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

共 34218 条记录，以下是121-130 订阅

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A Bi-Layer Joint Training Reinforcement Learning Framework for Post-Disaster Rescue 20

A Bi-Layer Joint Training Reinforcement Learning Framework f...

引用

20th Annual international conference on distributed computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Huang, Songjun Sun, Chuanneng Gong, Jie Pompili, Dario Rutgers Univ New Brunswick Dept Elect & Comp Engn New Brunswick NJ 08901 USA Rutgers Univ New Brunswick Dept Civil & Environm Engn New Brunswick NJ USA

ISBN: (纸本)9798350369458;9798350369441

In modern post-disaster rescue missions, the deployment of Multi-Robot Systems (MRSs) plays a key role in minimizing injuries and deaths among rescue personnel and civilians involved in the disaster. Achieving optimal MRS efficiency requires the implementation of a well-suited task allocation mechanism and a highly efficient pathfinding algorithm. However, due to inconsistent communication and low bandwidth, traditional frameworks in the mentioned domains may be impractical or may not work well. To address these problems, a novel Bi-Layer Joint Training Reinforcement (BJoT-RL) framework is proposed where, in the first layer, a Multi-Head Deep Q-Learning (MHDQN) is designed to perform task allocation;whereas, in the second layer, a Condition-Constrained Q-Learning (CCQ) is proposed to perform pathfinding. Noticeably, the output of each layer is used in the training of the other layer to realize a tight coupling, hence the innovative joint training. Thorough simulations show that the BJoT-RL framework performs better than state-of-the-art solutions in such applications.

关键词： Multi-Agent Reinforcement Learning distributed Learning and computing Task Allocation Pathfinding

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Descriptive Analysis of the Cloud computing Services and Deployment Models 2

Descriptive Analysis of the Cloud Computing Services and Dep...

引用

2nd international conference for Advancement in Technology, ICONAT 2023

作者： Kaur, Tajinder Kamboj, Sushil Chandigarh University Computer Science Department Mohali India

ISBN: (纸本)9781665475174

Cloud have wide-ranging implications in several areas of computing, notably massive knowledge, cloud computing has simply up to the highest of any list of technology subjects. Furthermore, it is one of the most important chop-chop developing technologies due to the availability of a wide range of computer infrastructure and services. The expansion of cloud computing has included mainframe computing, cluster and grid computing, distributed and parallel computing technologies, and so on. Cloud computing has mostly supplanted grid, cluster, and mainframe computing. This refers to the delivery of numerous services over the web differently. Such as databases, infrastructure, run time platforms, knowledge storage, servers, networking, and code area unit samples of these resources. Price savings, bigger productivity, speed, efficiency, high performance, high computing power, and security are simply many of the explanations why consumers and businesses embrace the on demand computing. This article goes through the definition of cloud computing as well as its properties, various cloud services, deployment models, and security concerns. © 2023 IEEE.

关键词： grid computing

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On the Dynamics of Non-IID Data in Federated Learning and High-Performance computing 32

On the Dynamics of Non-IID Data in Federated Learning and Hi...

引用

32nd Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Annunziata, Daniela Canzaniello, Marzia Chiaro, Diletta Izzo, Stefano Savoia, Martina Piccialli, Francesco Univ Naples Federico II Dept Math & Applicat R Caccioppoli Naples Italy

ISBN: (纸本)9798350363074;9798350363081

This paper investigates the symbiosis of Federated Learning (FL) and High-Performance computing (HPC) architectures, unraveling challenges introduced by the intricate interplay of heterogeneity and non-Independently and Identically distributed (non-IID) data. By leveraging the Flower framework, our research delves into the nuanced implications of FL in diverse HPC environments. We provide a comprehensive exploration of the heterogeneity within contemporary HPC architectures, spanning node organizations, memory hierarchies, and specialized accelerators, emphasizing adaptability to this complexity. Methodologically, we simulate a FL scenario within our research laboratory, leveraging Flower to orchestrate collaborative model training across heterogeneous nodes. The experiments involve variations in the Dirichlet beta parameter, offering insights into the effects of non-IID data. Results encompass communication efficiency, energy efficiency, and global model accuracy, providing a holistic understanding of the performances across diverse HPC infrastructures. This research contributes to the ongoing discourse on efficient and scalable algorithms, providing insights for collaborative learning in the era of diverse HPC architectures.

关键词： Federated Learning High-Performance computing (HPC) Heterogeneous Architectures Flower Framework

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Short Term Nonlinear Load Forecasting Method for distributed Photovoltaic grid Connection Based on GSA Algorithm 3

Short Term Nonlinear Load Forecasting Method for Distributed...

