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Implementation of Knowledge Collaboration and Consistency in Distributed Database Under CORBA and Its Application in Virtual Organizations

Implementation of Knowledge Collaboration and Consistency in...

引用

2023 IEEE international conference on Paradigm Shift in Information Technologies with Innovative applications in Global Scenario, ICPSITIAGS 2023

作者： Pei, Qiugen Chen, Feng Xu, Huan Feng, Xinyao Power Grid Co. LTD Enterprise Architecture and Digital Department of Guangdong Guangdong Guangzhou510000 China China Southern Power Grid Co. LTD. Digital Department Guangdong Guangzhou510000 China Guangdong Power Grid Co. LTD. Information Center Guangdong Guangzhou510000 China

ISBN: (纸本)9798350329773

In the era of cloud computing and big data, with the increasingly fierce competition between virtual organizations, achieving knowledge collaboration and consistency has become an important issue facing enterprises. This article discusses how to achieve this goal by studying the dynamic shared networking technology of CORBA (common object request broker architecture). First, the characteristics of the technology are analyzed, corresponding rules are constructed, and the system is verified to have the characteristics of interactivity and stability. A new method is proposed to solve the above problems, and the algorithm is improved. The test results show that the response time of the database to process data return and send data at different number of client points is within 9 seconds, which can save time and improve system efficiency. At the same time, the fault tolerance rate is as low as 79% and as high as 94%. © 2023 IEEE.

关键词： Fault tolerance

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Fedgrid: Federated Model Aggregation via grid Shifting 16

FedGrid: Federated Model Aggregation via Grid Shifting

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16th IEEE/ACM international conference on Utility and Cloud computing (UCC) / 10th IEEE/ACM international conference on Big Data computing, applications and Technologies (BDCAT)

作者： Kraychev, Boris Kiyamousavi, Ensiye Koychev, Ivan GATE Inst Sofia Bulgaria Sofia Univ St Kliment Ohridski Sofia Bulgaria

ISBN: (纸本)9798400702341

Federated Learning is a machine learning technique where independent devices (clients) cooperatively train a machine learning model by working on decentralized training data. A fundamental challenge in federated learning is the client model aggregation. The goal is to combine and preserve the knowledge that each client has acquired during its local training phase and generate an aggregated model with superior performance than any of the clients. Most current state-of-the-art algorithms for model aggregation still rely on simple weight parameter averaging. Though effective, these methods do not consider the internal structure of a deep neural network. In addition, the element-wise aggregation of the model parameters may create a conflict and loss of knowledge if a particular weight parameter serves different classification tasks over other learning clients. To decrease the probability of such conflicts, we propose Fedgrid, a method that involves rotated (shifted) weight parameter grids during the clients' local training phase, which stimulates the models to build non-overlapping neural network structures. Fedgrid can be applied to any existing algorithm that performs parameter averaging to improve the aggregation of deep NN models. To estimate the method's performance, we generate an ensemble of datasets with preset heterogeneity, measured via entropy balance metric, ranging from 0.1 to 1.0. Our results indicate that Fedgrid outperforms the state-of-the-art methods when applied to deep neural network models. Moreover, the proposed method is simple and does not add computational complexity to the learning process. We believe that our effective method for grid application provides a new field for future research and improvement of the federation of deep neural network models.

关键词： Federated Learning Neural Networks Model Aggregation Strategy FL Algorithm decentralized Learning Learning Rate grid

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Design of Power grid Full-service Data Monitoring Platform Based on Cloud computing 2

Design of Power Grid Full-service Data Monitoring Platform B...

引用

2nd international conference on Algorithms, Microchips, and Network applications, AMNA 2023

作者： Li, Wei Zhang, Li Duan, Junxiang Cao, Lu Guo, Tao Customer Service Center of State Grid Tianjin300306 China Beijing China-Power Information Technology Co. Ltd. Beijing100031 China

ISBN: (纸本)9781510664791

The use of power monitoring and management system in power enterprises has significantly improved the convenience, fault tolerance, and related economic income of power grid in power supply. At present, there are still many problems in the management system, such as the lack of unified planning. Many functions related to power monitoring and management often need a lot of software to be repeatedly installed in the system to complete the functions required by customers. Meanwhile, this software often has duplicate functions. Power data can not be shared, and the upgrade of the system will encounter many obstacles. To meet the data communication requirements of component-based power monitoring and management system, this paper applies the characteristics of multi-point distribution and real-time and efficient transmission of data based on model-based information transmission. It also applies component-based data monitoring technology to complete the design. For the power data content of monitoring and calculation, a reusable framework is applied to facilitate user expansion. Users can complete the interface layout by dragging controls on the control interface of the power monitoring and management system, and then realize data customization and saving by binding the database. Finally, the monitoring of the power grid data platforms under cloud service is realized, which is conducive to the effective and timely processing of blowout power grid data. It is of great significance to the safe and stable operation of the power system. © 2023 SPIE.

