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共 17264 条记录，以下是681-690 订阅

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Privacy Data Protection Scheme of Industrial Field equipment Based on Fully Homomorphic 37

Privacy Data Protection Scheme of Industrial Field Equipment...

引用

37th international conference on Information Networking (ICOIN)

作者： Xiao, Feng Wang, Jin Wei, Min Liu, Chang Le, Vanqui Xu, Jiangpei Chongqing Univ Posts & Telecommun China Korea Belt & Rd Joint Lab Ind Internet Thin Chongqing Peoples R China State Grid Hubei Elect Power Res Inst Wuhan Peoples R China

ISBN: (纸本)9781665462686

With the increasing value of industrial data, it has become an important demand to effectively protect the security of industrial network data. In the edge cloud collaborative environment of industrial Internet, how to transmit and process private data to avoid exposure and tampering caused by attacks is an urgent problem to be solved. The privacy data security of industrial field equipment is the basis of creating a secure environment for industrial edge computing. Therefore, how to ensure the security of private data of industrial field equipment, resist attacks of attackers and avoid privacy data leakage has become a research hotspot. This paper designs a privacy data protection method for industrial field equipment based on fully homomorphic encryption, and builds a test simulation platform to verify the proposed method. The results show that this method can effectively protect the privacy data of field equipment according to the sensitivity of the industrial field equipment data to the edge gateway and industrial cloud platform.

关键词： industrial field equipment privacy data fully homomorphic encryption edge gateway industrial cloud platform

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Implement of edge computing for Cyber-Physical System in the Smart grids Quasi-Real Time Control Context 12

Implement of Edge Computing for Cyber-Physical System in the...

引用

12th ieee international conference on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Dombrovskyi, Mykhailo Dombrovskyi, Zbyshek Pashchuk, Ivan West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9798350358056

An increase in the number of power consumers leads to scale up a power supply grids, and the introduction of Smart grid (SG) for connecting components and subsystems of distributed generation, increases the complexity of the interaction configuration and, accordingly, grids control. At the same time, with peak loads cases, when there is a lack of power, the probability of big-scale fluctuations increases, which turns the energy system from deterministic to stochastic. Unpredictable manifestations of big-scale fluctuations spread in the form of a cascade of failures through the grid, which lead to the loss of reliability of the entire power grid. To simplify the controllability of complex big-scale power grids and prevent cascading failures in the supply of electric power, they are divided into a big number of microgrids. To increase observability and controllability, big-scale SGs are implementing innovative intelligent products and cyber-physical systems (CPS) that connect the virtual world with the world of things. CPSs allow to carry out instant monitoring and display of the states of power system components in large geographical areas, however, due to the concentration of control in the center, it does not ensure control efficiency in critical peak load times. Thus, it is important to increase the energy efficiency of smart grids, in particular by reducing power dispersion and increasing the flexibility, safety and reliability of energy supply. Such requirements lead to rethinking the structure of both the physical and cybernetic subsystems of the CPS, with which users and suppliers of energy resources interact. To meet these requirements, it is proposed to develop the structure of the CPS and improve computation based on the processing of 'instant data' with the use of edge computations in quasi-real time, in order to ensure an increase in the efficiency of a big-scale complex power networks control. © 2023 ieee.

关键词： big-scale power grid cyber-physical systems edge computing instant data quasi-real time control smart grid

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Comparative Analysis of Deep Learning Methods for Lesion Segmentation in Gastrointestinal endoscopic Images 1

Comparative Analysis of Deep Learning Methods for Lesion Seg...

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Sai Chandana Reddy, K. Dheeraj Reddy, K. Sree Sai Praneeth Goud, L.e. Singh, Tripty Afnaan, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Gastrointestinal (GI) disorders present diagnostic obstacles, necessitating accurate imaging procedures. Conventional techniques for evaluating endoscopic images are mostly laborious and arbitrary. An innovative method is proposed to improve GI endoscopic image evaluation using three cuttingedge deep learning models, including Seg Net, U-Net, and Deep Lab, for lesion segmentation. exploiting the recently published Kvasir-SeG dataset, which comprises segmentation masks and accompanied gastrointestinal polyp images, this work attempts to further the field of polyp analysis and detection research. The study aims to enhance diagnostic precision and optimize the analytical process by utilizing deep learning approaches and datasets that are annotated. It will eventually contribute for enhanced patient outcomes as well as healthcare productivity. © 2024 ieee.

