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Simulation Research on Data Acquisition and Supervisory Control System of Distribution network Based on Vector Regression Model

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International Journal of High Speed Electronics and Systems 2025年第3期34卷

作者： Wu, Shujin Yuan, Ye Wu, Yuanjiang Xu, Wenli Network Security and Informatization Office Tianjin Maritime College Tianjin300350 China Party Committee Safety Work Department Tianjin Maritime College Tianjin300350 China Ideological and Political Education Department Tianjin Maritime College Tianjin300350 China

With the gradual maturity of deep learning and the Internet of Things, a smart grid is urgently needed to be established. For the intellectualization of the power grid, non-intrusive load monitoring (NILM) technology is the key step of intellectualization. The traditional load monitoring method is an invasive scheme, in which monitoring sensors are usually installed at the output end of each grid load. This method requires a lot of manpower and material resources in terms of equipment installation and maintenance, which is difficult to sustain. Therefore, monitoring electrical appliances are installed at the entrance of the distribution network to decompose the classification and operation status of the individual electrical appliances in the grid. Aiming at the problem of long-term multi-state electrical apparatus monitoring, this paper mainly optimizes the two optimal deep learning models Seq2Seq and WindowGRU based on activation function optimization, regular function optimization and neural network structure optimization. PReLU, Leaky-ReLU and ELU are used and tested. For the optimization based on regular functions, the value of F1-score is 0.9700, which is 0.1534 more than the optimization algorithm Seq2SeqLR in this paper. Meanwhile, it can also reach 0.8100 for other devices, which is 0.2373 more than the nilmTCN designed in this paper. The Recall value of the identification is improved to 0.6673, which is significantly higher than that of other models. It is proved that the research contents of this paper can improve the effective analysis of load energy consumption data, and can minimize unnecessary energy consumption to achieve the purpose of saving electricity. © 2025 World Scientific Publishing Company.

关键词： Shape optimization

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A wearable, real-time sEMG gesture classifier based on E-tattoo and CDF-CNN for prosthetic control 6

A wearable, real-time sEMG gesture classifier based on E-tat...

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6th IEEE Information Technology, networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Xu, Chuanbin Qu, Xiangwen Liang, Hongrui Chen, Da Shandong University of Science and Technology College of Electronics and Information Engineering Qingdao China Shandong University of Science and Technology Office of Network Security and Informatization Qingdao China

ISBN: (纸本)9781665460033

Bionic prosthetic hands are essential for the disabled to deal with most affairs of life independently. The surface electromyography(sEMG) is considered as a developing vigorous solution to realize the artificial limb system. However, most of the present devices are irritating to the skin, high cost and power energy, complexity, unnatural, which limits their promotion. This paper proposed a real-time bionic mechanical arm control system, based on the large-area flexible electronic tattoo(E-tattoo) and combining differential feature convolutional neural network (CDF-CNN). Similar to the tattoo attached on the skin, the electrode allows complying with the skin deformation comfortably and accommodation the local strains, providing long-term and robust monitoring of sEMG signals. Benefited from the convenient and low-cost fabrication and transferring to the skin surface, the large-area E-tattoo electrodes were applied to alleviate the requirement of controlling the accurate position of traditional Ag/AgCl electrodes. Moreover, a "sEMG feature map"is proposed by combining differential feature (CDF) to extract deep abstract features to improve the recognition effect, achieving 97.63% on average when using only two channels to classify 8 gestures. The proposed system is efficient, comfortable, natural and low-cost, which will help to facilitate the development and application of sEMG prosthesis. © 2023 IEEE.

关键词： Electrodes

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Prediction of Cyberspace security Data Based on the Markov Chain Model

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Applied Mathematics and Nonlinear Sciences 2023年第2期8卷 2539-2548页

作者： Zhang, Xinmiao Network Security and Informatization Office of Tianjin Normal University Tianjin300387 China

In the era of people's access to information, the network has undergone tremendous development, the connection patterns of network attacks have become endless, and the number of attack network methods is increasing year by year. Based on the Markov model, the author studies the problem of network security authentication and aims to determine the truth of network security. In this way, network security managers can identify current network security issues, and we can gain a better understanding of the current network situation so that we can update our current security network promptly. The development process proposed by the author is simulated and the obtained results are all compared and evaluated. The experimental results show that the hidden Markov model algorithm is improved, the network security situation is better recognized, and the accuracy of the n network security problem is better known. At the same time, it is known to predict network security problems in the future according to the application model of predicting network security problems. Tests have shown that the development process is feasible and effective. © 2023 Xinmiao Zhang, published by Sciendo.

