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IEEE Joint International information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zhenwan Zou Yingsa Hou Huiting Yang Bin Wang Ruxia Yang Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Maintenance company of State Grid Xinjiang Electric Power Co. Ltd. Urumqi China Global Energy Interconnection Research Institute Co. Ltd. Nanjing Jiangsu China State Grid Key Laboratory of Information & Network Security Nanjing Jiangsu China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Aiming at the current status and risks of industrial control system information security, a security protection system was proposed from the technical and management levels. At the technical level, it strengthen security protection by setting up firewall protection, isolating engineer stations, conducting system security tests, and deploying network monitoring. At the management level, it improves safety management through measures such as real-time maintenance and updating of existing management systems and safety technology standards. Finally, the feasibility of this protection system was proved by experiments, which has certain reference significance for the research on safety protection of industrial control systems.

关键词： Industrial control information security Safety management Maintenance engineering Real-time systems Standards Monitoring

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Remote Healthcare Monitoring System for Aging Population Based on IoT and Big Data Analysis

Remote Healthcare Monitoring System for Aging Population Bas...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qiang Liu Songlin Sun Liang Gong Zhongle Wu Mohamed Cheriet Michel Kadoch National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Academy of Broadcasting Planning NRTA China ETS University of Quebec Montreal Canada

ISBN: (数字)9781728157849

ISBN: (纸本)9781728157856

As the population ages, this will put tremendous pressure on pensions and the medical system. Health insurance for the elderly poses a major challenge to the allocation of limited medical resources. The Internet of Things (IoT) is widely regarded as a potential solution to relieve pressure on healthcare systems. With the rapid development of wireless communication technologies such as backscatter communication, Wi-Fi and 5G, in vivo and in vitro medical devices have been greatly developed. In this article, we propose a real-time big data healthcare analysis system based on the combination of shallow neural networks and deep neural networks. The system consists of two parts: the detection of fog layer abnormality based on shallow neural network and the diagnosis of cloud disease based on deep neural network. The system can not only make full use of the real-time nature of the fog layer but also give full play to its powerful computing power based on reducing the computing pressure of the cloud layer.

关键词： Cloud computing Neural networks Sociology Real-time systems Internet of Things Statistics Medical diagnostic imaging

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Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation

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IEEE Journal on Selected Areas in Communications 2025年

作者： Wu, Shangrui Li, Yupeng Wang, Wenhua Guo, Jianxiong Fan, Wentao Liu, Qin Jia, Weijia Yu, Shui Cao, Jiannong Wang, Tian Beijing Normal-Hong Kong Baptist University Guangdong Provincial/Zhuhai Key Laboratory of IRADS Department of Computer Science Zhuhai China Hong Kong Baptist University Hong Kong Hong Kong Hong Kong Baptist University Department of Interactive Media Hong Kong Hong Kong Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an China Hunan University College of Computer Science and Electronic Engineering Changsha China University of Technology Sydney School of Computer Science Sydney Australia The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

The integration of edge computing with deep neural networks (DNNs) is crucial for intelligent industrial cyber-physical systems. Typically, deploying DNNs on heterogeneous edge devices relies on methods like model compression and partitioning. However, these approaches often result in homogeneous models across devices. This homogeneity limits the collective capability of edge computing systems, particularly in terms of generalization to diverse data distributions and adaptation to dynamic industrial environments. In this work, we propose to treat each DNN on an edge device as an independent model, aggregating their capabilities via ensemble learning to enhance generalization and dynamic adaptability. To realize this, we introduce the Adaptive Ensemble Knowledge Distillation Framework (AEKDF), combining cloud-based model training with edge computing based collaborative inference. In the cloud, AEKDF develops an enhanced Born Again network that generates diverse, lightweight models tailored to specific edge devices through knowledge distillation. This process ensures model diversity which is critical to effective ensemble learning. On the edge, AEKDF employs an adaptive ensemble technique that aggregates prediction logits across devices, enabling rapid adaptation to changing environments and maintaining inference efficiency. Our extensive evaluations conducted on a realistic prototype demonstrate the substantial boost in predictive performance achieved by our AEKDF, showcasing a 4% to 10% accuracy improvement on the CIFAR-100 compared to conventional single-model approaches, while maintaining low latency. © 1983-2012 IEEE.

关键词： Deep neural networks

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Research on 3D lightweight engine technology for power grid service scenarios 3rd

Research on 3D lightweight engine technology for power grid ...

