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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Shuai Wang Lei Wang Wenji Mao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

The massive spread of false information has brought about severe security-related problems to individuals and society. To debunk misinformation automatically, fact checking has become an important task that aims at retrieving evidence from external sources to verify the truthfulness of a given claim. As knowledge graph (KG) is a classic external source for retrieving relevant evidence. Previous methods typically check a claim by making inferences over it. Entity category information can be utilized to strengthen both the learning and verification process. However, this information was largely ignored in previous research. To make better use of the category information, in this paper, we propose a category-based framework for improving the performance of fact checking with KGs. We first learn prototypes for each category as their representatives, and then propose a prototype-based learning technique for effectively modeling the entity dependency in KG. We further develop a prototype matching technique to explore the category-level relations between head and tail entities for more robust verification. Experimental results on two benchmark datasets and a real-world dataset show that our framework can significantly improve the reasoning abilities of KG reasoning methods on Fact Checking task.

关键词： Conferences Prototypes Benchmark testing Cognition Security Task analysis Informatics

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Gong, Xiaoyan Tang, Ying Liu, Xiwei Jing, Sifeng Cui, Wei Liang, Joleen Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100084 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

Nowadays, AI education from Kindergarten to 9th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts. © 2020 IEEE.

关键词： Curricula

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Deep Behavioral Cloning for Traffic control with Virtual Expert Demonstration Under a Parallel Learning Framework ⁎

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IFAC-PapersOnLine 2020年第5期53卷 176-181页

作者： Xiaoshuang Li Fenghua Zhu Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100080 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China

Intelligent traffic signal control is necessary for improving traffic efficiency. These fast changing and challenging traffic scenarios and demands are generally handled by professional traffic engineers. However, it may take years of time and thousands of practices to train such an engineer. This paper proposes a deep behavioral cloning method to learn how to control the traffic signal effectively and efficiently from virtual expert demonstration. The method imitates promising working behavior of optimized offline solutions, and applies it to solve online traffic signal control problems of the similar scenario. Different traffic demand patterns are generated through a combination of different kinds of components. Then the virtual demonstration is constructed by getting an exclusive and optimized solution for each generated virtual traffic demand pattern through a heuristic random search method. After that, a deep neural network-based behavioral cloning method is employed to learn from the virtual demonstration and finish on-line traffic signal control task. The experimental results show that compared with other methods, the proposed method significantly reduces the waiting time and time loss in different situations. And the average traffic speed of the road network at different saturation levels can be improved by 1.58% to 11.54%.

关键词： intelligent transportation system behavioral cloning virtual demonstration parallel learning

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Short Term Traffic Flow Forecast Based on CM-GRU Networks

Short Term Traffic Flow Forecast Based on CM-GRU Networks

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Guo, Zhongzheng Zhong, Weifeng Zhu, Fenghua Li, Xiaoshuang Wang, Fei-Yue Xiong, Gang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781728128535

Intelligent transportation systems (ITS) have developed for a long time. The rise of deep learning has brought new vitality of the ITS. However, traffic flow data is usually time-correlated and highly randomized. The data distribution will also change dynamically. To actualize the forecasting of traffic flow accurately, we use the historical traffic information to predict the messages of the traffic flow at any time interval. This paper proposes an efficient traffic flow forecast architecture based on deep learning. The method combines the gated recurrent unit (GRU, a type of recurrent neural network) layers and one-dimension convolution layers. Since the performance of these models has a strong dependence on hyper-parameters, this paper conducts a large-scale search of the hyper-parameter space. At the same time, experiments on flow data show that the method proposed in this paper can achieve a better prediction accuracy. Experiments also show lower test errors compared with the existing approaches. © 2019 IEEE.

关键词： Recurrent neural networks

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Parallel Distance: A New Paradigm of Measurement for Parallel Driving

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1169-1178页

作者： Teng Liu Hong Wang Bin Tian Yunfeng Ai Long Chen the Vehicle Intelligence Pioneers Inc. Qingdao 266109 the Department of Automotive Engineering Chongqing UniversityChongqing 400044China the Mechanical and Mechatronics Engineering Department Waterloo UniversityN2L 3G1Canada the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China

In this paper, a new paradigm named parallel distance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then,Markov chain(MC) is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.

