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Research on Dynamic Analysis Technology of Economic Evaluation of power Grid Digital Technology 3

Research on Dynamic Analysis Technology of Economic Evaluati...

引用

3rd International Conference on Applied Machine Learning, ICAML 2021

作者： Wang, Gang Dong, Aidi Pan, Jianhong Global Energy Interconnection Research Institute Co. Ltd Jiangsu Nanjing210003 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China State Grid Jilin Electric Power Co. Ltd Changchun130021 China

ISBN: (纸本)9781665421256

Digitalization plays a more and more important role in the development of power grid. Traditional technical and economic evaluation methods are difficult to meet the needs of Digital Construction Evaluation under the new situation. Based on Zachman and other enterprise architecture theories, aiming at the business demand scenarios such as basic resource operation, power data value-added services, digital platform ecological construction, this paper studies the optimization technology of power grid digital architecture meta model, including business architecture, data architecture, application architecture, technology architecture meta model optimization, etc. Research on dynamic analysis technology of intelligent quantitative control, including technology and economic analysis and evaluation technology of digital economic benefits of power grid enterprises for multi application scenarios, digital technology and economic evaluation model based on multi-dimensional index sensitivity analysis, technology and economic analysis model of multi-source correlation between business demand scenarios and digital architecture, and dynamic analysis model technology of intelligent quantitative control. This paper innovatively studies the relevance of business demand scenarios, technical and economic analysis rules and enterprise digital architecture, and constructs a multi-source digital benefit analysis model based on the integration of variables and factors. According to the different business scenarios such as basic resource operation, power data value-added services, and digital platform ecological construction, this paper analyzes the relationship between business demand scenarios and digital architecture, Dynamic adjustment of technical and economic evaluation index and weight can more accurately complete the digital economic benefit evaluation of power grid enterprises. © 2021 IEEE

关键词： Sensitivity analysis

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An Algorithm for Detecting Surface Defects in Industrial Strip Steel based on Receptive Field and Feature information Supplementation

An Algorithm for Detecting Surface Defects in Industrial Str...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Qingyu Xu Jiguo Yu Anming Dong Zihao Shang 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 School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute 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 Shandong Data Intelligence Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the context of Industry 4.0 and the rise of intelligent manufacturing, the quality of industrial products is becoming more and more important. Strip steel surface defect detection, as a key link in industrial production, is crucial to ensure the quality of industrial products. However, due to the irregularity of the defect scale and the inconspicuous defect features in the surface defect image of strip steel, it is difficult for the existing detection algorithms to realize the effective detection of defects. In order to better extract the features of defects and improve the network’s ability to detect defects, this paper proposes an algorithm for detecting surface defects on industrial strip steel based on receptive field and feature information supplementation. First, we design a receptive field (RF) module to replace the residual structure in the C3 module, which we name C3RF. This module can effectively increase the network’s receptive field, allowing the network to fully capture irregular defect features without increasing the cost. Second, for the characteristics that defective features are not obvious and tend to lose detail information as the network deepens, an extra information supplemental branching feature fusion pyramid (EFPN) is proposed on the basis of the original PAFPN architecture to compensate for the detail information that is lost by the fragile features in deeper layers. Finally, convolutional block attention module (CBAM) is introduced to replace the spatial pooling pyramid (SPPF) in the baseline network, which enhances the contrast between defects and backgrounds, and improves the classification and localization ability of the network. Our network achieves an accuracy of 82.2% on the publicly available strip steel defect detection dataset, which is a 4.0% improvement over the baseline. The results show that the network constructed in this paper can realize effective defect detection.

关键词： Radio frequency Location awareness Strips Production Feature extraction Real-time systems Hardware

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A New Method to Enhance Container with vTPM

A New Method to Enhance Container with vTPM

引用

International Conference on Computer and Communications (ICCC)

作者： Zhang Xiao Jian Zhou Peng Wang Qi Gao Peng Wang Yi Liang State Grid Smart Grid Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Nanjing China State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Hangzhou China

This paper proposes a new container security enhancement system and method for containers running on 5G edge computing nodes. The system consists of vTPM module, physical TPM, container manager, vTPM manager, MEC controller and physical host. vTPM module is responsible for not only container image integrity measurement and starting up the container with trust, but also the trustworthiness measurement of container runtime file system and process. Physical TPM undertakes data encryption and password protection. Container manager performs trustworthiness measurement and verification of container. The MEC controller stores the measurement reference value for the physical TPM and issues keys and certificates for the physical TPM. The container enhancing method based on this system establishes a complete trust chain from the host to the container manager, and finally to containers. This ensures the trusted startup of the container, preventing the container itself and its runtime environment from being maliciously tampered with. It also dynamically measures the file system of the container in runtime to ensure the trustworthiness of the container. The experimental results indicate that this method is able to meet the requirements of container security enhancement for power 5G edge computing nodes.

关键词： Runtime environment 5G mobile communication File systems Process control Passwords Containers Safety

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Multi-keyword Cipher-text Retrieval Method for Smart Grid Edge computing 3

Multi-keyword Cipher-text Retrieval Method for Smart Grid Ed...