关键词： Distributed database systems

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Edge Devices and Blockchain Integration in IoT System: A Novel Design Approach

Edge Devices and Blockchain Integration in IoT System: A Nov...

引用

2023 international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bisht, Tanisha Dinesh, Drishya Usha, G. Gautam, Kumar Srm Institute of Science and Technology Department of Computing Technologies Chennai India Founder of Quantum Research Centre of Excellence New Delhi India

ISBN: (纸本)9781665474511

Blockchain, the technology that powers cryptocurrencies, has recently attracted a lot of interest. Numerous applications, including the smart grid and cryptocurrency, have adopted it. Blockchain is unable to handle services that require frequent transactions due to a substantial scalability constraint. Edge computing allows cloud-based resources to be dispersed at the edge of the network, however it presently has difficulties with its decentralized administration and privacy. By providing reliable control and access over the networking, storage and computation dispersed at the edges, edge computing and blockchain integrated into one system could enable computing validity of the blocks and provide huge of number of network servers and ample storage space for data in a secure method. In this paper an insight into blockchain technology is given followed by an introduction about edge computing. Next, the existing architecture about the integrating blockchain and edge computing is discussed and explore the challenges caused by the fusion of blockchain technology with edge computing systems from the angles of scalability and optimizing consensus mechanism. a novel SC-IBEC architecture for integrating blockchain technology with edge computing architecture is presented. Then possible remedies to overcome the existing challenges and how it can be implemented at the different layers of the SC-IBEC architecture. Finally, we list the current difficulties brought on by the combination of blockchain technology with edge computing architecture, as well as any potential future solutions. © 2023 IEEE.

关键词： Blockchain

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Trustworthiness Evaluation System of UEIOT Devices Based on Deep Learning

Trustworthiness Evaluation System of UEIOT Devices Based on ...

引用

2023 international conference on Networks, Communications and Intelligent computing, NCIC 2023

作者： Chen, Cen Wang, Ming Li, Nuannuan Lv, Zhuo Li, Mingyan Chang, Hao State Grid Henan Electric Power Research Institute Zhengzhou China State Grid Corporation of China Beijing China

ISBN: (纸本)9798350371949

With the rising popularity of smart devices and the growth of IoT applications, AI technology is thriving in edge computing environments. These environments provide ample opportunities for applying AI due to the abundance of realtime sensing data and diverse application scenarios. Moreover, AI technology facilitates the extraction of value from edge data, thus driving the development of emerging information industries, particularly in areas like smart grids. However, the security of IoT devices in smart grids faces significant challenges, making it difficult to swiftly and effectively assess the trustworthiness of such devices. This poses implications for ensuring information security in IoT and edge computing. This paper introduces existing models for evaluating IoT devices, identifies shortcomings in current systems, and proposes a trust evaluation method for UEIOT devices based on deep learning. The experimental results demonstrate the following contributions of this paper: (1) Transformation of CNN's traditional map feature extraction into the extraction of IoT device connection data for effectively evaluating IoT devices over a historical period. (2) Application of the DQN model analogy to evaluate ubiquitous power IoT devices and simulate interactions between intelligent body devices and external IoT devices. © 2023 IEEE.

关键词： Edge computing

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Smart grid big data processing technology and cloud computing application status quo and challenges 2

Smart grid big data processing technology and cloud computin...

引用

2nd IEEE international conference on Power, Electronics and Computer applications, ICPECA 2022

作者： Yuanyuan, Ji Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665442763

With the rapid development of China's economy, people's demand for electricity is increasing, so that the power grid will generate a large amount of data information in the actual operation, and the smart grid big data processing technology is to store and use the data information generated by the operation of the power *** ultimate purpose of applying big data processing technology is to make efficient use of the data information generated by the power grid, improve the rate of data access and processing, so as to provide necessary support and help for the normal operation of the power grid, and ensure that the national power grid can provide more stable power for people and *** paper focuses on the smart grid big data processing technology and opportunities and challenges. © 2022 IEEE.

关键词： Smart power grids

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Hybrid CNN-LSTM Model for Multi-industry Electricity Demand Prediction 19th

Hybrid CNN-LSTM Model for Multi-industry Electricity Demand ...

