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2022 International Symposium on Artificial intelligence Control and Application technology, AICAT 2022

作者： Xu, Boyu Xu, Jiping Chai, Senchun Jin, Jian Yu, Chongchong Wang, Zhaoyang School of Artificial Intelligence Beijing Technology and Business University China China Key Laboratory of Light Industry Internet and Big Data China School of Automation Beijing Institute of TechnologyChina China Institute of Industrial Internet and Internet of Things China Academy of Information and Communications Technology China

ISBN: (纸本)9781510656710

The grains and oils supply chain has many characteristics, such as long life cycle, complex links, multiple sources of information, fragmented data, and many risk factors, etc. Based on the industrial Internet identification resolution system, the method and technology of information exchange in the whole supply chain of grains and oils are studied. Through the analysis of my country's industrial Internet identification resolution system, design identification and resolution connection middleware architecture. The identification resolution system is the nerve hub that supports the interconnection of all things in industry. By opening up the data of the whole supply chain of grains and oils, it will realize the extensive interconnection of upstream and downstream industries and cross-fields. It breaks the "information island"and ensures the quality and safety of the whole life cycle of grains and oils. On this basis, a feasible information exchange framework for the whole supply chain of grains and oils is constructed. This has practical implications. © 2022 SPIE.

关键词： Food safety

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A Novel Distributed K-Means Clustering Algorithm for big Text data 23

A Novel Distributed K-Means Clustering Algorithm for Big Tex...

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7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Li, Min Li, Meijing Cheng, Yonglong Ryu, Keun Ho College of Information Engineering Shanghai Maritime University Shanghai201306 China Big Data and Artificial Intelligence Laboratory Industrial and Commercial Bank of China Software Development Center Shanghai201306 China Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399883

With the advancement of diverse industries, text clustering has garnered significant attention in fields such as data mining, document retrieval, and pattern classification. The K-Means algorithm, a simple, efficient, and widely adopted clustering approach, has been extensively studied for its applicability in text data analysis. However, the exponential growth of information presents a formidable challenge when clustering large-scale data. The traditional K-Means algorithm proves ineffective in handling high-dimensional, massive text data, and the initial centroid selection significantly impacts the clustering outcomes. To address these limitations, this research introduces a clustering algorithm based on the virtual centroid and nearest centroid strategy. Furthermore, we incorporate the MapReduce parallelization framework to tackle the clustering problem associated with vast text data. Experimental results demonstrate the algorithm's commendable scalability and effective handling of large datasets. © 2023 ACM.

关键词： MapReduce

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Optimal Deployment of Underwater Sensor Networks Based on Hungarian Algorithm 7th

Optimal Deployment of Underwater Sensor Networks Based on Hu...

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7th International Conference on Artificial intelligence and Security, ICAIS 2021

作者： Liang, Jinglin Sun, Qian Wang, Xiaoyi Xu, Jiping Wang, Li Zhang, Huiyan Yu, Jiabin Cheng, Gongxue Yang, Fengbo Cao, Ning School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China China Light Industry Laboratory of Industrial and Big Data Beijing Technology and Business University Beijing100048 China Beijing Laboratory for Intelligent Environmental Protection Beijing Technology and Business University Beijing100048 China Shandong Chengxiang Information Technology Co.Ltd Dezhou253000 China

ISBN: (纸本)9783030786144

Underwater sensor networks are widely used in water quality monitoring and underwater resources detection. The deployment of sensor nodes greatly affects the monitoring performance of the network. After the initial deployment of underwater sensor nodes, due to the influence of water flow, the sensor nodes will deviate from the initial position, which will affect the coverage of the network. Therefore, an effective algorithm is needed to deploy the underwater sensor networks. In this paper, MCM flow movement model is introduced to simulate the influence of water movement on sensor node position. Then, the Hungarian algorithm is used to redeploy the sensor nodes. The simulation results show that, compared with direct movement, Hungarian algorithm can achieve the effective deployment of the network with less mobile distance and better prolong the network life cycle. © 2021, Springer Nature Switzerland AG.

关键词： Sensor nodes

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Research on Invulnerability of WSN Based on Small World Characteristics of Complex Networks 7th

Research on Invulnerability of WSN Based on Small World Char...

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7th International Conference on Artificial intelligence and Security, ICAIS 2021

作者： Cheng, Gongxue Sun, Qian Wang, Xiaoyi Xu, Jiping Wang, Li Zhang, Huiyan Yu, Jiabin Cao, Ning School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China China Light Industry Laboratory of Industrial and Big Data Beijing Technology and Business University Beijing100048 China Beijing Laboratory For Intelligent Environmental Protection Beijing Technology and Business University Beijing100048 China Shandong Chengxiang Information Technology Co. Ltd. Dezhou253000 China

ISBN: (纸本)9783030786205

Network security is an important indicator of wireless sensor networks, so how to improve the security performance of the network is an important issue of wireless sensor networks. Because the number and location of heterogeneous nodes play a decisive role in the performance of the network, this paper focuses on the optimal number and the deployment of heterogeneous nodes. By studying the optimal number and deployment of heterogeneous nodes, the network performance can be improved. Moreover, the invulnerability of the sensor network is also discussed. The simulation results show that the network efficiency and maximum connectivity are improved after the optimization of heterogeneous nodes, which proves the effectiveness of the algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Network security

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Research on Point-of-Interest Recommendation Method based on Graph Autoencoders and Long Short-Term Preferences 4

Research on Point-of-Interest Recommendation Method based on...

