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共 4330 条记录，以下是561-570 订阅

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Simulation of distributed Big Data Intelligent Fusion Algorithm based on Machine Learning

Simulation of Distributed Big Data Intelligent Fusion Algori...

引用

2022 International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022

作者： Tian, Di Henan Finance University Zhengzhou450000 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

Different from intelligent analysis applications in traditional small-scale data scenarios, intelligent analysis applications in big data scenarios are no longer a single AI algorithm model problem, but a fusion of big data, big models, and big computing. The existing data fusion algorithm has problems such as high network energy consumption and short lifetime of network nodes after data fusion. It is necessary to consider algorithm model design, big data processing and efficient distributed parallel computing at the same time, which brings a series of new challenges and problems to the research on the basic theoretical methods and key technologies of big data intelligent analysis. The method used in this paper is machine learning, setting an algorithm to have the machine analysis the data by itself using deep mining. It can largely finish the work that cannot be done manually. We need complex analysis based on machine learning and data mining, strong computing power and storage capacity to achieve massive data analysis. Through the research in this paper, it is concluded that the algorithm in this paper can achieve better time-to-time performance than the traditional algorithm. © 2022 IEEE.

关键词： Big data

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QRELATION: AN AGENT RELATION-based APPROACH FOR MULTI-AGENT REINFORCEMENT LEARNING VALUE FUNCTION FACTORIZATION 47

QRELATION: AN AGENT RELATION-BASED APPROACH FOR MULTI-AGENT ...

引用

47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Shen, Siqi Liu, Jun Qiu, Mengwei Liu, Weiquan Wang, Cheng Fu, Yongquan Wang, Qinglin Qiao, Peng Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Natl Univ Def Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9781665405409

The Centralized Training with Decentralized Execution paradigm (CTDE), which trains policies centrally with additional information, is important for Multi-Agent Reinforcement Learning (MARL). For CTDE, value function factorization methods make use of state during training and factorize the value function into multiple local value functions for decentralized execution. These approaches do not fully consider the relational information among agents, resulting in sub-optimal models for complex tasks. To remedy this issue, we propose QRelation which is a graph neural network approach for value function factorization. It considers both the static relations (e.g., agent types) and dynamic relations (e.g., close-by). We show that QRelation can obtain better results than state-of-the-art methods on challenging StarCraft II benchmarks.

关键词： Training conferences Reinforcement learning Benchmark testing Signal processing Graph neural networks Acoustics

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Evaluation of the Performance of a Computing Cluster based on Single Board Raspberry Pi 3B + Computer 4

Evaluation of the Performance of a Computing Cluster Based o...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Barkovska, Olesia Adamovych, Vladyslav Korniienko, Valentyn Filippenko, Oleh Filippenko, Inna Department of Electronic Computers Kharkiv National University of Radio Electronics Kharkiv Ukraine Design Automation Department Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Infocommunication Engineering V.V. Popovsky Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (纸本)9798350395532

The article is dedicated to the problem of parallel computing on multicomputer accelerated data processing systems, namely on cluster computing systems, which consist of many independent computers and the network that connects them. The computing components of the cluster being designed are four single-board Raspberry Pi 3B + computers. Raspbian Buster is used as the operating system for the designed cluster. The cluster system uses a SD memory card as a disk array and does not have the ability to connect nodes to the only one drive, so the topology of cluster pairs uses a distributed access topology that allows each memory disk to connect to only one cluster node. Messaging between processes is provided by the MPI interface. One of the main advantages of the cluster is the ability to scale, which, in the case of increasing computational load, leads to increased acceleration and efficiency of parallel computing. The performance of the created computing cluster based on four single-board Raspberry Pi 3B + computers was tested on the problem implementation of the large matrices accelerated multiplication, as matrix operations are the basis of scientific and engineering calculations, and are used in mathematical modeling of various processes, phenomena and systems. © 2023 IEEE.

关键词： Efficiency

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High Performance and Energy Efficient Integer Matrix Multiplication for Deep Learning

High Performance and Energy Efficient Integer Matrix Multipl...