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Xu, Pengfei Li, Yan Tang, Mengyuan Tian, Xing Gao, Baohao Economic and Technological Research Institute of State Grid Ningxia Electric Power Co. Ltd. Yinchuan Ningxia750004 China

ISBN: (纸本)9798331530280

In order to cope with the intermittency and uncertainty of photovoltaic power generation and improve the accuracy of short-term load forecasting, a distributed photovoltaic grid connected short-term nonlinear load forecasting method based on Genetic Simulated Annexing Algorithm (GSA) is proposed. The historical data of the distributed photovoltaic grid connected system is used as the analysis basis, and the concept of particle aggregation is introduced to establish a solution space. The global load data is searched in the space, and the original data is substituted into the solution space to achieve load data normalization processing. Design a distributed photovoltaic grid connected short-term nonlinear load forecasting method based on GSA, obtain the global optimal solution, and accurately predict short-term load changes. The experimental results show that this method has significantly improved prediction accuracy and can more accurately predict short-term nonlinear loads. © 2024 IEEE.

关键词： distributed photovoltaic grid connected system Genetic simulated annealing algorithm Load data preprocessing Short term nonlinear load

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Exploration of Infinite Triangular grid by Myopic Robots without Chirality 25

Exploration of Infinite Triangular Grid by Myopic Robots wit...

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26th international conference on distributed computing and Networking, ICDCN 2025

作者： Das, Raja Sau, Buddhadeb Department of Mathematics Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9798400710629

Exploration of a graph network means each node of the graph has to be visited by at least one robot. The problem of exploration has been studied in various networks like rings, trees, finite rectangular grids, etc. If we consider infinite domain, previous works solved the problem on infinite rectangular grid and the robots have common chirality. In this work, we have studied the problem of exploration of infinite triangular grid. We have given an algorithm that works under a fully synchronous scheduler. Five robots can explore the infinite triangular grid without collision executing the algorithm. We have used myopic luminous robots. The lights of the robots can be of four different types. Each robot can see up to two hop distance. The robots are without any chirality and there is no axis agreement among the robots. © 2025 Copyright held by the owner/author(s).

关键词： Chirality

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Reservoir Echo State Network for Classification of Multivariate Time Series 30

Reservoir Echo State Network for Classification of Multivari...

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30th international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Purkayastha, Basab Bijoy Barma, Shovan Indian Inst Technol Guwahati Dept Phys Gauhati India Indian Inst Informat Technol Guwahati Dept ECE Gauhati India

ISBN: (纸本)9798350383782;9798350383799

Multivariate time series (MTS) classification has been tackled using various methods, including Reservoir computing (RC), which generates efficient vectorized representations like reservoir state (RS). RS shines when handling extensive classes or training sets but demands longer processing and substantial memory. Addressing this, in this study we present the parallel Reservoir Echo State Network (PR-ESN), an optimized parallel training and evaluation algorithm rooted in the ESN principle. It leverages both CPU-shared memory and parallel distributed memory architecture to efficiently capture reservoir state's optimal model space representation, addressing computational challenges in MTS analysis. Distinguishing itself from previous works, PR-ESN combines distributed parallel processing at the network level and shared memory multiprocessing at the node level. This results in reduced memory requirements and faster processing, making it a significant contribution to the field. Key features include PR-ESN's distributed training and evaluation, shared memory parallelization, and MSR concatenation for comprehensive analysis of distributed model space representations. Testing on real-world MTS and benchmark ECG data proves PR-ESN-based classifiers achieve superior accuracy and faster processing times with optimal memory usage. Testing on real-world MTS and benchmark ECG data proves PR-ESN-based classifiers achieve superior accuracy and faster processing times with optimal memory usage.

关键词： Shared Memory parallelism distributed Memory parallelism Echo State Network Reservoir computing parallel computing Multivariate Time Series Classification

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DF-SegDiff: ADiffusion Segmentation Model using a New distributed parallel computing Algorithm

DF-SegDiff: ADiffusion Segmentation Model using a New Distri...

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2024 IEEE international conference on Cognitive computing and Complex Data

作者： Mi, Hancang Gan, Hong-Seng Wang, Xiaoyi Shimizu, Akinobu Ramlee, Muhammad Hanif Unlu, Mehmet Zubeyir Xian Jiaotong Liverpool Univ Sch AI & Adv Comp Suzhou Peoples R China Xian Jiaotong Liverpool Univ Sch Math & Phys Suzhou Peoples R China Tokyo Univ Agr & Technol Inst Engn Tokyo Japan Univ Teknol Malaysia Dept Biomed Engn & Hlth Sci Johor Baharu Malaysia Izmir Inst Technol Dept Elect & Elect Engn Izmir Turkiye

