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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34068 条记录，以下是201-210 订阅

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A Time-Efficient distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency 48

A Time-Efficient Distributed Constant Conditional Functional...

引用

48th Annual IEEE international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Chen, Junchao Thou, Jian-tao Hao, Xinyu Inner Mongolia Univ Natl & Local Joint Engn Res Ctr Intelligent Infor Inner Mongolia Engn Lab Cloud Comp & Serv Softwar Inner Mongolia Key Lab Social Comp & Data ProcCo Hohhot Peoples R China

ISBN: (纸本)9798350376975;9798350376968

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Data analysis Data quality Data consistency distributed computing Constant conditional functional dependency

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Research on Standardized Information Model of distributed Energy grid-Connected Communication Considering Social Factors 5

Research on Standardized Information Model of Distributed En...

引用

5th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Zhang, Hengrong Liu, Yinliang Zhang, Ruifeng Lin, Xinhao Xiao, Xiaobing Li, Zhangcheng Electric Power Research Institute of Guizhou Power Grid Co. Ltd Guiyang China Guangzhou China

ISBN: (纸本)9798350333442

The large-scale grid connection of distributed energy resources is an effective way to solve the problems of power supply shortage and environmental pollution, and there is no unified standard for the communication protocol when the existing distributed energy resources are connected to the grid. These standards focus on the electrical characteristics of the transformer itself, ignoring social factors. Firstly, this paper analyzes the business data requirements of distributed energy grid-connected communication. Then, the 'phenomenon/data/data-provider' pattern is extracted, and a standardized information model of distributed energy grid-connected communication is constructed. Finally, the summary and application prospect are carried out. © 2023 IEEE.

关键词： Energy resources

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Deep Reinforcement Learning-Based Smart grid Resource Allocation System 16

Deep Reinforcement Learning-Based Smart Grid Resource Alloca...

引用

international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart Data, SmartData and Congress on Cybermatics (Cybermatics)

作者： Lang, Qiong Zhu, La Ba Dun Ren, Mi Ma Ci Zhang, Rui Wu, Yinghen He, Wenting Li, Mingjia State Grid Tibet Elect Power Co Ltd Tibet Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (纸本)9798350309461

The existing power grid scheduling systems often use a distributed generation model. Because they need to manage multiple independent generators simultaneously, this system may be more complex in terms of coordination, scheduling, and maintenance. In addition, the variability and intermittency of renewable energy resources can lead to reliability issues, causing grid instability. To address issues such as resource wastage, high costs, and grid instability that occur during the power grid resource scheduling process, this paper proposes an intelligent power distribution system to achieve a rational allocation of electrical resources throughout the network. Firstly, the algorithm introduces a deep learning-based node fault detection module to address the problem of the lack of real-time monitoring and fault detection capabilities in traditional distribution networks. Secondly, by modeling it as a Markov decision process (MDP), it constructs state, action, and reward functions and uses a deep reinforcement learning module based on double deep Q-network (DDQN) to optimize the objective function. This ensures the allocation of power resources during peak periods, reduces energy waste, and avoids overloads. Experiments show that this algorithm has excellent fault localization capabilities, improving the stability of the grid during peak electricity demand periods. Additionally, it offers more flexibility in resource scheduling, enabling more precise resource allocation.

关键词： Markov Decision Process Deep Reinforcement Learning grid Resource Allocation Distribution Network

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Performance evaluation of GPU-enhanced Jacobi algorithm implementations for optimizing CFD Poisson partial differential equation solving 28

Performance evaluation of GPU-enhanced Jacobi algorithm impl...

引用

28th international conference on System Theory Control and computing

作者： Sbirna, Sebastian Sbirna, Liana-Simona Tech Univ Denmark Inst Appl Math & Comp Sci DTU Compute Dept Lyngby Denmark Univ Craiova Fac Sci Dept Chem Craiova Romania

ISBN: (纸本)9798350364309;9798350364293

The current work presents an in-depth analysis of several optimizations using GPU parallel computing applied to the Jacobi method for solving Poisson partial differential equations in computational fluid dynamics (CFD). We expand on previous CPU-parallelized Jacobi algorithm research, exploring four GPU-optimized Jacobi method variants: singlethreaded, multi-threaded, multi-GPU and a norm-based stopping criterion kernel. These implementations are benchmarked against a multi-threaded CPU baseline. Results indicate that, whereas the single-threaded GPU version is slower than the CPU baseline, multi-threaded GPU versions achieve significant speed gains, especially for larger grid sizes. The multiGPU version doubles memory bandwidth, enhancing performance for extensive computations, despite overhead for smaller matrices. The norm-stopping criterion kernel offers early convergence for small matrices but at a high overhead cost. Profiling confirms a memory-bound bottleneck, suggesting single-precision and optimized memory access as improvements. Ultimately, multi-threaded GPU kernels substantially outperform the CPU baseline for large-scale CFD problems, establishing GPUs as efficient accelerators for the Jacobi algorithm.

