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共 8967 条记录，以下是41-50 订阅

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Data Volume-aware Computation Task Scheduling for Smart grid Data Analytic applications

Data Volume-aware Computation Task Scheduling for Smart Grid...

引用

IEEE international conference on Communications (IEEE ICC)

作者： Guo, Binquan Li, Hongyan Yan, Ye Zhang, Zhou Wang, Peng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Tianjin Artificial Intelligence Innovat Ctr TAIIC Tianjin Peoples R China

ISBN: (纸本)9781538674628

Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.

关键词： Smart grid applications data analytics task scheduling job completion time branch and cut disjunctive formulation

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Security Enhancement Method for MQTT Based on TEE

Security Enhancement Method for MQTT Based on TEE

引用

international conference on Networking and Network applications (NaNA)

作者： Zhang, Fangzhe Liu, Donglan Zhao, Fuhui Liu, Xin Chang, Yingxian Wang, Rui Zhang, Hao Sun, Lili Xu, Shanjie State Grid Shandong Elect Power Res Inst Jinan 250003 Shandong Peoples R China Shandong Smart Grid Technol Innovat Ctr Jinan 250003 Shandong Peoples R China State Grid Shandong Elect Power Co Jinan 250001 Shandong Peoples R China

ISBN: (纸本)9798350376784;9798350376777

MQTT is a lightweight topic-based publish-subscribe protocol widely used in edge computing and IoT environments, and has become an industry standard adopted in various real-world scenarios such as finance, smart cities and medical environments. However, The server-side of MQTT, known as the broker, process data in plaintext, exposing a large attack surface. This paper introduces a security enhancement system for MQTT based on Trusted Execution Environment to address the deficiencies in the existing security mechanisms of MQTT. The system leverages TEE to implement secure MQTT brokers, introduces a lightweight authentication and key agreement protocol, TAKA, for end-to-end security between clients and trusted brokers, and integrates CapBAC for access control authorization to further enhance the broker's security against malicious users. The experimental evaluation of the system prototype demonstrates that while ensuring a high level of communication security, the overhead introduced by TEE is within an acceptable range.

关键词： Authentication Protocol

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Lightweight Method for Adaptive Power grid Operation Safety Control Model Based on Knowledge Distillation 24

Lightweight Method for Adaptive Power Grid Operation Safety ...

引用

2024 international conference on Computer Vision and Deep Learning, CVDL 2024

作者： Wei, Wei Zhang, Yi Du, Zexu Chu, Zongbo Zhang, Guoliang Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798400718199

Safety control is one of the most important tasks for power grid operation. However, safety control and risk warning models with high accuracy always have multitudinous parameters and need scaled resources for calculation. So as to sort out this problem, we proposed a novel and adaptive model lightweight method based on knowledge distillation. In our method, the intermediate features of the teacher network is utilized to guide the learning of the student network and a regressor is introduced behind the selected middle layer of teacher network to realize dimension match. The prediction confidence of teacher network is mapped to the feature space for the regressor initialization. Besides, the prediction confidence is used to construct the distillation temperature to improve the adaptability of information transfer. Experiments on ImageNet, CIFAR 100 and grid-specific datasets demonstrate the lightweight model obtained by our method possesses powerful and adaptive feature extraction and representation ability. © 2024 ACM.

关键词： Safety engineering

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Sudoku Solver Algorithms: A Comparative Analysis 1

Sudoku Solver Algorithms: A Comparative Analysis

引用

1st international conference for Women in computing, InCoWoCo 2024

作者： Manyam, Yashaswini Sundaram, Srinidhi Sahay, Apurvan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The exploration of advanced algorithms for solving Sudoku puzzles reveals unique insights into their performance and effectiveness. The study analyzes and compares effectiveness in solving Sudoku puzzles with the classic backtracking method, an innovative approach based on ant colony optimization, and advanced constraint propagation algorithm. The project evaluates the performance of these algorithms about solution quality, computation time, and memory usage concerning the complexities of different puzzles: $9\times 9,\ 16\times 16$, and $25\times 25$ grid dimensions. Each individual of the different grid sizes tests time and space computations for each algorithm. These algorithms are putting up certain types of strengths and weaknesses in practical applications. The results, on average, produce that out of all, the ant colony optimization algorithm, which gives a good overall performance in terms of time and memory. © 2024 IEEE.