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4th International Conference on Artificial intelligence and Computer Engineering, ICAICE 2023

作者： Lian, Xiaoqin Mi, Jiachen Gao, Chao Chen, Xiang Mai, Weimin Guan, Wenyang Gong, Yonggang Li, Jin School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China School of Electronics and Information Technology Sun Yat-sen University Guangdong Province Guangzhou510006 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9798400708831

Point-of-Interest (POI) recommendation is one of the most important tasks in the research of Location-based Social Networks (LBSNs). To solve the spatial sparsity problem in POI recommendation, this study proposed a recommendation model based on graph autoencoder (GAE) and long short-term preferences applied to location recommendation. Firstly, the POI transfer map of the check-in sequence is constructed, from which the hidden links between each POI are extracted using GAE to alleviate the troubles caused by data sparsity. Secondly, a long short-term preference extraction module is incorporated into the model, considering the influence of user preferences on user decisions. Finally, the proposed GELS model was tested on the Foursquare dataset and compared with results from other models. The GELS model in this study achieved Recall@10 scores of 0.6933 and 0.5099 on two different datasets, surpassing other models and demonstrating its superior performance in POI recommendation. The results show that GELS model can provide new ideas for POI recommendations. © 2023 ACM.

关键词： Recommender systems

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Research on data value evaluation of railway construction period based on ahp-grey clustering method

Research on data value evaluation of railway construction pe...

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2022 International Workshop on Automation, Control, and Communication Engineering, IWACCE 2022

作者： Lian, Xiaoqin Yang, Kai Gao, Chao Wu, Yanhua Cheng, Zhibo Wu, Yelan Gong, Yonggang Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100081 China

ISBN: (纸本)9781510660915

In the hierarchical storage system, the data value needed to be evaluated to formulate rules data for hierarchical storage and migration. To further research the data hierarchical storage system in railway construction period and how to allocate the resources of the storage media with different performances, the researches on evaluation of the data value in railway construction period were pursued in the paper. Based on the business and non-business characteristics of the railway construction period, the evaluation indicator system of data value in railway construction period were designed, which can provide a comprehensive reference for the model. Combined with the quantification standard of evaluation indicators of data value in railway construction period designed in this paper, the complexity of indicator measurement can be reduced. In order to further improve the accuracy of model evaluation and meet data storage requirements, a comprehensive data value evaluation of railway construction period based on analytic hierarchy process (AHP)-grey clustering method was proposed. A simulation was carried out based on the data actually stored in a railway construction period, and the applicability of the model in the hierarchical storage system was proved by the geometric distribution of the results. In order to further verify the accuracy of the data value evaluation model proposed in this paper, its evaluation results were compared with that of the traditional model based on the same group of data. Experimental results shown that the data value model proposed in this paper was more accurate and adaptable than the traditional model in railway construction period, and can better meet the application requirements of the actual railway hierarchical storage system. © 2022 SPIE. All rights reserved.

关键词： Analytic hierarchy process

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Tunnel Disease Detection Based on Spark and Deep Learning 8th

Tunnel Disease Detection Based on Spark and Deep Learning

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8th International Conference on Artificial intelligence and Security , ICAIS 2022

作者： Zhao, Xia Zheng, Ying Yu, Chongchong You, Xiangjun Zhao, Lu China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University Beijing100048 China School of Artificial Intelligence Beijing Technology and Business University Beijing100048 China Zhejiang HuaZhan Institute of Engineering Research and Design Ningbo315012 China Secregen Technology San MateoCA94402 United States

ISBN: (纸本)9783031067662

Aiming at metro shield disease detection with LiDAR scanning data, this paper proposes an approach of distributed parallel point cloud data processing and shield disease detection. The approach is based on HDFS and Spark, and converts the LiDAR point cloud data into the orthographic projection space, and generates projected image data efficiently. Based on the optimized deep neural network YOLOv5 target detection model, our approach detects four types of metro shield diseases and labels their locations and the classifications on the orthographic projection image. The experiments show that our distributed parallel process framework archives the speedup ratio of 4.96 to the approach of a single server on the LiDAR data processing. Using the optimized YOLO v5 model, with the appropriate IOU threshold, the accuracy of the water seepage and spalling detection box is higher than 90%, and that of the cracks and the cracks on joints is higher than 80%. Our approach can improve the efficiency and accuracy of metro shield disease detection significantly, and promote the intelligence of metro operation and maintenance in the rail transit industry. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical radar

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Semi-direct Visual SLAM Algorithm Based on Improved Keyframe Selection

Semi-direct Visual SLAM Algorithm Based on Improved Keyframe...

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Chinese Automation Congress (CAC)

作者： Yuxin Xie Xiaoqin Lian Jie Liu Yelan Wu Zhiyao Zhao School of Artificial Intelligence China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University Beijing China

ISBN: (纸本)9781665426480

In order to meet the continuous improvement of real-time performance, positioning accuracy and robustness for visual Simultaneous Localization and Mapping (SLAM), and to address the problem that sparse maps are difficult to directly use for autonomous navigation, a semi-direct visual SLAM algorithm based on improved keyframe selection is proposed. It is based on ORB-SLAM2 framework. The front-end fuses feature-based method and direct method to get the camera initial poses. By minimizing the re-projection error to optimize the pose, it obtains a more accurate pose estimation. The proposed redundant keyframe deletion algorithm is applied in the back-end. It is based on the relative motion distance between keyframes, so as to delete the keyframes with overlapping visual fields and reduce the number of keyframes. The filtered keyframes are used for closed-loop detection and dense point cloud map construction. Then the map is filtered and converted into an octree map with higher storage efficiency. Experiments show that the algorithm can improve the real-time performance while ensuring the positioning accuracy, and has strong robustness to the sparse texture or camera’s fast-moving scene, and it can reconstruct the 3D maps of different environment.

关键词： Point cloud compression Visualization Simultaneous localization and mapping Octrees Pose estimation Filtering algorithms Cameras

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