关键词： GPU computational optimization parallel processing software engineering CFD Poisson process

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Research on parallel optimization and acceleration technology for new energy power generation large model computing based on localized supercomputing platforms

Research on parallel optimization and acceleration technolog...

引用

2024 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Gan, Rundong Wang, Bin Luo, Chen Mu, Xuepeng Wang, Che Su, Haibin Liu, Bin Power Grid Planning & Research Center Guizhou Power Grid Co Ltd Guizhou Guiyang550000 China Fangxin Technology Co. Ltd. Hunan Changsha410000 China Hunan Electric Power Computing Power Integration Engineering Technology Research Center Hunan Changsha410000 China

ISBN: (纸本)9781510685840

Due to the disconnection between arithmetic demand and supply, and encountering the limitations of cloud blocking in the upgrading process, in order to solve the challenges of computational complexity and accuracy, as well as the problems of data security and privacy protection, we propose the research of parallel optimization and acceleration technology for new energy power generation large-scale model computation based on localized supercomputing platform. This study enhances efficient parallel algorithms in the prediction process by parallelizing the processing and improving the time complexity of the algorithms, dividing the large-scale computational task into multiple subtasks and executing these subtasks simultaneously, establishing regular performance monitoring and optimization plans, and periodically evaluating the performance of the system. After the optimization measures are implemented, performance testing and validation are carried out, and the experiments show that the computational parallel optimization acceleration of new energy generation capacity prediction is realized on the supercomputing platform. The optimization effect meets the expectation and achieves 100% of the set performance index. © 2024 SPIE.

关键词： Cloud platforms

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Quantum-Enabled distributed Transient Stability Assessment of Power Systems 5

Quantum-Enabled Distributed Transient Stability Assessment o...

引用

2024 international conference on Quantum computing and Engineering

作者： Yu, Sijia Zhou, Yifan Wang, Lizhi SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA Siemens Technol Sustainable Bldg & Grid Grp Princeton NJ 08540 USA

ISBN: (纸本)9798331541378

Transient stability assessment (TSA) is an indispensable routine in power system operation and control. The increasing integration of distributed energy resources highlights the necessity of distributed transient stability assessment which can effectively capture the complicated stability characteristics of the entire power system without compromising the data privacy of individual local subsystems. This paper devises a quantum-enabled distributed transient stability assessment (Q-dTSA) method to enable data-driven transient stability prediction of power grids in a distributed, expressive and privacy-preserving manner. Our contributions include: 1) A quantum federated learning (QFL) architecture, which enables local power grids to jointly realize the data-driven TSA for the entire system using shallow-depth quantum circuits;2) A distributed quantum gradient descent (d-QGD) algorithm, which supports effective coordination between local subsystems to perform distributed training of the QNNs without leaking local power system information. 3) Extensive experiments in real-scale power grids obtained from both noise-free simulators and noisy IBM quantum computers, which validate the accuracy, fidelity, and noise-resilience of Q-dTSA, as well as its superiority over centralized quantum computing algorithms.

关键词： Quantum machine learning power system stability transient stability assessment distributed quantum processing quantum federated learning

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Using grid Search Methods and parallel computing to Reduce AI Training Time for Reliability Lifetime Prediction of Wafer-Level Packaging 24

Using Grid Search Methods and Parallel Computing to Reduce A...

引用

24th international conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)

作者： Chang, C. Y. Lee, C. H. Chiang, K. N. Natl Tsing Hua Univ Adv Microsyst Packaging & Nanomech Res Lab Dept Power Mech Engn Hsinchu 300 Taiwan

ISBN: (纸本)9798350345971

Electronic packaging technology undergoes Accelerated Thermal Cycling Test (ACTC) before hitting the market. Finite element analysis is commonly used to build models for electronic packaging products. However, simulation errors may arise due to differences in physical concepts and considerations among researchers. To overcome this challenge, we create a database through validated finite element models and combine it with machine learning. In the domain of machine learning models, training time is a crucial research focus. Nevertheless, grid search time is often overlooked, despite its significant impact on machine learning model efficiency. To address this issue, this study utilizes parallel computing to explore the search for optimized hyperparameters in the context of the Wafer Level Chip Scale Package (WLCSP) as a case study. Additionally, custom empirical formulas are utilized to enhance the efficiency of grid search methods, thereby improving the time-to-market and competitiveness of packaged products.