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3rd International Conference on Intelligent, Interactive Systems and Applications, IISA2018

作者： Wang, Gang Zhang, Xiaodong Zhu, Chengzhi Wang, He Peng, Lin Xu, Min Global Energy Interconnection Research Institute Co. Ltd NanjingJiangsu210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China State Grid Corporation of China Beijing100031 China State Grid Zhejiang Electric Power Co. Ltd Hangzhou310007 China

ISBN: (纸本)9783030028039

The development of information display interaction from planar 2D to stereoscopic 3D has become increasingly mainstream. In the future, there will be more and more 3D content creators and consumers. The core model engine is standardized, lightweight, cross-platform and 3D-based. The interactive content library has become a hot topic in the IT industry. This paper studies the mainstream model file conversion compression technology based on the characteristics of the power grid business, and realizes the compression and weight reduction of the three-dimensional model. In this paper, compressive sensing-based 3D model compression method is used to achieve rapid compression of server-side models, which can greatly reduce the model space and increase the loading speed of the model. Compressed sensing compresses data at the beginning of data acquisition. When sampling, it does not follow the frequency twice as high as the maximum cut-off frequency of the initial signal as described by Shannon Nyquist Theorem, which greatly reduces the amount of data sampled, and does not require subsequent data recompression, which can greatly increase the model’s compression efficiency and quality. First, get the original model and split the model data into material data, animation data, grid data, node tree data, and map data. According to the model data classification, compression-sensing algorithm based on the business characteristics of power grid services was used to compress the model for sampling. Get a discrete representation of the model after sampling. Obtain the compressed model, and combine the material data, animation data, grid data, node tree data, and map data according to the characteristics of the power business and associate it with the unique identifier of the business model. The compression of mainstream model files based on the characteristics of power grid services can effectively improve the operational efficiency of 3D models. The research results lay a solid fou

关键词： Efficiency

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Optimizing network resource allocation with graph pointer neural network in large-scale AI systems

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Digital Communications and networks 2024年

作者： Peiying Zhang Yilin Li Athanasios V. Vasilakos Konstantin Igorevich Kostromitin Jianyong Zhang Mohsen Guizani Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710071 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250013 China Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Physics of Nanoscale Systems South Ural State University Chelyabinsk 454080 the Russian Federation Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Key Lab of All Optical Network and Advanced Telecommunication of EMC Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi 999041 United Arab Emirates

For increasingly complex communication demands of large-scale AI communication systems, the Space-Air-Ground Integrated network (SAGIN) better caters to demands but also raises concerns about resource scarcity and diversity. This paper innovatively combines Graph Pointer Neural networks (GPNN) and Reinforcement Learning (RL) to enhance resource allocation efficiency. The method leverages the advantages of GPNN in handling graph data and RL in optimizing decisions in dynamic environments. It also targets the optimization goal of maximizing resource allocation while minimizing deployment latency. This paper begins by modeling SAGIN and elucidating the SAGIN logical architecture based on Software-defined networking (SDN). Subsequently, it introduces an SFC deployment algorithm aimed at joint optimization of resource allocation and latency. The algorithm leverages GPNN and RL to deploy virtual nodes and links, with the goal of optimizing resource allocation and deployment latency. Experiment findings conclusively demonstrate that the efficacy of proposed algorithm in effectively weighing limited heterogeneous resources and minimum mapping delay. Notably, when compared to three other SFC mapping algorithms MLRL, NFVdeep, and RL, the proposed algorithm consistently outperforms them, with an average improvement of 10.17% in long-term average reward/cost, 11.21% in link resource utilization ratio, 15.34% in node resource utilization ratio, and 16.38% in acceptance ratio.

关键词： Space-air-ground integrated network Topological structure Reinforcement learning Service function chain Graph pointer neural network

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Correlation-Aware Virtual Machine Placement in Data Center networks 7th

Correlation-Aware Virtual Machine Placement in Data Center N...

