Tasks for Edge Computi...

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Chinese Automation Congress (CAC)

作者： Yue Liu Gang Xiong Fenghua Zhu Shichao Chen Liguo Zhang Xudong Liu School of Artificial Intelligence and Automation Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation and the Cloud Computing Center Chinese Academy of Sciences Beijng China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation and Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China Faculty of Information and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665426480

In the face of the rapid development of the IoT, the traditional cloud computing, while providing services for mobile intelligent devices, also brings problems such as network bandwidth occupation and communication delay. Edge computing can solve these problems to some extent. But too many offloaded tasks may result in an edge server becoming overloaded and execution delay of tasks increasing, the computing resources of idle edge devices are also not fully utilized. In order to make full use of the computing resources of idle edge devices in the local network and reduce the execution delay for computing tasks, this paper takes the average delay time for all end devices as the optimization objective and proposes an end-edge cooperative scheduling (EECS) model to schedule tasks for edge computing. The scheduling algorithm used in EECS model is improved based on MCT algorithm. We use the EdgeCloudSim to verily the EECS model and scheduling algorithm. The simulation results show that the average completion time of computing tasks in EECS model, compared with only single edge server (SEE) task computing model, can be reduced by up to three times. In addition, the system load can be balanced to a certain extent in local network.

关键词： Analytical models Scheduling algorithms Computational modeling Collaboration Scheduling Delays Servers

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Graph Attention Model Embedded With Multi-Modal Knowledge For Depression Detection

Graph Attention Model Embedded With Multi-Modal Knowledge Fo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wenbo Zheng Lan Yan Chao Gou Fei-Yue Wang School of Software Engineering Xi’an Jiaotong University Xi’an China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

With more than 300 million people depressed worldwide annually, depression is a global problem. The goal of depression detection is to improve diagnostic accuracy and availability, leading to faster intervention. The most important and challenging problem here is to design an effective and robust depression detection model. To this end, there are two challenges to overcome: 1) Multi-modal (audio, image, text, etc.) information must be jointly considered to make accurate inferences. 2) Existing deep learning-based work suffers from multi-modal data sufficiency problem. To address these issues, we propose a graph attention model embedded with multi-modal knowledge for depression detection. This approach learns not only reasonable embeddings for nodes in the knowledge graph, but also exploits medical knowledge to improve the performance of classification and prediction with the knowledge attention mechanism. Experimental results on the real-world datasets show that the proposed approach significantly improves the classification and prediction performance compared with other major state-of-the-art approaches, with guaranteed the robustness with each modality of multi-modal data. Overall, this paper shows how multi-modal knowledge attention mechanism and deep-learning-based networks can be combined to assist mental health patients and practitioners.

关键词： Biomedical imaging Robustness Data models Machine learning Task analysis Knowledge engineering

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Urban trip generation forecasting based on gradient boosting algorithm 1

Urban trip generation forecasting based on gradient boosting...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Zhishuai Xiong, Gang Zhang, Yu Zheng, Meng Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Traffic Planning Department Beijing China

ISBN: (纸本)9781665433372

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. © 2021 IEEE.

关键词： Decision trees

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ACP-based social computing and parallel intelligence： Societies 5.0 and beyond

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CAAI Transactions on Intelligence Technology 2016年第4期1卷 377-393页

作者： XiaoWang Lingxi Li Yong Yuan Peijun Ye Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Qingduo Academy of Intelligent Industries Qingduo 266100 China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN 46202 USA

Social computing, as the technical foundation of future computational smart societies, has the potential to improve the effectiveness of opensource big data usage, systematically integrate a variety of elements including time, human, resources, scenarios, and organizations in the current cyber-physical-social world, and establish a novel social structure with fair information, equal rights, and a flat configuration. Meanwhile, considering the big modeling gap between the model world and the physical world, the concept of parallel intelligence is introduced. With the help of software-defined everything, parallel intelligence bridges the big modeling gap by means of constructing artificial systems where computational experiments can be implemented to verify social policies, economic strategies, and even military operations. Artificial systems play the role of ＂social laboratories＂ in which decisions are computed before they are executed in our physical society. Afterwards, decisions with the expected outputs are executed in parallel in both the artificial and physical systems to interactively sense, compute, evaluate and adjust system behaviors in real-time, leading system behaviors in the physical system converging to those proven to be optimal in the artificial ones. Thus, the smart guidance and management for our society can be achieved.

关键词： Social computing Societies 5.0 Parallel intelligence Knowledge automation Cyber-physical-social system Artificial societies Computational ex-periments Parallel execution

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Plant growth architecture and production dynamics : A set of e-learning resources

Plant growth architecture and production dynamics : A set of...

