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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Li, Enjing Li, Xiang Zhao, Zhigang Wu, Lu Wang, Yinglong 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 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Deep learning is particularly important in the field of time series data analysis, and has been applied to tasks such as marine data prediction. However, there is a’concept drift’ problem in marine observation data, which leads to performance degradation and catastrophic forgetting of traditional deep learning models used in online scenarios. For this reason, this paper proposes OL-TCN, an adaptive temporal convolutional neural network online prediction deep learning model, which is more suitable for marine online learning and inference scenarios. We make the following innovations: Inside the model, model performance is enhanced by incorporating a multi-head attention mechanism and introducing an automatic machine learning enhancement in the residual cell. Outside the model, a model repository approach is used to effectively cope with the complexity and evolutionary challenges of data streams. The experimental results verify that the OL-TCN model is effective and feasible in marine time series processing. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Spatio-temporal data

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MTRNet: An Efficient Hybrid network Model for Surface Defect Detection of Sheet Metal 36

MTRNet: An Efficient Hybrid Network Model for Surface Defect...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Hao, Huijuan Zhu, Sijian Chen, Yu Yi, Changle Zhao, Hongge Feng, Yue Yang, Rong 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 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Addressing the challenges posed by the insufficient computational power of low-spec devices to achieve the inference efficiency of prevailing deep learning models, alongside the variability in type and shape within industrial metal sheet datasets, while also catering to the demand for high-precision detection, this paper proposes a novel single-stage detection architecture termed MTRNet. In this paper, we introduce three key enhancements: Firstly, to reduce the number of parameters, we propose a novel Partial Depth Convolution (PDC) structure. By minimizing redundant computations, we aim to enhance the efficiency of spatial feature extraction. Secondly, we propose a Convolution Transformer (CTR) structure that replaces the self-attention module with a convolutional module. This modification addresses the computational inefficiency inherent in the self-attention mechanism of existing Visual Transformers (ViT). Finally, we introduce a novel Attention Convolution Transformer (ACTR) structure to enhance the extraction of global and local feature information. This architecture seamlessly integrates the strengths of both attention and convolution mechanisms, working synergistically to improve performance. Our proposed MTRNet network demonstrates superior detection performance compared to existing industrial standards, achieving a detection accuracy of 81.5% on the NEU-DET dataset. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep learning

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StarMFND: Star Topology Multi-Domain Fake News Detection 7

StarMFND: Star Topology Multi-Domain Fake News Detection

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7th International Conference on Data Science and information Technology, DSIT 2024

作者： Gu, Pengfei Chen, Yibing Bing, Wanlong Liu, Yahui Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection. © 2024 IEEE.

关键词： Fake News Detection Multi-Domain Learning Multi-Task Learning Social Media

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Land cover change detection with hyperspectral remote sensing images: A survey

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information Fusion 2025年 123卷

作者： Lv, Zhiyong Zhang, Ming Sun, Weiwei Lei, Tao Benediktsson, Jón Atli Liu, Tongfei Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Province Xi'An710048 China The department of Geography and Spatial Information Techniques Ningbo University Zhejiang Province NingBo315211 China The School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Shaanxi Province Xi'An710021 China The Faculty of Electrical and Computer Engineering The Rocket Force University of Engineering Reykjavik Iceland

Land cover change detection with hyperspectral remote sensing images (HyperCD), which focuses on monitoring land cover change on the Earth's surface, has attracted increasing attention due to its advantages and wide applications in capturing detailed information. Over the past two decades, significant progress has been made in HyperCD, with numerous methods successfully applied to real-world datasets. However, a comprehensive survey on the current state of HyperCD remains lacking. To advance the future research of HyperCD, this study presents a comprehensive survey of the published papers on HyperCD between 2000 and 2023. In this survey, the existing studies were analyzed from different perspectives, including the problems and challenges of HyperCD, open-access resource datasets and code analysis, a detailed taxonomy of major techniques for HyperCD, and potential future research directions. The primary objective of this survey is to provide peer researchers a "one-stop" understanding, offering a clear and systematic overview of the current challenges, major techniques, opportunities, and future research direction for HyperCD. Compared with the current studies about HyperCD, the main contribution of this survey lies in addressing the lack of comprehensive reviews within the HyperCD research community. © 2025 Elsevier B.V.