关键词： network security

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CCSv6: A Detection Model for DNS-over-HTTPS Tunnel Using Attention Mechanism over IPv6 28

CCSv6: A Detection Model for DNS-over-HTTPS Tunnel Using Att...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Jiao, Liang Zhu, Yujia Fu, Xingyu Zhou, Yi Qin, Fenglin Liu, Qingyun School of Cyber Security University of Chinese Academy of Sciences China Natl. Comp. Network Emergency Response Technical Team/Coordination Center of China Shandong Branch China Institute of Information Engineering Chinese Academy of Sciences China Shandong University Informatization Office China

ISBN: (纸本)9798350300482

In this paper, we first show DNS-over-HTTPS (DoH) tunneling detection methods verified to be effective over IPv4 can be applied to IPv6, and then propose a new model called CCSv6, using attention-based convolution neural network to build classifiers with flow-based features to detect DoH tunneling over IPv6, achieve 99.99% accuracy on the IPv6 dataset. In addition, we discuss the influence of various factors such as locations or DoH resolvers on the detection results in detail over IPv6. All the more important, our model shows better transfer learning ability, which can achieve the F1-score of 96% when trained on the IPv6 dataset and tested on the IPv4 dataset. © 2023 IEEE.

关键词： Classification (of information)

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Learner Modeling Framework Based on Learning Analytics 2

Learner Modeling Framework Based on Learning Analytics

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2nd IEEE International Conference on Data Science and Computer Application, ICDSCA 2022

作者： Sun, Hongtao Zheng, Qinhua Zhang, Hengguo Pan, Huiyao Administrative Data Management Center Beijing Normal University Beijing China Research Center of Distance Education Beijing Normal University Beijing China China University of Political Science and Law Network Security and Informatization Office Beijing China School of Government Beijing Normal University Beijing China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

Learner modeling is essential to adaptive learning, which is the trend of online learning. Learning analytics is an effective way of learner modeling. This paper suggests a learner modeling framework from the perspective of learning analytics, including the logic layer, data layer, analysis layer, and application layer and provides methodological references for the practice of learner modeling. Empirical research is carried out to verify the validity of the learner modeling framework. The forming of a learner model with the proposed framework is introduced in detail. Finally, the article highlights the critical issues of learner modeling and practical applications. Both the educational theory and platform function are the basis of learner modeling. High-quality data guarantees a high-quality learner model. Moreover, the learner features used for learner modeling should be comprehensive, operable, and reusable. © 2022 IEEE.

关键词： Computer software reusability

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Deep learning for image segmentation: Ultrasound image segmentation of thyroid nodules based on U_Net 22

Deep learning for image segmentation: Ultrasound image segme...

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Proceedings of the 6th International Conference on Advances in Image Processing

作者： Xueting Zhou Yan Chen Shoushan Liu College of Electronic Information Engineering Shandong University of Science and Technology China Office of Network Security and Informatization Shandong University of Science and Technology China

ISBN: (纸本)9781450397155

The purpose of this article is to investigate the value of deep learning algorithms in the application of ultrasound images of thyroid nodules. Using a dataset of 7288 ultrasound images of thyroid nodules provided by the MICCAI 2020 Challenge, based on the U_Net framework, incorporating a multiscale input mechanism and improving loss optimization function, through continuous training to find the optimal model, so that the computer can autonomously segment the thyroid nodules. The segmentation accuracy reaches 0.955, and the network has good segmentation performance.

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A wearable, real-time sEMG gesture classifier based on E-tattoo and CDF-CNN for prosthetic control

A wearable, real-time sEMG gesture classifier based on E-tat...

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IEEE Information Technology, networking, Electronic and Automation Control Conference

作者： Chuanbin Xu Xiangwen Qu Hongrui Liang Da Chen College of Electronics and Information Engineering Shandong University of Science and Technology Qingdao China Office of Network Security and Informatization Shandong University of Science and Technology Qingdao China

Bionic prosthetic hands are essential for the disabled to deal with most affairs of life independently. The surface electromyography(sEMG) is considered as a developing vigorous solution to realize the artificial limb system. However, most of the present devices are irritating to the skin, high cost and power energy, complexity, unnatural, which limits their promotion. This paper proposed a real-time bionic mechanical arm control system, based on the large-area flexible electronic tattoo(E-tattoo) and combining differential feature convolutional neural network (CDF-CNN). Similar to the tattoo attached on the skin, the electrode allows complying with the skin deformation comfortably and accommodation the local strains, providing long-term and robust monitoring of sEMG signals. Benefited from the convenient and low-cost fabrication and transferring to the skin surface, the large-area E-tattoo electrodes were applied to alleviate the requirement of controlling the accurate position of traditional Ag/AgCl electrodes. Moreover, a "sEMG feature map" is proposed by combining differential feature (CDF) to extract deep abstract features to improve the recognition effect, achieving 97.63% on average when using only two channels to classify 8 gestures. The proposed system is efficient, comfortable, natural and low-cost, which will help to facilitate the development and application of sEMG prosthesis.

关键词： Electrodes Automation Gesture recognition Feature extraction Control systems Skin Real-time systems

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CCSv6: A Detection Model for DNS-over-HTTPS Tunnel Using Attention Mechanism over IPv6

CCSv6: A Detection Model for DNS-over-HTTPS Tunnel Using Att...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Liang Jiao Yujia Zhu Xingyu Fu Yi Zhou Fenglin Qin Qingyun Liu Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences National Computer Network Emergency Response Technical Team/Coordination Center of China Shandong Branch Informatization Office Shandong University

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