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2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications, FSPMA 2016

作者： Jaeger, Marc De Reffye, Philippe Sabatier, Sylvie Caraglio, Yves Krit, Hatem Lafond, Marie-Helene Letort, Veronique Heuvelink, Ep Motisi, Matacha Zhang, BaoGui Kang, Mengzhen CIRAD AMAP Unit Montpellier34398 France University of Montpellier LIRMM ICAR Montpellier34095 France CentraleSupélec MAS Châtenay-Malabry92290 France WURL HPC Group Wageningen6700AA Netherlands CIRAD Pest and Disease Unit Montpellier34398 France Key Laboratory of Plant-Soil Interactions College of Resources and Environment China Agricultural University Beijing100193 China State Key Laboratory of Management and Control for Complex Systems LIAMA CASIA Beijing100190 China Qingdao Academy of Intelligent Sciences Qingdao266109 China

ISBN: (纸本)9781509016594

FSPM Plant and crop models are popular in the research community and there is increasing interest in their applications, from yield prediction to crop management optimization. A wide range of approaches and their software implementation can be found in the literature, with a large diversity in modelling scale (from plant organ to plot levels) and scope (structural, functional or both), as well as generic or specific aim. However, most of these approaches rely on similar key assumptions and methods related to different disciplines: Botany for plant structure, eco-physiology for production, statistics and dynamic system theory for parameter identification and estimation. Web disseminated plant and crop models learning supports are not numerous, often specific, and partial. We present here a set of e-learning resources, named 'Plant growth architecture and production dynamics' which describes the main notions from plant morphology and architecture, eco-physiology and applied mathematics necessary to the understanding of most plant and crop models, and their typology. In this paper, after an overview of existing learning resources on plant and crop modelling which, we believe, might be of interest for the whole research community, we present the proposed resource, which uses the GreenLab model as illustration, organized as follows: •Five standalone introductive disciplinary resources: Botanical notions, assumptions in eco-physiology, basis in data processing, introduction to models, introduction to GreenLab •An FSPM example in more details: GreenLab positioning, its basis, structure modelling, production modelling, and applications including model fitting and a study case •Tools: Inline (for structure analysis and statistics), online (access to fitters for instance) and access to download simulators and their documentation •Glossary, bibliography, web site collection, and a pedagogic kit for instructors Supported by the French Sustainable Development Virtual University

关键词： Glossaries

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Biomimetic inspiration for PKM torso design in humanoid robots

Biomimetic inspiration for PKM torso design in humanoid robo...

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IEEE International Conference on Mechatronics and Automation

作者： Hulin Huang Ceccarelli Marco Weimin Zhang Qiang Huang Zhangguo Yu Xuechao Chen Ministry of Education Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Beijing China Laboratory of Robotics and Mechatronics University of Cassino and South Latium Cassino Italy Ministry of Education International Joint Research Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (纸本)9781479970995

A new torso design of humanoid robot is proposed as a 3-DOF PKM (Parallel Kinematic Manipulator) structure by using results of a biomimetic analysis of torso in humans and animals. Main characteristics of torso in nature are presented from which design requirements are outlined for a solution in humanoid robot. The PKM torso is consist of two platforms and four UPU mechanisms. The usefulness of such a biomimetic inspiration is proved by a numerical simulation of the proposed PKM torso in its basic movements.

关键词： Torso Humanoid robots Joints Thorax Horses Payloads

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Embed Trajectory Imitation in Reinforcement Learning: A Hybrid Method for Autonomous Vehicle Planning

Embed Trajectory Imitation in Reinforcement Learning: A Hybr...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yuxiao Wang Xingyuan Dai Kara Wang Hub Ali Fenghua Zhu State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Anhui University Hefei China

Learning-based autonomous vehicle trajectory planning methods have shown excellent performance in a variety of complex traffic scenarios. However, the existing imitation learning (IL) and reinforcement learning (RL) algorithms still have their limitations, such as poor safety and generalizability for IL, and low data efficiency for RL. To leverage their respective advantages and mitigate the limitations, this paper proposes a novel hybrid RL algorithm for autonomous vehicle planning, where IL is embedded in it to guide its exploration with expert knowledge. Different from existing approaches, we use multi-step trajectory prediction instead of behavior cloning as the IL method integrated with online RL. Through such design, we make a further step in the research about how expert demonstration can be helpful to RL. Moreover, we conduct parallel training and testing of the algorithm based on real-world driving data. Experimental results demonstrate that our proposed approach outperforms standalone IL and RL methods, and performs better than RL methods enhanced by behavior cloning.

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Video vehicle detection through multiple background-based features and statistical learning

Video vehicle detection through multiple background-based fe...

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International Conference on intelligent Transportation

作者： Mingxia Sun Kunfeng Wang Ming Tang Fei-Yue Wang Jinfeng Yang Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences (CAS) Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) Beijing China

This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.

关键词： Vehicles Feature extraction Training Image edge detection Vehicle detection Meteorology Radio frequency

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A simple and effective method for the use of chemical accident cases

A simple and effective method for the use of chemical accide...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Sifeng Jing Xiwei Liu Chuanzhong Xu Institute of Smart Education Systems Qingdao Academy of Intelligent Industries The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Refinery No.2 PetroChina Fushun Petrochemical Company Fushun China

ISBN: (纸本)9781509029280

Past chemical industry accident cases include volume valuable information like precursor, equipment involved, relevant causes and experiences of emergence response. In-depth analysis and fully utilization of these information play an important decision-making role in controlling risk and enhancing safety performance in chemical industry. In this work, a survey of use of accident data is conducted around the world and the result shows that the detailed use of these information play an important role in preventing accidents. However, in China there much more needs to be done in the application of accident data. According to the needs of safety management of chemical plants, a simple and effective method of the use of chemical accident cases is proposed based on collecting and analyzing of a large number of chemical accident cases. And finally, a utilization system of chemical industry accident cases is implemented through Microsoft access 2010. The result shows that the system can realize the effective management and use of past chemical accident information.

关键词： Accidents Chemicals Databases Petrochemicals Chemical industry Safety management

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