关键词： Surveying

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Research on the Application Mode of security Defense Service of the Closed-source power Industrial Control System 11

Research on the Application Mode of Security Defense Service...

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11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021

作者： Wang, Xiangqun Guo, Zhimin Lv, Zhuo Zhang, Xiaojian Yang, Wen Global Energy Interconnection Research Institute Co Ltd State Grid Key Laboratory of Information Network Security Nanjing210003 China State Grid Henan Electric Power Research Institute

ISBN: (纸本)9781665425278

Aiming at the long-life cycle, stable topology and predictable behavior characteristics of power industrial control system, the security defense service application mode of power industrial control system based on the combination of 'off-line learning' and 'on-line analysis' is studied. In the 'off-line learning' stage, the behavior pattern of the power industrial system is characterized and classified. Therefore, the multi-area basic data extraction and featurization can be established. In the 'online analysis' stage, the scenarios such as 'operation data extraction in different scenarios', 'abnormal behavior identification based on the baseline' and 'collaborative threat defense' are designed, and the technical requirements of the defense architecture and defense model corresponding to the power industrial control system security defense service application mode are summarized. Based on the case where machine learning method is applied in the anomaly detection, the effectiveness and correctness of the security application mode is verified. © 2021 IEEE.

关键词： Anomaly detection

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The Utility Model Relates to a Real-Time Interaction Method of Mobile Terminal information Based on power Emergency Scene 4

The Utility Model Relates to a Real-Time Interaction Method ...

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4th Annual International Conference on Data Science and Business Analytics, ICDSBA 2020

作者： Hou, Zhansheng Sun, Shijun Peng, Lin Yu, Zhen Song, Jinyin Wang, He Xu, Min Wang, Gang Bao, Xingchuan Yu, Hai He, Zhimin Zhu, Liang Xu, Xiyuan Zhang, Zehao Tang, Shiyang Global Energy Interconnection Research Institute State Grid Key Laboratory of Information Network Security Nanjing China State Grid ZheJiang Electric Power Co. Ltd Hangzhou China

ISBN: (纸本)9781728181646

At present domestic emergency information interaction lack of flexibility, after a natural disaster, on-site rescue workers with the rescue headquarters, emergency command center, sharing of information between were studied based on the scene of the electric power emergency mobile information real-time interactive methods, design a kind of power at the scene of the emergency mobile friendly each other, are at the scene of the emergency and emergency command center cluster information real-time interactive technology, gives the power emergency field information real-time interactive system prototype and power at the scene of the emergency information real-time interactive software and hardware integration solutions, to build the efficient method of multi-type information submitted by emergency disposal across departments and information interaction ability. © 2020 IEEE.

关键词： Disasters

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A Shortest Path Query Approach for Encrypted Graphs Based on Padding Dictionary Structure

A Shortest Path Query Approach for Encrypted Graphs Based on...

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2023 China Automation Congress, CAC 2023

作者： Yang, Ming Zhang, Kaiyang Mu, Chao Wang, Xin Liu, Yuanlong Zhang, Heng Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China School of Computer Engineering Jiangsu Ocean University Lianyungang China

ISBN: (纸本)9798350303759

Hybrid intelligence has emerged as an innovative approach that synergistically combines the strengths of human intelligence and artificial intelligence (AI) to address complex problems. In this paradigm, knowledge graphs play a pivotal role by supplying vital background information and contextual understanding to hybrid intelligence systems. However, due to limitations in local storage capacity, managing large-scale graph data often requires outsourcing to cloud servers. Nevertheless, the use of untrusted cloud servers poses a risk of potential user data leaks. To address these concerns, this paper presents an encryption scheme specifically designed to protect graph data, enabling queries involving intermediate nodes in the shortest path between two nodes within the graph. The proposed scheme ensures the privacy and security of outsourced data by employing a novel method that utilizes padding dictionary structure encryption. This method ensures strict limitations on the cloud server's capabilities, restricting it solely to query operations, it effectively prevents any compromise of user data or unauthorized access to sensitive information, users can securely store and process their graph data on servers, significantly reducing the potential risk of data leakage and unauthorized breaches. To validate the feasibility and effectiveness of the proposed solution, practical experiments were conducted using real-world data. © 2023 IEEE.