引用

19th international conference on Advanced Intelligent computing Technology and applications (ICIC)

作者： Zhang, Haitao Dai, Yuxing Yin, Qing He, Xin Ju, Jian Zheng, Haotian Shen, Fengling Guo, Wenjuan Li, Jinhu Zhang, Zhihong Duan, Yong Metrol Ctr State Grid Shaanxi Mkt Serv Ctr Xian Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China State Grid Infotelecom Great Power Sci & Technol Xiamen Peoples R China

ISBN: (纸本)9789819947607;9789819947614

Accurately predicting electricity demand is crucial for optimizing power resource allocation, improving the safety and economic performance of power grid operations, and providing significant economic and social benefits. To address this challenge, we propose a hybrid model that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for predicting electricity demand across different industries in urban areas. The proposed model leverages the LSTM component to capture the temporal patterns of the time series data and the CNN component to extract spatial features of electricity demand across different areas. We evaluate our model on a diverse dataset of electricity demand from multiple city areas and industries. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods, resulting in significant improvements in the accuracy of electricity demand prediction. Overall, our proposed hybrid model provides a valuable framework for accurately predicting electricity demand and has practical implications for power grid operations and management in urban areas.

关键词： Convolutional Neural Network Time Series Network Electricity Demand Prediction

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Improve Knowledge Graph Completion for Diagnosing Defects in Main Electrical Equipment 19th

Improve Knowledge Graph Completion for Diagnosing Defects in...

引用

19th international conference on Advanced Intelligent computing Technology and applications (ICIC)

作者： Huang, Jianye Qian, Jian Chen, Yanyu Lin, Rui Weng, Yuyou Lin, Guoqing Zhang, Zhihong State Grid Fujian Elect Power Res Inst Fuzhou 350000 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Fuzhou Power Supply Co State Grid Fujian Elect Po Fuzhou 350000 Peoples R China

ISBN: (纸本)9789819947607;9789819947614

Defects in main electrical equipment can potentially cause power grid collapses, leading to catastrophic risks and immense pressure on dispatch operations. We adopt a Knowledge Graph Completion (KGC) approach to diagnose defects in main electrical equipment. The conventional approach for knowledge graph completion involves randomly initializing the feature representation of nodes and edges. In the context of main electrical equipment, we propose an innovative method based on ChatGPT to generate a corpus for fine-tuning the pre-trained model. We then employ the pre-trained model as a feature extractor for nodes and edges, enhancing the relationships between nodes and improving the initial embedding quality of both nodes and edges. Our pre-trained model fine-tuning process can be efficiently executed on a CPU with less than 10% of the parameters required for fine-tuning the entire pre-trained model. Our experimental results show that our model effectively improves the performance of KGC in diagnosing defects in main electrical equipment.

关键词： Knowledge graph completion Defect diagnosis Pre-training model

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A Distributed Asset Trading Mechanism Based on Automated Negotiation

A Distributed Asset Trading Mechanism Based on Automated Neg...

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2023 IEEE international conference on Metaverse computing, Networking and applications, MetaCom 2023

作者： Zeng, Jiahao Zang, Xiuhuan Liu, Chang Chen, Zhiyi Guo, Shaoyong Qi, Feng Pan, Jianhong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China State Grid Economic and Technological Research Institute Co. Ltd Engineering Data Center Beijing China State Grid Jilin Electric Power Co. Ltd Digital Department Changchun China

ISBN: (纸本)9798350333336

Asset digitization is an important part of digital society. However, there are many problems in existing centralized asset trading platforms, for example, the platform has the dominant power in the transaction process and holds a large amount of user data, which is detrimental to users' interests. To solve these problems, we propose a distributed asset trading mechanism based on automated negotiation. Firstly, we propose a distributed asset trading framework based on the consortium blockchain, which provides trust guarantees for trading through the access control of the consortium blockchain, represents the ownership of assets through non-fungible tokens (NFT), and reduces the blockchain burden by moving the negotiation process off the blockchain through the state channel technology. Then, to reduce the negotiation cost and improve the negotiation results, we propose a bilateral automated multi-issue negotiation model for asset trading. The experimental results show that the negotiation model performs well in terms of success rate and negotiation time, and can achieve win-win agreements. © 2023 IEEE.

关键词： Multi agent systems

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Containerization of the Security Application on Electric Power IoT Terminal 4

Containerization of the Security Application on Electric Pow...

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4th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Wang, Tengyan Shao, Zhipeng Li, Nige Xu, Zheng State Grid Smart Grid Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350337181

The development of Internet of Things (IoT) technology brings convenience to the development of the electric power business but also brings security risks. Moreover, owing to the massive heterogeneity of IoT terminals, traditional security protection measures are restricted in terms of deployment, adaptation, operation, maintenance, and other aspects. Docker containers take up fewer resources and are easy to transplant;therefore, they are suitable for deploying secure ap-plications in the IoT computing environment with limited resources and power. This study fo-cuses on the container virtualization technology of the security applications on electric power IoT terminals. We propose a general process for application containerization to standardize the work done in each stage of containerization. The union file system is used to build a multilayer container architecture to reduce resource usage and improve deployment speed. Thus, the secu-rity applications can be run in containers to realize their real-time deployment and real-time op-eration in the heterogeneous intelligent electric power IoT terminals to achieve the security ca-pabilities of on-demand orchestration and timely response of electric power IoT terminals. © 2023 IEEE.

关键词： Internet of things

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