关键词： Optimization algorithms

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Automatic Optimization of Hyperparameters for Deep Convolutional Neural Networks: grid Search Enhanced with Coordinate Ascent 24

Automatic Optimization of Hyperparameters for Deep Convoluti...

引用

international conference on Machine Intelligence and Digital applications (MIDA)

作者： Song, Qingqing Xia, Shaoliang Wu, Zhen Belarusian State Univ Fac Appl Math & Comp Sci Minsk BELARUS Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China

ISBN: (纸本)9798400718144

Using the same depth convolutional neural network model will result in significantly different results due to different combinations of hyperparameters. By adjusting the configuration of hyperparameters, we can enhance the performance of the model. However, hyperparameter optimization typically requires a significant amount of computational resources and time. Therefore, improving the efficiency of hyperparameter optimization is crucial. In this study, we utilized the coordinate ascent method, which only offered initial candidate values for each hyperparameter. By changing only the hyperparameter that had the greatest effect on the model in each iteration, we gradually expanded the search grid until we achieved convergence in accuracy. This method enables us to effectively and automatically find hyperparameter combinations that can improve model accuracy. The experimental results show that using the MWD dataset, the model optimized through hyperparameters achieved an accuracy of 95.71% on the validation set, and this hyperparameter combination can be considered an approximate global optimal solution. In addition, the performance of the hyperparameter combination in its neighborhood is stable, which further proves the robustness of our hyperparameter optimization strategy. CCS CONCEPTS .computing methodologies .Machine learning .Learning settings

关键词： Deep convolutional neural network Hyperparameter optimization Coordinate ascent grid search Machine learning Optimization strategy

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Design and Practice of Super-computing grid Integration in Computility Network 12

Design and Practice of Super-computing Grid Integration in C...

引用

12th international conference on Information Systems and computing Technology

作者： Fan, Yamei Cui, Chao Pan, Zhiwei Zhang, Tingting Zhang, Hao Cheng, Kairong China Mobile Res Inst Dept Network & IT Technol Res Beijing Peoples R China Inspur Commun Informat Syst Co Ltd Computil Network Res Inst Jinan Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Jiangsu Co Ltd Wuxi Branch China Mobile Grp Wuxi Jiangsu Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Currently computility shows an explosive growth trend. Heterogeneous software and hardware architecture, super-computility distribution and performance difference bring significant challenges to super-computing grid integration. We propose a super-computing grid integration system to access and schedule heterogeneous super-computing, and provide high-quality service. Queue based super-computing virtualization and multi-level scheduling system are adopted respectively to address the problem of heterogeneity of super-computing centers and scheduling system. The system has unique scheduling policies, is theoretically compatible with majority of super-computing scheduling systems like Slurm and also has practicability and extensibility compared with traditional super-computing center. The system supports historical performance evaluation by agent to support applications deployment between architectures and it can optimize scheduling. Example case analysis proves the feasibility of our architecture. And we also find that the computational performance of heterogeneous computing resources needs to be comprehensively analyzed in terms of instruction sets, IO(Input/output), network and specific optimizations.

关键词： High performance computing Super-computing integration Unified resource management Hierarchical scheduling policy

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Utilizing Blockchain Technology for Financial Services in Parallel, Distributed, and grid computing Frameworks

Utilizing Blockchain Technology for Financial Services in Pa...