关键词： Wafer Level Packaging Finite Element Analysis Thermal Cycle Load Reliability Estimation Machine Learning parallel computing grid Search

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Straggler Mitigation in Edge computing with Coded Compressed Sensing

Straggler Mitigation in Edge Computing with Coded Compressed...

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international conference on computing, Networking and Communications (ICNC)

作者： Tao, Yangyang Zhou, Junxiu Northern Kentucky Univ Dept Comp Sci Highland Hts KY 41099 USA

ISBN: (纸本)9798350371000;9798350370997

In edge computing the performance of distributed edge computation tasks are adversely impacted by stragglers (i.e., slowest devices). Previous research addresses this problem using coding techniques to bypass the dependence on stragglers. In stead of completely discarding partially unfinished coded computations on stragglers, recent research incorporates those computations contributed by stragglers before the deadline. A faster computation recovery is achieved. One problem with this approach, however, is the recovery accuracy because it is based on lossy quantization over coded data. In this paper, we treat the partially unfinished coded computation as erroneous computations and formulate the computation recovery problem as a compressed sensing (CS) problem. With this rateless approximate code approach, we can recover the erroneous computations with a high accuracy rate when the ratio of stragglers is relatively low. Experimental results show that we reduce the error rate by average of 32% under various straggler ratios compare with the state of the art.

关键词： Edge computing parallel Machine Learning Federated Learning Compressed Sensing Rateless Coding

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Analysis and Simulation of DC Microgrid for Sustainable Energy Development 3

Analysis and Simulation of DC Microgrid for Sustainable Ener...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Jayaram, Kruthi Vidya, H.A. Bnm Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical & Electronics Engineering Bengaluru India

ISBN: (纸本)9798331515911

A Cutting edge modern day technology for the existing conventional power system is the idea of smart grid. To eradicate climate changes, market variations and security of power supply in near future smart grids help in restructuring the power structure. Smart grids are applied to low carbon energy emissions, electric vehicles, microgrid, renewable energy, flexible power system, distributed energy resources (DER's), advanced metering infrastructure, fuel markets, customer support etc. In this paper, Analysis of DC Microgrid is implemented with distributed generation. On site generations like PV, wind and battery act as input sources and deliver output to the load. The proposed model is simulated in MATLAB Simulink and the results are verified for the operation of DC Microgrid using distributed energy resources. Results highlight upon the flow of power continuously using any one the input resources. The structure is implemented in MATLAB/Simulink and the results are tabulated. © 2025 IEEE.

关键词： Battery DC Bus Microgrid PID Controller PV Smart grid Wind

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Enhanced Resource Discovery Algorithm for Efficient grid computing 3

Enhanced Resource Discovery Algorithm for Efficient Grid Com...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Alqaraleh, Muhyeeddin Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa13110 Jordan

ISBN: (纸本)9798350375190

In general, parallel applications require lots of computer power and grid computing. Efficient Resource Discovery (RD) algorithms determine grid resource allocation and execution time. To improve the resource distribution and reduce grid node communication costs, this study introduces hierarchical, weighted RD (WRD). A behavioral modeling technique checks the algorithm's accuracy and efficacy. For complete analysis and simulation, StarUML implements a WRD algorithm behavioral model. The NuSMV model checker evaluates reachability and deadlock-free RD. The WRD algorithm is assessed using key performance metrics. To evaluate resource-finding efficiency, each request's inspected nodes are counted. The number of re-discovery operations shows the algorithm's resource flexibility. Algorithms that find free resources with high bandwidth links are also evaluated to optimize grid resource allocation. Resource information tables could improve resource location. Resource information is stored in a table to help the algorithm allocate resources. This research seeks to develop grid computing by solving RD problems. The hierarchical architecture and weighted resource selection of the WRD algorithm improve resource inspection, flexibility, and high-bandwidth RD. Behavioral modeling and verification show the algorithm's accuracy and grid suitability. WRD and resource information tables boost grid computing RD efficiency and efficacy. This research optimizes grid performance and resource allocation. © 2024 IEEE.

关键词： grid computing

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