关键词： endoscopy

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Deep Reinforcement Learning Optimization Algorithm Designed for IRS-Assisted edge computing 6

Deep Reinforcement Learning Optimization Algorithm Designed ...

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6th ieee international conference on electronic Information and Communication Technology, ICeICT 2023

作者： Liu, Kaixu Lin, Fei Zhao, Yingxue Zhang, Junrui Jinan China

ISBN: (纸本)9798350399059

To reduce the computing delay of the terminals and improve the poor wireless channel environment, in this paper, a computing resource allocation algorithm for IRS-assisted edge computing is proposed. By introducing the intelligent reflecting surface (IRS) technique to reduce the edge computing latency, the computing offloading variables are optimized through an Alternating optimization approach, and a partial offloading strategy for MeC offloading is adopted. Deep reinforcement learning (DRL) is applied for coarse phase control of the IRS, and fine phase control of the IRS is achieved through fine phase optimization. In simulations, the proposed scheme is demonstrated to be effective and low latency. © 2023 ieee.

关键词： Reinforcement learning

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Analysis of the role of computer big data and cloud computing in information security

Analysis of the role of computer big data and cloud computin...

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2023 international conference on Networking, Informatics and computing, ICNeTIC 2023

作者： Wei, Qian Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350313314

With the rapid development of social economy, science and technology, e-commerce and network, computer extends more and more content, in the era of big data, cloud computing has become an emerging field, multi-tenant, distributed computing, distributed storage and virtualization concepts onto the stage. This paper mainly discusses the factors affecting information security in the era of big data, and analyzes the role of big data and cloud computing in information security. © 2023 ieee.

关键词： economic and social effects

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New energy Distribution Network Scheduling Based on Optimized PSO 16

New Energy Distribution Network Scheduling Based on Optimize...

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16th th international conference on Intelligent computing Technology and Automation, ICICTA 2023

作者： Zhang, Wei Song, Xueqing He, Chaolin Xie, Hu Xie, Xinglang Yang, Zhanjie Digital Grid Research Institute China Southern Power Grid Guangzhou510600 China Shenzhen Digital Grid Research Institute Csg Shenzhen518055 China

ISBN: (纸本)9781643684864

Considering the fluctuation of predicted output power of new energy units such as wind power, a strategy to strengthen the dispatching optimization of new energy distribution network is proposed. Taking the constraints such as operation cost, voltage deviation and network loss of distribution network as a reference, we provide the description of wind power active power control. Then, combined with stochastic chance constrained programming, an improved particle swarm optimization algorithm is proposed to establish the optimal dispatching model of 'source-network-load' distribution network. Finally, the effectiveness of the models and methods proposed in this chapter is verified by a simulation example of ieee-33 node distribution system. The results show that the comprehensive optimization of the two is better than the reconstruction or optimization separately, which can facilitate the dispatcher to select the best optimal scheduling strategy. © 2024 The Authors.

关键词： electric power distribution

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Towards a Greener Future: Harnessing Green computing to Reduce ICT Carbon emissions 7

Towards a Greener Future: Harnessing Green Computing to Redu...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Patel, Unnati A. Patel, Sanskruti Nanavati, Jay Faculty of Computer Science and Applications Smt. Chandaben Mohanbhai Patel Institute of Computer Applications CHARUSAT India