引用

2024 international conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Chokkamreddy, Prakash Palagati, Anusha Rao, Akurathi Lakshmi Pathi Maheswari, P. Mahadevan, Vanitha Balan, Santhosh Kumar School of Management Studies Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications KSR College of Arts and Science for Women Tiruchengode India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798331510299

Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain in improving the functionality of financial services like real time financial transactions, high frequency trading, portfolio and risk analysis is examined in this paper. It has been ascertained that namely parallel and distributed computing together with blockchain are the most suitable for facing increasing needs in data in the sphere of financial services. It also investigates the use of grid computing frameworks and their effects on the correspondingly balanced flows of medium amongst multiple systems. Various issues such as security, privacy violations, and adherence to the law are also reviewed, and existing prospects for developing through smart contracts and consensus mechanisms using blockchain. Exploring how some of the most successful pilot use-cases of blockchain are applicable for future growth in the financial services industry offers insights into how blockchain will soon enter mainstream as a disruptive technology in the broader global financial sector. © 2024 IEEE.

关键词： Smart contract

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Deep Learning Models in Simulating and Analyzing Smart grid Stability and Resilience 11

Deep Learning Models in Simulating and Analyzing Smart Grid ...

引用

11th IEEE/ACM international conference on Big Data computing, applications and Technologies, BDCAT 2024

作者： Rezazadah, Jeffrey Kashef, Rasha Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9798350367300

This research paper presents a comprehensive study on applying deep learning models for optimizing power balance management in smart grids. Utilizing a synthetic dataset based on a 4-node star network, this paper explores the efficacy of a deep learning model augmented to 60,000 observations, reflecting permutations of consumer nodes. The model incorporates advanced data preprocessing techniques, including feature selection, normalization, and outlier removal, alongside sophisticated machine learning strategies like Bayesian Optimization for hyperparameter tuning. The core of the research lies in evaluating the model's performance and resilience. The model was rigorously tested against various data loss scenarios using different imputation methods and assessed through metrics like accuracy, BCE Loss, and MSE Loss. These evaluations provided insights into the model's robustness and adaptability in simulating grid stability and resilience conditions. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on the Construction of Smart grid Digital System Based on Big Data Cloud computing Model Optimization Algorithm 4

Research on the Construction of Smart Grid Digital System Ba...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Li, Zhipeng Liu, Shaobo Zhang, Jianfeng Wang, Jun Shen, Shuo State Grid Gansu Electric Power Company Lanzhou China State Grid Gansu Electric Power Company Digital Division Lanzhou China

ISBN: (纸本)9798350359589

An intelligent power business development method based on digital service information is proposed. At the same time, a complete data mining framework is constructed. Parameters such as distance of adjacent pixels, similarity degree of carrier components and average amplitude were determined by coding and parsing. Then the digital business information resources are extracted. The mining method of data cloud in power system based on time series is established. The correlation degree of each information characteristic of middle station is analyzed based on classification and characteristic identification of power system middle station. Experiments show that the algorithm can effectively improve the distribution of data in the middle station in every minute. The standard deviation of the algorithm is reduced and the mining effect is improved. © 2024 IEEE.

关键词： artificial intelligence digital system Optimization algorithm smart grid

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Artificial Intelligence Cloud Platform for 5G Era 2

Artificial Intelligence Cloud Platform for 5G Era

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2nd IEEE international conference on Image Processing and Computer applications, ICIPCA 2024

作者： Liu, Yue Cai, Peilin Li, Dong Zhu, Beibei State Grid Henan Electric Power Information and Communication Company Zhengzhou450000 China

ISBN: (纸本)9798350360240

This paper studies an artificial intelligence cloud platform for the 5G era, specifically involving the fields of artificial intelligence, cloud computing and big data, including heterogeneous distributed cloud platforms, which take the distributed mobile cloud computing collaborative architecture as the basic architecture;Distributed mobile cloud computing collaboration architecture includes intelligent terminal, mobile network, distributed mobile cloud computing server, collaboration controller and end-to-end terminal computing offload service quality assurance mechanism system. This paper establishes a 5G oriented distributed mobile cloud computing collaboration architecture and an end-to-end terminal computing offload service quality assurance mechanism, which can reduce the network interaction signaling overhead of the terminal in the computing offload, as well as the CPU utilization and energy consumption of the terminal resident decision process;The distributed deep learning platform, which ensures the end-to-end service quality and supports the customization and expansion of deep learning models and algorithms, can avoid the huge load and energy consumption brought by traditional mobile cloud computing to the WAN. © 2024 IEEE.

关键词： Cloud platforms

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