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第42届中国控制会议

作者： Heng Deng Jiahui Zhou Zhiyao Zhao Liguo Zhang Key Laboratory of Computational Intelligence and Intelligent Systems Faculty of Information TechnologyBeijing University of Technology Key Laboratory of Industrial Internet and Big Data China National Light IndustryBeijing Technology and Business University

To achieve the purpose of large-scale visual tracking of targets based on Visual Sensor Networks(VSNs),this paper proposes a sensor placement optimization method to provide a suitable sensor configuration that satisfies the requirements of maximal network coverage and minimal reconstruction error as closely as *** the proposed method,some necessary models are established as the mathematical basis of the optimization ***,a multi-objective optimization function is designed to balance the coverage and reconstruction error,followed by an intelligent optimization algorithm that combines the advantages of Particle Swarm Optimization(PSO) and Genetic Algorithm(GA).Additionally,a dimensionality reduction technique is used to represent the sensor location information using a one-dimensional forward distance,which makes the search process simpler and increases execution ***,comprehensive comparison experiments are carried out in various settings to show the reliability and effectiveness of the optimization algorithms.

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Research on information interoperability of grains and oil supply chain based on identification resolution system of industrial internet

Research on information interoperability of grains and oil s...

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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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networking and Resource Allocation for industrial Internet Scenarios base on 5G networks

Networking and Resource Allocation for Industrial Internet S...

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Chinese Control and Decision Conference, CCDC

作者： Kun Tong Cunqian Yu Changyu Zhu Junyu Dou Bing Han Xiaolong Yu Big Data and Industrial Intelligence Networking Technology Laboratory Hangzhou Innovation Institute Beihang University Hangzhou China School of Computer Science Shenyang Aerospace University Shenyang China Pinming Technology Co. Ltd Hangzhou China General Administration Office Hangzhou Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In industrial Internet systems, data is the core, networks are the foundation, and security is the guarantee. In order to achieve the overall goal of upgrading industrial Internet technology, efficiently utilizing production factors and improving the level of Internet industry, the emerging technology of Internet scenario networking has emerged, which links and integrates all elements of the industrial production process. This paper is based on the existing networking mode of industrial Internet and explores the fusion networking mode of 5G+time-sensitive networking (TSN), analyzes the enhanced technology of 5G+new radio (NR) basic configuration. We strive to meet the requirements of industrial Internet scenarios with the optimal resource allocation strategy and improve the success rate of executing computing tasks in Internet systems. And we promote the deep integration of TSN technology and 5G technology, and lay the foundation for adapting to the trend of industrial Internet scenario networking. The advantages of combining edge-cloud fusion computing technology with elastic optical transport network in terms of migration delay, migration cost, and resource balance are verified through the example of industrial automated guided vehicles (AGVs).

关键词： 5G mobile communication Simulation Production Containers Streaming media Internet Delays

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Differential-Trust-Mechanism Based Trade-off Method Between Privacy and Accuracy in Recommender Systems

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5054-5068页

作者： Xu, Guangquan Feng, Shicheng Xi, Hao Yan, Qingyang Li, Wenshan Wang, Cong Wang, Wei Liu, Shaoying Tian, Zhihong Zheng, Xi Qingdao Huanghai University School of Big Data Qingdao China Tianjin University College of Intelligence and Computing Tianjin300350 China KLISS and School of Software Beijing100084 China Sichuan University School of Cyber Science and Engineering Chengdu610207 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China East China Normal University Shanghai200062 China Hiroshima University School of Informatics and Data Science Higashihiroshima739-8511 Japan Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University China Macquarie University School of Computing SydneyNSW2109 Australia

In the era where Web3.0 values data security and privacy, adopting groundbreaking methods to enhance privacy in recommender systems is crucial. Recommender systems need to balance privacy and accuracy, while also having the ability to overcome cold start problems. The Differential Trust Mechanism (DTM) introduced in this paper is such an approach. The DTM provides a unique use of Gaussian distributions in modeling trust relationships within data, offering a novel way to balance recommendation accuracy with user privacy. This mechanism innovatively applies differential privacy principles, using Gaussian noise addition to protect individual user data from inference attacks, while maintaining the integrity and utility of the overall dataset. Unlike traditional anonymization techniques that often compromise data utility or vulnerability to reverse engineering, DTM provides a robust solution by dynamically adjusting privacy levels based on the trustworthiness of data requests. By combining DTM with existing mainstream recommendation algorithms, the prediction accuracy of MAE and RMSE increases by at least 6.60% and 2.69%, respectively. This dual benefit positions DTM as a significant advancement in secure data processing, especially relevant for online businesses and platforms where personalized recommendations are crucial yet privacy concerns are paramount. © 2005-2012 IEEE.