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euromicro conference on parallel, distributed and network-based processing

作者： Pau San Juan Pedro Alonso-Jordá Enrique S. Quintana-Ortí Universitat Politècnica de València Spain

We present a multi-threaded implementation of the matrix multiplication for deep learning on ARM multicore processors. Following standard practice for inference with convolutional neural networks, our GEMM kernel operates with 16-bit integer arithmetic, yielding significant performance acceleration and cutting the memory requirements with respect to IEEE (floating point) single precision by half, allowing the deployment of larger neural network models on low power devices with limited storage capacity.

关键词： Performance evaluation Deep learning Program processors Multicore processing Neural networks Memory management Energy efficiency

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Reservoir Echo State network for Classification of Multivariate Time Series

Reservoir Echo State Network for Classification of Multivari...

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IEEE International conference on High Performance Computing Workshops (HiPCW)

作者： Basab Bijoy Purkayastha Shovan Barma Dept. of Physics Indian Institute of Technology Guwahati Guwahati India Dept. of ECE Indian Institute of Information Technology Guwahati Guwahati India

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.

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Truly Scalable Real-time Coincidence Processor based on Serial and parallel Hybrid Architecture

Truly Scalable Real-time Coincidence Processor based on Seri...

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2022 IEEE Nuclear Science Symposium, Medical Imaging conference, and Room Temperature Semiconductor Detector conference, IEEE NSS MIC RTSD 2022

作者： Cui, Mingyue Zhao, Zhixiang Qu, Huifan Huang, Qiu Shanghai Jiao Tong University School of Biomedical Engineering Shanghai China

ISBN: (纸本)9781665488723

Many PET systems use coincidence processors based on a serial architecture for coincidence detection, but this single-threaded coincidence detection has high latency and may even lose events in the case of a high event rate. The parallel coincidence processor has high event processing efficiency. However, the increase in detectors leads to a rapid expansion in the usage of hardware resources. In this study, we investigate a coincidence processor based on a serial and parallel hybrid architecture. The proposed processor first groups and merges data streams to reduce the number of them then uses serial processing to detect coincidence events within the merged data streams. After that, the processor uses a homogeneously distributed coincidence detection network to achieve parallel coincidence detection between different streams. The hybrid architecture of the coincidence processor saves hardware resource usage when the number of input data streams is high. When the coincidence processor has 10 input channels in the second stage of coincidence detection, it can achieve a maximum data transfer rate of 400M events/s and has a coincidence detection efficiency larger than 99.97%. The hybrid architecture of the coincidence processor presented in this paper enables real-time coincidence detection for systems with high event rates such as the long-axis PET while saving lots of hardware resources. © 2022 IEEE.

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Big Data Classification Model and Algorithm based on Double Quantum Particle Swarm Optimization

Big Data Classification Model and Algorithm Based on Double ...

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2023 IEEE International conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： Jingbo, Duan Guangdong Business and Technology University Guangdong526060 China Philippine Christian University Center for International Education Manila1004 Philippines

ISBN: (纸本)9798350399028

For the traditional big data classification models and algorithms that have the drawback of long processing time, the use of intelligent algorithms to model identification of multivariable systems has become a mainstream research direction. Discussion This paper proposes an efficient solution for data-parallel optimized computing. The problems in the identification of multi-variable systems that integrate historical big data and intelligent algorithms are analyzed, and the impact of input variables on output is quantified. Build a big data classification model, mine valid data points, arrange data combination methods, divide data local connection methods, obtain local node micro-cluster data, calculate redundant data and invalid data increment values in node data groups, and reconstruct central node sample algorithm, adjust the integrated data classification strategy, optimize and update the data integration classification method. Design the experiment, simulate the experimental environment, and verify that the proposed model calculation method can shorten the data processing time in practical application, which has practical research value. Through the design of the overall process of the algorithm, data classification to form a data set, and then describe the steps of the algorithm, and finally verify the feasibility of the algorithm through simulation experiments, which can not only greatly reduce the communication cost between network nodes, but also greatly improve the global mining accuracy and improve the query efficiency in distributed data, It has good utilization value in the analysis of massive data information. © 2023 IEEE.