ISBN: (纸本)9798350352900;9798350352894

Brain tumours are among the most life-threatening diseases, and automatic segmentation of brain tumours from medical images is crucial for clinicians to identify and quantify tumour regions with high precision. While traditional segmentation models have laid the groundwork, diffusion models have since been developed to better manage complex medical data. However, diffusion models often face challenges related to insufficient parallel computing power and inefficient GPU utilization. To address these issues, we propose the DF-SegDiff model, which includes diffusion segmentation, parallel data processing, a distributed training model, a dynamic balancing parameter and model fusion. This approach significantly reduces training time while achieving an average Dice score of 0.87, with several samples reaching Dice values close to 0.94. By combining BRATS2020 with the Medical Segmentation Decathlon dataset, we also integrated a comprehensive dataset containing 800 training samples and 53 test samples. Evaluation of the model using Dice, IoU, and other relevant metrics demonstrates that our method outperforms current state-of-the-art techniques.

关键词： distributed Algorithms Brain Tumour Diffusion Segmentation

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distributed parallelized Password Cracking Framework for Enhanced Cybersecurity using Authentication Codes 3

Distributed Parallelized Password Cracking Framework for Enh...

引用

international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Balasubramaniam, N. Sweetlin, Dhalia J. Anirudh, R. Anna Univ Dept Informat Technol Chennai India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

An innovative approach to password cracking by leveraging a distributed computing model is developed. The sys- tem comprises a client-server architecture where clients receive segmented password ranges for parallelized hashing attempts. The Java-based implementation employs MD5 hashing and divides the password into smaller units for distribution among multiple clients. Each client independently processes its assigned password range, attempting to match the hash against a pre- determined target. The server orchestrates the distribution of password segments and collects results from clients, facilitating the cracking process. Security measures include secure communication protocols, and ethical considerations center around legality, user consent, and emphasizing the educational value of responsible hacking practices. The work explores the tech- nical challenges of distributed password cracking, addressing efficiency, scalability, and security implications, while fostering a deeper understanding of cybersecurity and distributed systems.

关键词： MD5 SHA256 password cracking distributed systems parallel computing

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distributed Large Memory and Message Passing Interface in Cloud Environment using OpenStack Cloud Platform 15

Distributed Large Memory and Message Passing Interface in Cl...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deshmukh, Pranjali P. Prof. Ram Meghe Institute of Technology & Research Badnera Amravati India

ISBN: (纸本)9798331300579

distributed large memory offers the use of large virtual memory by using remote memory distributed over nodes in a cluster. The message passing interface (MPI) plays important role in DLM. MPI-based DLM manages the large virtual memory over the cluster using MPI batch queuing system. In distributed memory system message MPI is important parallel programming. As the number of virtual machines increases MPI and shared memory parallel programming combined to perform efficient communication in virtual machines [1,2]. Various tasks communicate with each other by sharing memory on the different disk through shared memory distributed computing. If various tasks are trying to access same the same memory location for reading and writing purpose, sometimes it may cause deadlock. In this case synchronization mechanism should handle such situation to keep the integrity of the data [2]. MapReduce and Hadoop are having inbuilt functions to handle distributed shared memory mechanism. The distributed computing with MPI can process thousands of parallel processes in cloud environment. © Grenze Scientific Society, 2024.

关键词： Cloud platforms

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Research and Application of Low Voltage distributed Power Supply Control System 1

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7th international conference on Cognitive computing (ICCC) Held as Part of the Services conference Federation Meeting (SCF)

作者： She, Shida Yu, Tongwei Ge, Junxiong Hong, Haimin Yan, Zhenhong Wang, Tong Zhang, Wuyang Shi, Mingfeng State Grid Liaoning Elect Power Co Ltd Elect Power Res Inst Shenyang 110006 Peoples R China China Gridcom Co Ltd Shenzhen 518109 Peoples R China

ISBN: (数字)9783031518263

ISBN: (纸本)9783031518256;9783031518263

With the launch of the National Energy Administration's rooftop photovoltaic pilot construction in all counties and cities, power companies in various provinces and cities of the State grid are facing severe safety management challenges. For example, distributed power sources such as photovoltaic and wind power are affected by natural weather, and their output is intermittent and difficult to control. Large-scale integration into the power system will have an impact on the power system, making it unable to operate normally, and even leading to power system collapse and damage to power ***, it is necessary to form a low-cost solution that meets the basic requirements of stable and safe operation of the power grid. In this paper, we propose a regulation scheme of low-voltage distributed generation, which uses the edge computing capability deployed in the distribution transformer fusion terminal, and the communication capability of power line carrier and the intelligent circuit breaker integrated with function fusion to realize the observation and control of distributed energy and electrical equipment. The proposed scheme has been piloted and verified in two typical power distribution areas in Shenyang, and the pilot application has achieved the expected goals.

关键词： Photovoltaic distributed Generation Regulation Power Line Carrier Fusion Terminal Intelligent Circuit Breaker

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