关键词： Artificial and physical system parallel distance parallel driving 3.0 parallel system rotational and accelerator signal

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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Chinese automation Congress (CAC)

作者： Xiaoxuan Zhao Haoyu Wang Xiujuan Wang Udom Lewlomphaisarl Dong Li Jing Hua Mengzhen Kang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.

关键词： Ventilators Green buildings Buildings Production Ventilation Data models Data mining

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An Island Remote Sensing Image Segmentation Algorithm Based on A Fusion Network with Attention Mechanism 3

An Island Remote Sensing Image Segmentation Algorithm Based ...

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2020 3rd International Conference on Computer Information Science and Artificial Intelligence, CISAI 2020

作者： Chen, Tianyuan Wang, Hongfei Liu, Hao Wu, Peng Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, in view of the problems that remote sensing images contain richer ratio information and complex background interference, we propose an island remote sensing image segmentation algorithm based on a fusion network with attention mechanism, called AFU-Net. The network is built on the basis of FC_U-Net [1]. An attention mechanism is added to pre-weight the shallow features before deep-shallow layer feature fusion, in order to enhance the response capability of the target features, suppress the background interference and improve the segmentation accuracy of the network. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of AFU-Net are greatly improved compared to FC_U-Net, and are also superior to other three state-of-the-art methods, U-Net, FCN, SegNet, which indicates the effectiveness of our method. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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An Island Remote Sensing Image Segmentation Algorithm Based on FC_U-Net Network

An Island Remote Sensing Image Segmentation Algorithm Based ...

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2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Tianyuan Wang, Hongfei Liu, Hao Du, Junrong Wu, Peng Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194813

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, for remote sensing images with rich details and different target scales, there are still some problems such as insufficient segmentation accuracy. To handle these problems, we propose an island remote sensing image segmentation algorithm based on FC_U-Net network. In this network, U-Net and FCN (Fully Convolutional Network) are improved respectively to form two new modules, and then the semantic features and detail features from the two modules are fused by a fusion module. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of FC_U-Net are better than other three state-of-the-art methods, U-Net, FCN (Fully Convolutional Network), SegNet, which indicates the effectiveness of our method. © 2020 IEEE.

关键词： Semantic Segmentation

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A Bioinspired retinal neural network for accurately extracting small-target motion information in cluttered backgrounds

arXiv

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arXiv 2021年

作者： Huang, Xiao Qiao, Hong Li, Hui Jiang, Zhihong School of Mechatronical Engineering Advanced Innovation Center for Intelligent Robots and Systems Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of’Hand-Eye-Brain’ Interaction Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

—Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks. Inspired by the neural circuitry of elementary motion vision in the mammalian retina, this paper proposes a bioinspired retinal neural network based on a new neurodynamics-based temporal filtering and multiform 2-D spatial Gabor filtering. This model can estimate motion direction accurately via only two perpendicular spatiotemporal filtering signals, and respond to small targets of different sizes and velocities through adjusting the dendrite field size of spatial filter. Meanwhile, an algorithm of directionally selective inhibition is proposed to suppress the target-like features in the moving background, which can reduce the influence of background motion effectively. Extensive synthetic and real-data experiments show that the proposed model works stably for small targets of a wider size and velocity range, and has better detection performance than other bioinspired models. Additionally, it can also extract the information of motion direction and motion energy accurately and rapidly. Copyright © 2021, The Authors. All rights reserved.

关键词： Ophthalmology

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A Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds

arXiv

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arXiv 2021年

—Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit of elementary motion vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (ASNN) that contains three main contributions. Firstly, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural network is modeled as a push-pull structure formed by ON and OFF pathways, which responds strongly to approaching motion while insensitivity to lateral motion. Finally, a method of directionally selective inhibition is introduced, which is able to suppress the translational backgrounds effectively. Extensive synthetic and real robotic experiments show that the proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making. © 2021, CC BY.

关键词： Mammals

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