关键词： Big data

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A Differential Privacy Image Publishing Method based on Wavelet Transform 22nd

A Differential Privacy Image Publishing Method Based on Wave...

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22nd International conference on parallel and distributed Computing, Applications and Technologies (PDCAT 2021)

作者： Zhang, Guifen Wei, Hangui Ge, Lina Qin, Xia Guangxi Univ Nationalities Sch Artificial Intelligence Nanning Peoples R China Guangxi Univ Nationalities Key Lab Network Commun Engn Nanning Peoples R China

ISBN: (纸本)9783030967727;9783030967710

Image is an important information-bearing medium with many important attributes. If the image data is released directly, personal privacy will be compromised. This paper aims at how to use the method of differential privacy to protect the privacy of image data and make the image data have high usability. In this paper, a WIP method based on wavelet change is proposed. Firstly, wavelet transform is used to compress the image. Then, noise is added to the main features after transformation to obtain the published image satisfying the differential privacy. It solves the problem of low usability of large images and the problem that Fourier transform cannot deal with abrupt signal. Experimental results show that compared with similar methods in the frequency domain, the denoised image obtained by the proposed WIP method is more distinguishable and the information entropy is closer to the original image. The accuracy is 10% higher than other methods. Compared with other frequency-domain methods for image differential privacy protection, the proposed WIP method has higher usability and robustness.

关键词： Image processing Differential privacy Privacy protection The wavelet transform

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Research on Trust Evaluation Model for Vehicle-to-Grid Interaction based on Zero Trust Architecture

Research on Trust Evaluation Model for Vehicle-to-Grid Inter...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Nankai Zhou Shenglong Ji Qingmei Mao North China University of Technology Beijing China Qianxin Technology Group Co. LTD

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

During vehicle-to-grid (V2G) interactions, information is transmitted across multiple platforms, posing significant security risks to both information transmission and network boundaries. Traditional evaluation techniques, which rely on fixed permission assignments and predefined rules, struggle to adapt to scenarios characterized by a large number of vehicles, frequent changes in network topology, and diverse service demands. This paper proposes a trust evaluation model based on zero trust architecture. By introducing multidimensional user attribute sets, platform attribute feature sets, and user recommendation attributes, the model dynamically evaluates the trustworthiness of users and platforms. It uses trust values to dynamically authorize access subjects and assess platform reliability. This approach enhances the system's ability to identify malicious behavior and improves the security of data resources and transmission in V2G interactions. Experimental results indicate that the proposed zero trust-based trust evaluation model significantly enhances the security and dynamic responsiveness of V2G interactions.

关键词： Vehicle-to-grid Technological innovation network topology Data security Evaluation models Information processing Zero Trust

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GRAPHGUESS: Approximate Graph processing System with Adaptive Correction 28th

GRAPHGUESS: Approximate Graph Processing System with Adaptiv...

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28th International conference on parallel and distributed Computing (Euro-Par)

作者： Ramezani, Morteza Kandemir, Mahmut T. Sivasubramaniam, Anand Penn State Univ State Coll PA 16801 USA

ISBN: (纸本)9783031125973;9783031125966

Graph-based data structures have drawn great attention in recent years. The large and rapidly growing trend on developing graph processing systems focuses mostly on improving the performance by preprocessing the input graph and modifying its layout. These systems usually take several hours to days to complete processing a single graph on high-end machines, let alone the overhead of pre-processing which most of the time can be dominant. Yet for most graph applications the exact answer is not always crucial, and providing a rough estimate of the final result is adequate. Approximate computing is introduced to trade off accuracy of results for computation or energy savings that could not be achieved by conventional techniques alone. In this work, we design, implement and evaluate GraphGuess, inspired from the domain of approximate graph theory and extend it to a general, practical graph processing system. GraphGuess is essentially an approximate graph processing technique with adaptive correction, which can be implemented on top of any graph processing system. We build a vertex-centric processing system based on GraphGuess, where it allows the user to trade off accuracy for better performance. Our experimental studies show that using GraphGuess can significantly reduce the processing time for large scale graphs while maintaining high accuracy.

关键词： Graph processing Approximate Computing

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