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7th EAI International Conference on Cloud computing, CloudComp 2016 and 1st EAI International Conference on security and Privacy in New computing Environments, SPNCE 2016

作者： Chen, Tao Zhu, Yaoming Gao, Xiaofeng Kong, Linghe Chen, Guihai Wang, Yongjian Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9783319696041

The resource utilization (CPU, memory) is a key performance metric in data center networks. The goal of the cloud platform supported by data center networks is achieving high average resource utilization while guaranteeing the quality of cloud services. Previous work focus on increasing the time-average resource utilization and decreasing the overload ratio of servers by designing various efficient virtual machine placement schemes. Unfortunately, most of virtual machine placement schemes did not involve the service level agreements and statistical methods. In this paper, we propose a correlation-aware virtual machine placement scheme that effectively places virtual machines on physical machines. First, we employ Neural networks model to forecast the resource utilization trend according to the historical resource utilization data. Second, we design correlation-aware placement algorithms to enhance resource utilization while meeting the user-defined service level agreements. The results show that the efficiency of our virtual machine placement algorithms outperform the previous work by about 15%. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Virtual machine

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Global context aware RCNN for object detection

arXiv

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arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Xie, Haoyu Zhu, Mai Tie, Ming Chen, Junxin School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Science and Technology on Space Physics Laboratory Beijing100076 China College of Medicine and Biological Information Engineering Northeastern University Shenyang110819 China

RoIPool/RoIAlign is an indispensable process for the typical two-stage object detection algorithm, it is used to rescale the object proposal cropped from the feature pyramid to generate a fixed size feature map. However, these cropped feature maps of local receptive fields will heavily lose global context information. To tackle this problem, we propose a novel end-to-end trainable framework, called Global Context Aware (GCA) RCNN, aiming at assisting the neural network in strengthening the spatial correlation between the background and the foreground by fusing global context information. The core component of our GCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement, respectively. Specifically, we leverage the dense connection to improve the information flow of the global context at different stages in the top-down process of FPN, and further use the attention mechanism to refine the global context at each level in the feature pyramid. In the end, we also present a lightweight version of our method, which only slightly increases model complexity and computational burden. Experimental results on COCO benchmark dataset demonstrate the significant advantages of our approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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A vulnerability scanning scheme based on attack graph for smart grid industrial control system

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IOP Conference Series: Earth and Environmental Science 2021年第1期645卷

作者： Kai Xu Xiangqun Wang Hao Xu Ning Dong Meng Han Xianfei Zhou State Grid Beijing Electric Power Company Beijing 100031 China Global Energy Interconnection Research Institute Co. Ltd Nanjing 210003 China State Grid Key Laboratory of Information & Network Security Nanjing 210003 China

As the core of national development, smart grid industrial control system has extremely high security requirements. However, due to the existence of many security vulnerabilities and lack of effective security protection measures, it is vulnerable to attacks from the Internet. Therefore, the vulnerability scanning of smart grid ICS has important research significance. Aiming at the problem that the smart grid ICS cannot bear the heavy system overhead, a safe and low system overhead vulnerability scanning scheme for smart grid ICS is innovatively proposed. This scheme introduces attack graph technology into vulnerability scanning, proposes vulnerability risk assessment indicators, and flexibly designs the scanning cycle according to the generated attack graph. Simulation results show that this scheme effectively reduces scanning costs while ensuring system security, and is better adapted to smart grid ICS.

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Transferring knowledge distillation for multilingual social event detection

arXiv

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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Research on Intelligent Monitoring and Maintenance Technology of Substation Based on Digital Twin

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Journal of Physics: Conference Series 2021年第1期2136卷

作者： He Wang Peng Lin Zhansheng Hou Shijun Sun Global Energy Interconnection Research Institute Nanjing Jiangsu 210003 China State Grid Key Laboratory of Information & Network Security Nanjing 210003 China State Grid ZheJiang Electric Power Co. LTD Zhejiang Hangzhou 310000 China

The design, research and development, test, operation and maintenance of power grid substation equipment are relatively independent. Related problems in each link cannot be comprehensively managed from the perspective of the full life cycle of equipment. Operation and maintenance personnel need to face data monitoring, network monitoring, equipment monitoring, business system monitoring, fault diagnosis and repair and other business scenarios. There are many types of information, complex association relationship, abstract and inefficient operation and maintenance information, "man-machine-object" can not be two-way cooperation and interaction, fault diagnosis and troubleshooting difficulties. Digital substation intelligent monitoring operational digital twin applications, digital intelligent substation operations provide operational monitoring service ability, realize the grid digital substation twin modeling and environment reconstruction, false or true merged superposition, two-way coordinated interaction, twin intelligence operations and panoramic visual monitoring, field devices for power grid operation maintenance, resource scheduling, and construction planning to provide guidance, Fully guarantee the safe and reliable operation of power grid equipment, realize the full life cycle management of power grid equipment, and improve the level of intelligence, visualization and digitalization of power grid.

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