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检索条件"任意字段=NPC 2009 - 6th International Conference on Network and Parallel Computing"

共 603 条记录，以下是21-30 订阅

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DGIC: A Distributed Graph Inference computing Framework Suitable For Encoder-Decoder GNN 2022

DGIC: A Distributed Graph Inference Computing Framework Suit...

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6th international conference on Innovation in Artificial Intelligence (ICIAI)

作者： Pan, Zeting Chang, Junsheng Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (纸本)9781450395502

*A graph is a structure that can express the relationship between objects. the emergence of GNN enables deep learning to be applied in the field of graphs. However, most GNNs are trained offline and cannot be directly used in real-time monitoring scenarios such as financial risk control. In addition, due to the large scale of graph data, a single machine often cannot meet actual needs, and there are bottlenecks such as throughput performance. therefore, we propose a distributed graph inference computing framework, which can be applied to Encoder-Decoder GNN models. We complete the adaptation of the model by disassembling the graph data and using the extension storage and dynamic invocation mechanism to solve the model invocation problem. For inference performance, we implement dynamic graph construction through incremental composition and decouple the inference process to apply to different scenarios, so that GNNs conforming to the Encoder-Decoder style can be applied to the framework. A large number of experiments show that this method has good timeliness while improving the throughput upper limit, and can maintain the model effect of multi-tasking.

关键词： Graph inference Graph Neural networks Distributed graph computing

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Application research of time-slot task synchronization method based on timestamp counter polling in electromagnetic transient real-time simulation 6

Application research of time-slot task synchronization metho...

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6th international conference on Information Science, Electrical, and Automation Engineering, ISEAE 2024

作者： Guo, Qi Lu, Yuanhong Guo, Tianyu Zhang, Jie Huang, Libin Tu, Liang Guo, Zhilong Xu, Dehuai Guo, Chuang State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China Dawning Network Technology Corp. Ltd. Wuhan China

ISBN: (纸本)9781510682924

In the practical application of electromagnetic transient real-time simulation computing platform, due to the large amount of calculation and long calculation time of electromagnetic transient simulation calculation, in order to improve the calculation speed and accuracy, it is usually necessary to use multiple CPU cores for parallel calculation at the same time, and ensure that each task synchronization and coordination among the computing cores. So, it is of great significance to select an appropriate task synchronization method for the realization of the electromagnetic transient real-time simulation computing platform. the paper puts forward a kind of based on TSC (Time Stamp Counter) Time Stamp Counter polling Time slot task synchronization method, this method supports high precision Time synchronization and coordination between different tasks, and can adapt to the transmission and efficient processing of large amounts of data. the test results prove that this method can avoid the overhead and delay of a large number of interrupts and thread switching, improve the efficiency, stability and predictability of task execution, and meet the performance requirements of the task synchronization method based on the centralized tightly coupled electromagnetic transient real-time simulation prototype. © 2024 SPIE.

关键词： Electromagnetic transients

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Handwritten Digit Recognition for Native Gujarati Language Using Convolutional Neural network 6th

Handwritten Digit Recognition for Native Gujarati Language U...

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6th international conference on Innovative computing and Communication, ICICC 2023

作者： Rajyagor, Bhargav Rakholia, Rajnish Gujarat Technological University Gujarat Ahmedabad India S. S. Agrawal Institute of Management and Technology Gujarat Navsari India

ISBN: (纸本)9789819940707

Computer vision's most active area of research is handwritten digit recognition. Numerous applications essentially motivate to build of an effective recognition model using computer vision that can empower the computers to analyze the images in parallel to human vision. Most efforts have been devoted to recognizing the handwritten digits, while less attention has been paid to the recognition of handwritten digits in resource-poor languages such as Gujarati is a native language in India mainly due to the morphological variance in the writing style. We used a CNN-based model for digit recognition and a Convolution Neural network for classification in this paper to propose a customized deep learning model for the recognition and classification of handwritten Gujarati digits. this work makes use of the 52,000 images in the Gujarati handwritten digit dataset, which was made from a scanned copy of the Gujarati handwritten digit script. the proposed method outperforms the current state-of-the-art classification accuracy of 99.17%, as demonstrated by extensive experimental results. In addition, the precision, recall, and F1 scores were calculated to assess the classification's effectiveness. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Computer vision

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6th international conference on Smart Grid and Internet of things, SGIoT 2022

6th International Conference on Smart Grid and Internet of T...