关键词： Sensitive data

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A Robot Control Method based on Motor Imagery EEG Signals

A Robot Control Method based on Motor Imagery EEG Signals

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Robotics & Intelligent Manufacturing technology (ISRIMT), International Symposium on

作者： Xiaoqin Lian Mohao Cai Chao Gao Zitong Wangl Yelan Wu Wenyang Guan Yonggang Gong School of Artificial Intelligence Beijing Technology and Business University Beijing China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China

In recent years, brain-computer interface (BCI)-based research has become a research direction that has attracted much attention in the field of artificial intelligence, especially the brain-computer interface-based human-computer interaction system has a wide range of development forward. In this paper, a new recognition method is proposed for the problem of low accuracy of multi-classification recognition of EEG signals based on motor imagery, and the method is combined with Nao robot control to realize a human-computer interaction system based on brain-computer interface. In this study, the EEG data collected by EMOTIV EPOC FlEX was used as the experimental basis, and firstly, the wavelet packet decomposition was used to process the EEG signals, extract the four important frequency bands of EEG signals, namely, Alpha, Beta, Theta, and Gamma, and compute the differential entropy (DE) features of each band respectively, and reconstructed the three-dimensional according to the spatial characteristics of the brain electrodes EEG feature cube, and input the features into the convolutional neural network of the visual geometry group network for four classifications. Finally, the robot is controlled to realize different limb movements according to different classification results, thus realizing the function of human-machine interaction system and laying a technical foundation for intelligent control based on EEG signals.

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Research on recognition method of adolescent schizophrenia based on EEG feature fusion

Research on recognition method of adolescent schizophrenia b...

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IEEE/ICME International Conference on Complex Medical Engineering, CME

作者： Xiaoqin Lian Zitong Wang Mohao Cai Chao Gao Wenyang Guan Yonggang Gong School of Artificial Intelligence Beijing Technology and Business University Beijing China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China

ISBN: (数字)9798350316117

ISBN: (纸本)9798350316124

Schizophrenia is a common mental illness, and adolescent schizophrenia has a higher recurrence rate and disability rate than adult schizophrenia. In order to improve the recognition accuracy of adolescent schizophrenia, this study proposed a classification method of adolescent schizophrenia combined with artificial intelligence technology. In this method, the pre-processed original electroencephalogram (EEG) signals are decomposed by Discrete Wavelet Transform (DWT) and Ensemble Empirical Mode Decomposition (EEMD), respectively, and then the decomposed signals are extracted and the extracted features are fused. Finally, five machine learning models are used for classification. The results show that compared with DWT or EEMD methods alone, the feature fusion method proposed in this paper exhibits a higher accuracy in classification, in which the K-Nearest Neighbours (KNN) model achieves the best classification performance. The identification accuracy, recall, precision, F1 score, and Kappa coefficient of the model were 97.48 % , 95.71%, 99.56%, 97.59%, and 0.9494, respectively, which proved that the proposed method could effectively distinguish adolescent schizophrenia patients from healthy adolescents, and could assist doctors in disease analysis, providing a new method for the diagnosis of adolescent schizophrenia.

关键词： Accuracy Mental disorders Telemedicine Medical services Machine learning Feature extraction Brain modeling

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Sensor Placement Optimization and Rapid Deployment of a Class of Visual Sensor Networks

Sensor Placement Optimization and Rapid Deployment of a Clas...

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Chinese Control Conference (CCC)

作者： Heng Deng Jiahui Zhou Zhiyao Zhao Liguo Zhang Key Laboratory of Computational Intelligence and Intelligent Systems Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China

To achieve the purpose of large-scale visual tracking of targets based on Visual Sensor Networks (VSNs), this paper proposes a sensor placement optimization method to provide a suitable sensor configuration that satisfies the requirements of maximal network coverage and minimal reconstruction error as closely as possible. In the proposed method, some necessary models are established as the mathematical basis of the optimization process. Then, a multi-objective optimization function is designed to balance the coverage and reconstruction error, followed by an intelligent optimization algorithm that combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Additionally, a dimensionality reduction technique is used to represent the sensor location information using a one-dimensional forward distance, which makes the search process simpler and increases execution speed. Finally, comprehensive comparison experiments are carried out in various settings to show the reliability and effectiveness of the optimization algorithms.

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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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2021 China Automation Congress, CAC 2021

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

ISBN: (纸本)9781665426473

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. © 2021 IEEE

关键词： Cameras

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Research on Point-of-Interest Recommendation Method based on Graph Autoencoders and Long Short-Term Preferences 23

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

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Proceedings of the 4th International Conference on Artificial intelligence and Computer Engineering

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

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Building Health State Recognition Method Based on Multi-channel Convolution Neural Network Fusion 10

Building Health State Recognition Method Based on Multi-chan...

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10th IEEE data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Su, Tingli Li, Jian Yang, Aiqiang Jin, Xuebo Kong, Jianlei Bai, Yuting School of Artificial Intelligence Beijing Technology and Business University 100048 China China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University 100048 China

ISBN: (纸本)9781665424233

The identification of the health status of buildings has been paid more and more attention by all sectors of the society. The early warning of catastrophes or the assessment of the damage degree and residual life of building structures after catastrophes has become a hot topic for scholars from all over the world. In order to improve the performance of building health state recognition, a novel framework based on multi-channel convolution neural network fusion is proposed in this paper. By combining the output results of different convolution neural networks, temporal information and spatial information are used to achieve the accurate classification of building health status. Eventually, with the data collected by the sensor during the earthquake, the proposed framework is proved to be effective and superior. © 2021 IEEE.

关键词： Buildings

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