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IEEE International Conference on Networking, Sensing and control

作者： Yegan Qian Gang Xiong Yanjie Yao Anhui Radio TV Station Hefei Hanteng Information Tech Company Hefei Anhui China State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

The paper discusses two important classification techniques, Fisher's linear discriminated analysis (FLDA) and Support Vector Machine (SVM). First, we propose a theoretical discussion, and then implement FLDA and SVM on several datasets of two classes and multiclass, a comparative experimental analysis among these two techniques aims at exploring and assessing the performance of FLDA and SVM classifiers. To sustain such analysis, the two classification techniques are compared with different training data sets and testing data sets. Different performance indicators have been used to support our experimental studies in a detailed and accurate way such as the classification accuracy. The results obtained on different datasets conclude that FLDA and SVM are valid and effective approaches for pattern classification and conclude their different performance and problems with different size datasets. Meanwhile, the paper employs a non-traditional method to get the training and testing data set, and concludes detailed pros and cons from the experiment results.

关键词： Support vector machines Training Testing Accuracy Vectors Head Magnetic heads

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Review on Teacher's Online Practice in Community

Review on Teacher's Online Practice in Community

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

作者： Xinzhu Wang Xiaoyan Gong Xiwei Liu Yan Yan Nan Zhang Jie Luo Faculty of Education Beijing Normal University Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Chinese Association of Automation Beijing China The State Key Laboratory of Software Development Environment Beihang University Beijing China

Teacher's Online Practice in Community (TOPIC) is an informal and practical online learning community that improves teachers' professional development. Domestic researches on TOPIC have begun to take shape. By sorting out relevant literature retrieved from China National Knowledge Infrastructure (CNKI), this article summarizes research topics such as knowledge management, teacher professional development, and community management and summarizes qualitative and quantitative research methods commonly used by domestic researches. This article finally puts forward some limitations of current researches in this field and forecasts the possible future research directions.

关键词： Knowledge transfer Training Reflection Organizations Focusing

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Differential-Evolution-Based Generative Adversarial Networks for Edge Detection

Differential-Evolution-Based Generative Adversarial Networks...

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International Conference on Computer Vision Workshops (ICCV Workshops)

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

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Since objects in natural scenarios possess various scales and aspect ratios, learning the rich edge information is very critical for vision-based tasks. Conventional generative adversarial networks (GANs) based methods for edge detection don't perform so well due to model collapse. In order to capture rich edge information and avoid model collapse as much as possible, we consider the learning of GANs as an evolutionary optimization and propose a novel method termed as differential-evolution-based generative adversarial networks (DEGAN) for richer edge detection. In particular, built upon GANs structure, we introduce an improved differential evolutionary algorithm to refine the input of generator, with fitness function evaluated by the discriminator. Experimental results on the well-known BSDS500 and NYUD benchmarks indicate our proposed DEGAN can achieve state-of-the-art performance while retaining a fast speed and validate its simplicity, effectiveness, and efficiency. The high quality of our results on edge detection with proposed DEGAN may promise to make other vision-based tasks work better.

关键词： Image edge detection Generators Gallium nitride Training Generative adversarial networks Evolutionary computation Evolution (biology)

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ACP based 3D Emergency Drills system for Petrochemical Plants

ACP based 3D Emergency Drills System for Petrochemical Plant...

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World Congress on intelligent control and Automation

作者： Sifeng Jing Changjian Cheng Gang Xiong Xiwei Liu Xiuqin Shang Weixing Wang State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Dongguan Research Institute of CASIA Cloud Computing Industrial Technology Innovation and Incubation Center Chinese Academy of Sciences

ISBN: (纸本)9781467313971

To further enhance emergency management skills of an organisation's emergency response personnel, emergency response training, especially 3D emergency drill, is currently becoming more and more important in the petrochemical sector. So, a novel 3D emergency drills system is designed and developed based on ACP approach, which can be used for mocking emergency response plan drills and evaluating the plan. A case study reveals that the performance of the system is good and the system can meet the needs of emergency response training and optimizing emergency response plan in petrochemical plants.

关键词： ACP Approach Artificial systems Computational Experiments Parallel Execution Mock-Drill Emergency Response Training

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Temporal-Difference Spatial Sampling and Aggregating Graph Neural Network for Crowd Flow Forecasting

Temporal-Difference Spatial Sampling and Aggregating Graph N...

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

作者： Xiaojie Bu Zebing Wei Zhishuai Li Xiao Wang Yuanyuan Chen Yunquan Song Yisheng Lv College of Science China University of Petroleum Qingdao China Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

With the development of traffic demand management, crowd flow forecasting arouse increasing interest. In order to tackle this spatial-temporal prediction problem, GNN (Graph Neural Networks) have been recently employed to model spatial dependencies, and usually spatial graphs are constructed based on geodetic distances or direct connections between nodes. However, it might be insufficient for spatial graphs to model real dependencies because they neglect spatial-temporal correlations. In this paper, a novel Temporal-Difference Spatial Sampling and Aggregating graph neural network (TDSSA) is proposed to model spatial-temporal dependencies. Firstly, a new sub-sparse spatial-temporal graph is constructed to represent spatial-temporal relationships among different nodes, then a TDSSA block is designed to extract features by spatial sampling and aggregating, and difference is utilized in TDSSA blocks to exploit temporal trend and increase robustness. Experiments show that the proposed method outperforms baseline methods.