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6th international conference on Smart Grid and Internet of things, SGIoT 2022

ISBN: (纸本)9783031312748

the proceedings contain 33 papers. the special focus in this conference is on Smart Grid and Internet of things. the topics include: Research on Edge computing Offloading Based on Reinforcement Learning in Multi-user Scenarios;design of Malicious Code Detection System Based on Convolutional Neural network;comprehensive Task Priority Queue for Resource Allocation in Vehicle Edge computing network Based on Deep Reinforcement Learning;Applying the Shapley Value Method to Predict Mortality in Liver Cancer Based on Explainable AI;Face Emotion Expression Recognition Using DLIB Model and Convolutional Neural network Approach for Supporting Online Learning;Image Classification for Smoke and Flame Recognition Using CNN and Transfer Learning on Edge Device;Non-uniform Time Slice parallel Simulation Method Based on Offline Learning for IEEE 802.11ax;A Joint Optimization Method for Scheduling and Random Access Based on the Idea of Particle-Based Access in IEEE 802.11ax;a Two-Level Adaptive Resource Allocation Algorithm for Quality of Service Guarantee in Home WiFi networks;the Impact of Disposal Effects Generated by Internet of things Technology on Quantitative Investment;Joint Energy Detection and Transmission Power Adjustment for FIM Problem in High Density WLANs;An Uplink OFDMA Access Method for Low Latency in Next-Generation WLANs;edge Station throughput Enhancement Method Based on Energy Detection threshold and Transmission Power Joint Dynamic Adjustment;A Channel Reservation Mechanism in IEEE 802.11be for Multi-cell Scenarios;An Adaptive Beamtracking Method for the Next Generation mmWave WLAN;A Collision Aware Multi-link Operation for Next Generation WLAN;angular Position Estimation for Human-Following and Robot Navigation;social Risk Analysis of Smart Grid Based on Emerging Technologies in the Chinese Context: A Review Based on CiteSpace;Using HTC Vive to Design a Virtual Reality Simulation Environment on Radiography.

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Machine Learning for Job-flow Online Scheduling and Resources Allocation in Distributed computing 6

Machine Learning for Job-flow Online Scheduling and Resource...

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6th international conference on Information Technologies in Engineering Education, Inforino 2022

作者： Toporkov, Victor Yemelyanov, Dmitry Bulkhak, Artem National Research University MPEI Moscow Russia

ISBN: (纸本)9781665405775

In this work we study a promising approach for efficient online scheduling of job-flows in high performance and distributed parallel computing. the majority of job-flow optimization approaches, including backfilling and microscheduling, require apriori knowledge of a full job queue to make the optimization decisions. In a more general scenario when user jobs are submitted individually, the resources selection and allocation should be performed immediately in the online mode. In this work we consider a neural network prototype model trained to perform online optimization decisions based on a known optimal solution. For this purpose, we designed MLAK algorithm which implements 0-1 knapsack problem based on the apriori unknown utility function. In a dedicated simulation experiments with different utility functions MLAK provides resources selection efficiency comparable to a classical greedy algorithm. the interdisciplinary nature of the considered problem and its solution (including elements from the areas of optimization, scheduling algorithms, machine learning and simulation) makes it a good study example in technical disciplines. © 2022 IEEE.

关键词： Greedy algorithms Scheduling algorithms Computational modeling Scalability Prototypes Machine learning parallel processing

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A Binarized Systolic Array-Based Neuromorphic Architecture with High Efficiency 6

A Binarized Systolic Array-Based Neuromorphic Architecture w...

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6th international conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Hu, Shaogang Li, Tianchen Zuo, Yue Zhou, Pujun Ma, Ruichen Qiao, Guanchao State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350399615

Neuromorphic chips can run biologically inspired neural networks to process spatiotemporal information with greatly low power, but there is still room for improvement in terms of energy efficiency (i.e., computing power/power consumption). In this work, we are inspired by the Google TPU and propose a systolic array-based neuromorphic architecture to accelerate spiking neural networks with high efficiency. the inputs/weights are binarized and each core of the architecture can realize 4K integrate-and-fire (IF) neurons for parallel computing. through RTL-level simulation, synthesis, and FPGA implementation, the proposed architecture achieves 98.67% accuracy on the N-MNIST dataset, 0.4 TSOPS computing power, scaled power consumption of 60.96 mW, and scaled energy efficiency of 6.72 TSOPS/W. this work provides a proof of concept for the future development of efficient systolic array-based neuromorphic chips. © 2022 IEEE.

关键词： neuromorphic hardware spiking neural network systolic architecture

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A COMPARATIVE ANALYSIS AND STUDY OF A FAST parallel CNN BASED DEEPFAKE VIDEO DETECTION MODEL WIth FEATURE SELECTION (FPC-DFM) 6

A COMPARATIVE ANALYSIS AND STUDY OF A FAST PARALLEL CNN BASE...