ISBN: (纸本)9798350372816

The escalating environmental impact of Information and Communication Technology (ICT) has raised concerns about its substantial carbon footprint. Green computing, a concept that focuses on minimizing the environmental impact of computing and IT infrastructure, has emerged as a promising approach to address this issue. This research paper investigates the potential of integrating green computing practices to curtail the environmental ramifications of ICT operations. Understanding the fundamental forces behind the adoption of green computing in business, industry, and government is crucial since ICTs are so critical to technical innovation, the economy, and environmental protection. By examining the principles and methodologies of green computing, this study aims to delineate its role in reducing energy consumption, minimizing e-waste, and fostering eco-friendly practices within the realm of information technology. examined are the major implementation challenges and the global adoption of green computing. Through a comprehensive review of existing literature and case studies, this research assesses the efficacy of green computing strategies in mitigating the carbon footprint of ICT infrastructures. Furthermore, it evaluates the economic viability and long-term sustainability implications for businesses and society at large. ICT would be a bigger enhancer of productivity and utility with a global carbon limitation, offering opportunity for the industry to be financially successful as a crucial component of a global net zero society. The paper concludes by proposing recommendations for the seamless integration of green computing into ICT ecosystems, emphasizing the pivotal role of stakeholders in fostering a more sustainable digital landscape while simultaneously reducing carbon emissions. © 2024 ieee.

关键词： Zero-carbon

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Proceedings of 2024 international conference on energy and electrical engineering, eee 2024

Proceedings of 2024 International Conference on Energy and E...

引用

2024 international conference on energy and electrical engineering, eee 2024

ISBN: (纸本)9798350341768

The proceedings contain 102 papers. The topics discussed include: application of machine learning algorithms for enhanced smart grid control and management;a cryo-CMOS wideband sub-1-dB NF low noise amplifier for quantum computing application;computational analysis on jump height of overhead transmission lines after uneven ice shedding;tower line condition assessment based on improved fuzzy integrated judgment method;open circuit voltage-state of charge testing for a lithium polymer battery cell;research on intelligent operation and maintenance technology based on power distribution edge gateway;electromagnetic analysis of generator rotor winding impedance when testing turn-to-turn faults;and research on fault diagnosis of marine diesel engine based on DA-DBN.

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Q-TetoFormer: A New Gene Regulatory Network Prediction Method Based on Quantum computing and Transformer

Q-TetoFormer: A New Gene Regulatory Network Prediction Metho...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Hanmo South China Normal University School of Computer Science Guangzhou China

ISBN: (纸本)9798350386226

Quantum computing has enabled precise simulation in gene regulatory network (GRN) prediction by capturing regulatory relationships at a microscopic level. However, current quantum approaches face significant limitations due to high noise levels and prohibitive training costs, making them challenging to implement effectively. To address these issues, we introduce Q-TetoFormer (Quantum-Tuned extraction and Transformation Orchestrator Former), a novel framework that merges quantum computing with Transformer architecture to enhance GRN prediction accuracy and efficiency.Q-TetoFormer begins by preprocessing gene data, feeding it through a neural network-embedded quantum circuit. The circuit's output then flows into TetoFormer, a Transformer-based module that leverages attention mechanisms to reduce quantum noise and enrich feature extraction. The output from TetoFormer subsequently optimizes the quantum circuit, creating a feedback loop that improves model performance iteratively. extensive experiments on five GRN prediction benchmarks demonstrate that Q-TetoFormer not only achieves superior prediction accuracy over state-of-the-art methods but also significantly reduces training overhead, enhancing its practical utility. © 2024 ieee.

关键词： Quantum computers

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Peer to Peer energy Transaction Market Prediction in Smart grids using Blockchain and LSTM

Peer to Peer Energy Transaction Market Prediction in Smart G...

引用

ieee international conference on Consumer electronics (ICCe)

作者： Chien, I. Karthikeyan, P. Hsiung, Pao-Ann Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Minxiong Taiwan

ISBN: (纸本)9781665491303

Predicting future demand for distributed energy sources is difficult because of the energy generations is unpredictability. We proposed an auto executable blockchain-based peer-to-peer energy transaction market with Long ShortTerm Memory (LSTM) neural network for energy trading and predictions. Our proposed energy transaction market and trading strategy are built via smart contracts inside the blockchain. The energy trading process in the market is auto executable and does not need a long waiting time as in the conventional bidding strategy. LSTM is integrated into the market for predicting future energy usage. experimental results show that the proposed blockchain-based peer-to-peer energy transaction market can earn more profit than the traditional method.

关键词： Blockchain Smart Contract,Long Short-Term Memory (LSTM) Smart grid

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