关键词： Correlation Digital twin Conferences Aggregates Predictive models Feature extraction Market research

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The Smart Street Lighting system Based on NB-IoT

The Smart Street Lighting System Based on NB-IoT

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Chinese Automation Congress

作者： Shichao Chen Shuangshuang Han Gang Xiong Fei-Yue Wang Jia Xu Kun Wang Macau University of Science and Technology Chinese Academy of Sciences Macau China The Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing University of Chemical Technology Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries The State Key laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Qingdao China Beijing University of Chemical Technology Beijing China

With the development of communication technology, in order to meet the smart city's demand for intelligent infrastructure, the smart street lighting system based on NB-IoT is an effective method to realize the goal. This paper elaborates on the smart street lighting system's architecture, including the perception and control layer, the transport layer, the platform layer and the application layer. In addition, the actual system is built to verify. This system uses STM32 to build the perception and controller, and relies on China Telecom's NB-IoT communication network for the data transmission, finally using the IoT management platform-Huawei OceanConnect- to implement the connection management, the device management, the data calculation and the remote control functions. This system verifies the feasibility of the smart street lighting system based on NB-IoT technology, which provides the reference for the promotion and application. It is expected to provide intelligent, diversified, efficient and economical services for the smart cities.

关键词： Smart street lighting system NB-IoT IoT Smart city

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RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts

arXiv

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arXiv 2024年

作者： Yang, Jing Wang, Xiao Zhao, Yu Liu, Yuhang Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Artificial Intelligence Anhui University Hefei266114 China Qingdao Academy of Intelligent Industries Qingdao230031 China National Key Laboratory of Information Systems Engineering National University of Defense Technology China Macau University of Science and Technology 999078 China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing100098 China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China

Crowdsourcing is a critical technology in social manufacturing, which leverages an extensive and boundless reservoir of human resources to handle a wide array of complex tasks. The successful execution of these complex tasks relies on task decomposition (TD) and allocation, with the former being a prerequisite for the latter. Recently, pre-trained language models (PLMs)based methods have garnered significant attention. However, they are constrained to handling straightforward common-sense tasks due to their inherent restrictions involving limited and difficult-to-update knowledge as well as the presence of "hallucinations". To address these issues, we propose a retrieval-augmented generation-based crowdsourcing framework that reimagines TD as event detection from the perspective of natural language understanding. However, the existing detection methods fail to distinguish differences between event types and always depend on heuristic rules and external semantic analyzing tools. Therefore, we present a Prompt-Based Contrastive learning framework for TD (PBCT), which incorporates a prompt-based trigger detector to overcome dependence. Additionally, trigger-attentive sentinel and masked contrastive learning are introduced to provide varying attention to trigger and contextual features according to different event types. Experiment results demonstrate the competitiveness of our method in both supervised and zero-shot detection. A case study on printed circuit board manufacturing is showcased to validate its adaptability to unknown professional domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Parallel Education Based intelligent Tutoring systems Framework

A Parallel Education Based Intelligent Tutoring Systems Fram...

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IEEE International Conference on Networking, Sensing and control

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong Wei Cui Joleen Liang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA Squirrel AI Learning Shanghai China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

although online education has improved efficiency of learners' access to high-quality educational resources, real-time interaction between instructors and learners have not yet been achieved in online personalized learning. intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research & development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed.

关键词： Human computer interaction Conferences Education Market research Sensors Artificial intelligence

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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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IEEE International Conference on Networking, Sensing and control

作者： Xiaoyan Gong Ying Tang Xiwei Liu Sifeng Jing Wei Cui Joleen Liang Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA Squirrel AI Learning Shanghai China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Nowadays, AI education from Kindergarten to 9 th 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.

关键词： Ethics systematics Education Government Tools Sensors Artificial intelligence

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Agent-based traffic simulation and traffic signal timing optimization with GPU

Agent-based traffic simulation and traffic signal timing opt...

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International Conference on intelligent Transportation

作者： Zhen Shen Kai Wang Fenghua Zhu State Key Laboratory of Intelligent Control and Management of Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences (CAS) Beijing China Center of Military Computational Experiments and Parallel Systems Technology and College of Mechatronics Engineering and Automation National University of Defense Technology Changsha Hunan China

With the advantage of simulating the details of a transportation system, the “microsimulation” of a traffic system has long been a hot topic in the intelligent Transportation systems (ITS) research. The Cellular Automata (CA) and the Multi-Agent system (MAS) modeling are two typical methods for the traffic microsimulation. However, the computing burden for the microsimulation and the optimization based on it is usually very heavy. In recent years the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing. Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption. In this paper we build an MAS model for a road network of four signalized intersections and we use a Genetic Algorithm (GA) to optimize the traffic signal timing with the objective of maximizing the number of the vehicles leaving the network in a given period of time. Both the simulation and the optimization are accelerated by GPU and a speedup by a factor of 195 is obtained. In the future we will extend the work to large scale road networks.

关键词： Graphics processing unit Vehicles Roads Optimization Computational modeling Genetic algorithms Timing

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