引用

6th international conference on Advanced computing and Communication Technologies for High Performance Applications (ACCthPA)

作者： Das, Athirasree Sebastian, Linda St Josephs Coll Engn & Technol Dept Comp Sci & Engn Palai Kerala India Coll Engn Dept Comp Sci & Engn Kidangoor Kerala India

ISBN: (纸本)9798350398441

Deep learning is an efficient and practical method that has been widely applied in numerous fields. Videos created with swapped faces in a video, altered facial emotions, changed genders, fraudulent video content generation, and altered facial features are referred to as "DeepFake" videos. these videos are created utilizing deep learning technology called generative adversarial networks. Fake videos are used to stir up political agitation, commit acts of terrorism, and demand money. A fast parallel CNN-based deepfake video detection model with feature selection is the new model we presented in this project (FPC-DFM). In order to identify Deepfake videos, the FPC DFM architecture uses feature extraction, feature selection, and prediction. the FPC-DFM model extracted features using three convolutional models: EfficientNet, VGG16, and ResNet as well as Principal Component Analysis (PCA)-based feature selection and Support Vector Machine-based classification. We offer a new architecture for capturing the video frame features that will be utilized to determine if the video is real or fake by utilizing deep learning techniques. In comparison to other pre-trained models like VGG16-TL, EfficientNet-TL and Resnet50-TL and our results demonstrated that FPC-DFM has the best performance and the highest accuracy of 96.50%.

关键词： Deepfake video detection Convolutional neural network (CNN) Principal Component Analysis Support vector machine (SVM)

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ICIAI 2022 - 6th international conference on Innovation in Artificial Intelligence

ICIAI 2022 - 6th International Conference on Innovation in A...

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6th international conference on Innovation in Artificial Intelligence, ICIAI 2022

ISBN: (纸本)9781450395502

the proceedings contain 40 papers. the topics discussed include: object tracking algorithm for Siamese network combined with channel attention mechanism;effective community detection algorithm based on edge influence weight;accelerating federated learning based on grouping aggregation in heterogeneous edge computing;parallel physics-informed neural networks with bidirectional balance;mixture density hyperspherical generative adversarial networks;multi-modal intermediate fusion model for diagnosis prediction;named entity recognition of Zhuang language based on the feature of initial letter in word;transformer-based question text generation in the learning system;heterogeneous collaborative refining for real-time end-to-end image-text retrieval system;and ECA-CBAM: classification of diabetic retinopathy: classification of diabetic retinopathy by cross-combined attention mechanism.

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Edge computing Based Coordinated Energy-saving Train Regulation with Multi-agent Learning 6

Edge Computing Based Coordinated Energy-saving Train Regulat...

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2021 6th international conference on Information Science, Computer Technology and Transportation, ISCTT 2021

作者： Shang, Mengying Zhou, Yonghua School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9783800757282

Regenerative braking technology shows great potential in energy conservation and plays an increasingly important role in urban rail transit industry. In order to enhance the operation synergy among multiple trains to make full use of regenerative braking energy and reduce overall energy consumption, train schedule regulation needs to take driving strategy as a whole into account, which asks for higher requirements for the real-time adaptability of urban rail train control system. Based on the analysis of metro train big data environment, this paper puts forward an edge computing architecture combined with multi-agent actor-critic (EC-MAAC) model for real-time train schedule regulation considering energy saving. the deployed cloud server adopts multi-agent actor-critic network for parallel offline training. Each edge server downloads the multi-agent model trained in the cloud to the local for online traffic regulation. the experiments show the improvement of regenerative braking energy using among multiple trains through the real-time adjustment of inter station driving time, dwell time and traction-braking strategy. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Energy conservation

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Architecture of Train Onboard Trusted network System Based on Endogenous Security 6th

Architecture of Train Onboard Trusted Network System Based o...

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6th international conference on Electrical Engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Du, Jiewei Liang, Jianying Liu, Shaoqing Chang, Zhenchen Jia, Dongxiao Liu, Mingming State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Qingdao266111 China

ISBN: (纸本)9789819993147

On basis of studying passive defense technology, carried out research on active security technology to achieve the transformation from passive defense to active security, aiming at the information security problem of rail vehicle on-board network. Based on the design of autonomous security operating system and hardware system, a dual architecture of on-board trusted network system for rail vehicles based on endogenous security is proposed. this architecture is based on cryptographic technology and achieves a trusted and actively controlled computing platform at the source by constructing trusted roots of physical chips and using trusted software as the core, which having four major features: dual endogeneity, trustworthiness and robustness, active immunity, and fault tolerance and self-consistency. the on-board trusted network system of rail vehicles is designed, and its trusted transfer mechanism is analyzed and designed the on-board network system test experiment. By injecting "safety genes" into train network system, through genes control security and computation, and have the function of parallel operation of computation and protection, which can achieve active immunity. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Fault tolerance

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