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2020 3rd International Symposium on power Electronics and Control Engineering, ISPECE 2020

作者： Zhu, Jing Wu, Tao Li, Jintao Liu, Yanbin Jiang, Qixin School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science Chongqing University of Posts and Telecommunications Chongqing China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China Electric Power Institute CSG Guangzhou China

With the continuous deepening of China's power grid intelligent upgrading and the rise of edge computing technology, power grid terminals can carry out data storage and intelligent calculation at the edge of the power grid. However, the generated data by users may cause the leakage of user privacy information. The power grid edge system can retrieve the user's electricity data on demand, so as to improve the level of privacy protection and reduce the demand of transmission and computing resources through the localized storage of data. Therefore, how to carry out privacy protection and on-demand retrieval is a key problem to realize the intelligent power grid edge. In this paper, a multi-keyword cipher-text retrieval scheme (MCR- SGEC) for smart grid edge computing is proposed, which can achieve accurate matching of index and trap door, and has low spatial complexity. At the same time, on this basis, a multi-keyword fuzzy cipher-text retrieval method for smart grid edge computing (MFSE-SGEC) is proposed, which allows users to have spelling errors, which makes the user experience better. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Sequence-to-Sequence Knowledge Graph Completion Based On Gated Attention Unit

Sequence-to-Sequence Knowledge Graph Completion Based On Gat...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Fengge Yi Xiumei Wei Xiaojing Liu Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 State Key Laboratory of High-end Server & Storage Technology Jinan China

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5’s proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment.

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A JPEG compression-resistant data watermark embedding and detection algorithm 21

A JPEG compression-resistant data watermark embedding and de...

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5th International Conference on Electronic information Technology and Computer Engineering, EITCE 2021

作者： Yu, Pengfei Huang, Xiuli Hu, Bo Zhou, Xiaoming State Grid Key Laboratory of Information and Network Security Global Energy Interconnection Research Institute Co. Ltd. Nanjing Jiangsu Province China State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang Liaoning Province China

ISBN: (纸本)9781450384322

When the image is processed by JPEG compression, the data watermark originally hidden in the image will not be extracted effectively according to resampling and noise addition. This would greatly limit the application of image data watermark in real scenes. In this paper, a novel JPEG compression-resistant data watermark embedding and detection algorithm is proposed for the robustness of data watermark embedding and extraction under lossy channels. The algorithm uses the compression invariance of DCT coefficient symbols to select candidate coefficients in the carrier image that can resist JPEG compression, followed by assigning distortion cost to each candidate coefficient, and then embedding the watermark information into the carrier image using error correction codes and Syndrome-Trellis Code encoding. The tests show that the proposed watermark embedding method improves the detection resistance performance by more than 7.7% when the relative embedding rate is 0.1, and the correct extraction rate of data watermark can reach more than 99% under JPEG compression with different quality factors. © 2021 ACM.

关键词： Image compression

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A Parallelizable Counterfactual Generation Method Based on Gradient Optimization

A Parallelizable Counterfactual Generation Method Based on G...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Haorun Ding Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models.

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu 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 Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory Data mining Cybernetics Convergence

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Sensitive Data Recognition and Filtering Model of Webpage Content Based on Decision Tree Algorithm 2nd

Sensitive Data Recognition and Filtering Model of Webpage Co...

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2nd International Conference on Big Data and security, ICBDS 2020

作者： Ye, Sheng Cheng, Yong Yang, Yonggang Guo, Qian State Grid ZheJiang Electric Power Company Ltd Hangzhou310000 China State Grid ShanXi Electric Power Company Xi’an710000 China Global Energy Interconnection Research Institute Co. Ltd Nanjing210000 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China

ISBN: (纸本)9789811631498

In recent years, privacy-protecting data mining has attracted widespread concern because it is necessary to provide protection for the privacy level of sensitive and confidential data from unauthorized attacks. The purpose of this study is to develop a privacy-protecting anonymity algorithm using decision tree classification. This paper focuses on k-anonymity technology, which can prevent identity leakage. K-anonymity technology adopts generalization and suppression methods to achieve data anonymity. Then, the privacy level and mining quality of anonymous data sets will be tested by using decision tree classification, and then compared with other data mining technologies (logistic regression and support vector machine). As is shown in the research, compared with other data mining technologies, privacy level and data quality provide better results. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Research and Implementation of User Behavior Simulation Technology Based on power Industry Cyber Range 2

Research and Implementation of User Behavior Simulation Tech...

引用

2nd IEEE International Conference on information Technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Yang, Huiting Chen, Tao Bai, Yunxiao Li, Feng Li, Mingxuan Yang, Ruxia State Grid Xinjiang Electric Power Research Institute Urumqi China State Grid Xinjiang Electric Power Co. Ltd Urumqi China State Grid Key Laboratory of Information Network Security Nanjing China Global Energy Interconnection Research Institute Co. Ltd. Jiangsu Nanjing China

ISBN: (纸本)9781665428767

The current cyber range of the electric power industry has background traffic simulation problems, prospect behavior generation problems, and target simulation diversity problems in large-scale network environments. In response to these problems, this paper proposes a user behavior simulation technology suitable for power industry cyber range. We perform large-scale, high-fidelity network user behavior simulation by combining real traffic playback, inject user behavior traffic in the real network into the virtual target network, then model the user behavior in multiple scenarios in the power network space and generate it based on the method of sequence prediction, and finally realize the diversity simulation of user behavior. © 2021 IEEE.

关键词： Behavioral research

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