关键词： Cryptography

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Intelligent scenario computing workflow fusion design technology for power mobile operations 3rd

Intelligent scenario computing workflow fusion design techno...

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

作者： Wu, Haoran Xu, Min Wang, Xiao-Ling Zhu, Jun Huang, Xueling Peng, Lin Bao, Xingchuan State Grid Wuxi Power Supply Company Sheng China Global Energy Interconnection Research Institute Beijing China State Grid Key Laboratory of Information and Network Security Beijing China

ISBN: (纸本)9783030028039

The purpose of electric power enterprise management is to improve the efficiency and competitiveness of enterprises. For this purpose, advanced information management systems are needed to assist enterprise management. The essence of proposing enterprise automation information management is to use workflow technology to coordinate and control each management process. This article starts with the introduction of the concept of workflow, analyzes the nature of workflow, classification and several modes of workflow implementation. Based on this, the application of workflow in electric power enterprise management was introduced in view of the characteristics of electric power companies. Finally, a detailed analysis of the typical workflow in the management of power companies was conducted. It has certain guiding significance for enterprise information management. Zhenjiang company’s power grid production site standardization operation business application process, based on wireless private network research mobile terminal workflow processing technology, including mobile workflow definition and interpretation, mobile workflow driver, mobile workflow scheduling, mobile workflow management technology, design The mobile workflow basic engine can run and manage processes on the mobile terminal, thus providing mobile standardization operation functions, and automating and intelligentizing the work process management and integrating on-site operation processes. © Springer Nature Switzerland AG 2019.

关键词： Standardization

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A Threshold-Free Random Access Preamble Detection Method Based on Joint Clustering for 6G NTN

A Threshold-Free Random Access Preamble Detection Method Bas...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Zhen, Li Wang, Yue Sun, Teng Wang, Wenjing Yu, Keping Kharel, Rupak Xi'An University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Global Information and Telecommunication Institute Waseda University Tokyo Japan Manchester Metropolitan University Department of Computing and Mathematics United Kingdom

ISBN: (纸本)9781665423908

Thanks to its irreplaceable superiority in providing seamless coverage, satellite communication is expected to play an important role in supporting ubiquitous and continuous connectivity services for 6G wireless networks. With the pre-compensation of timing and frequency offset, we propose a novel random access (RA) preamble detection method for the high-dynamic non-terrestrial networks (NTN). Unlike the conventional threshold-based methods, the proposed method leverages a joint clustering algorithm to accurately capture the timing position corresponding to the first arrival path without noise variance estimation. Simulation results validate the effectiveness of the proposed method in a typical satellite non line-of-sight (NLOS) scenario, and indicate that our method can achieve a notable performance enhancement in terms of the error detection probability (EDP) and the timing mean square error (MSE), in comparison to the exiting methods. © 2021 IEEE.

关键词： Mean square error

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Research on network Intrusion Detection Technology Based on DCGAN 5

Research on Network Intrusion Detection Technology Based on ...

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5th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Chao, Wang Wenhui, Wang Jiahan, Dong Guangxin, Guo State Grid Beijing Electric Power Company Beijing China Global Energy Interconnection Research Institute Beijing China State Grid Key Laboratory of Information and Network Security Beijing China

Traditional network intrusion detection algorithms tend to lack learning in a small number of classes due to data imbalance. In reality, intrusion detection systems pay more attention to the detection accuracy of a small number of classes, that is, attack samples. In order to improve the detection accuracy of intrusion detection system, a network intrusion detection method based on deep convolution generative adversarial networks (DCGAN) was proposed. Firstly, the sample data of network intrusion is preprocessed, and the character data set is replaced by image data. Then, DCGAN is used to train and test the sample data. Both the generator and the discriminator are constructed by CNN. The generator is used to construct attack samples, balance the number of training samples, and solve the over fitting problem caused by insufficient training samples. Finally, the trained discriminator is used to test the classification accuracy of samples. Experimental results show that, compared with the traditional algorithm, the proposed algorithm can not only balance the detection accuracy of various types of samples, but also has higher detection accuracy for attack samples. © 2021 IEEE.

关键词： Generative adversarial networks

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