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检索条件"任意字段=IFIP International Conference on Network and Parallel Computing"

共 17252 条记录，以下是81-90 订阅

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STB-GraCapsNet: A Novel Capsule network Structure with Swin Transformer Block 25th

STB-GraCapsNet: A Novel Capsule Network Structure with Swin...

引用

25th international conference on parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Chunying Dong, Ziao Wang, Liya Liu, Lu Ren, Jing Ma, Jiang Liu, Bin College of Science North China University of Science and Technology 21 Bohai Road Caofeidian Xincheng Hebei Tangshan063210 China Big data and Social Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang0500198 China

ISBN: (纸本)9789819642069

Capsule network is a new type of neural network encoding features into capsules and constructing the part-whole relationships, which demonstrated good performance in image classification. However, it has some issues such as weak feature extraction ability and large number of training parameters, leading to performance decline when processing images with complex background. To address these issues, this article proposes a novel capsule network structure called STB-GraCapsNet, with key improvements as follows: 1) It uses Swin Transformer Block with the shift window attention mechanism for data preprocessing to obtain richer features;2) the features are then processed through Linear Embedding and passed into the Primary Capsule Layer to generate high-dimension feature capsules, which reduces primary capsules number and improves feature extraction ability of the network. Ablation and comparative experiments based on multiple datasets show that STB-GraCapsNet performs better on complex datasets: 77.8% and 36.2% reduction in parameters number of capsule structure, and 12.07% and 5.22% improvement in model accuracy, as compared to CapsNet and GraCapsNet, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615278

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for parallel Processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A Neural network-Based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness Data Poisoning Attacks in Federated Learning.

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615476

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615414

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615506

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615247

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PTS-NET: A parallel Dual-Stage network for Raw Low-Light Image Enhancement and Denoising 3rd

PTS-NET: A Parallel Dual-Stage Network for Raw Low-Light Ima...

引用

3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Li, Feihong Gao, Wenguang Zhang, Juan Shanghai University of Engineering Science Shanghai China Zhejiang Huadong Engineering Consulting Co. Ltd. Hangzhou China

ISBN: (纸本)9789819624676

Imaging in low-light environments has strong noise and low contrast, which can easily be exacerbated during image enhancement. RAW images have advantages over SRGB in low-light enhancement due to their linearity and other advantages, but they have the problem of image misalignment in the mapping to SRGB, and image features are lost with the model stream. In order to overcome these problems, we propose a supervised learning approach, which is a parallel two-stage network PTS-NET with RAW supervision and SRGB supervision. In the color restoration stage, we design a conversion module (FGM) that can extract high-frequency low-frequency feature information to achieve a smooth conversion from RAW to sRGB. Extensive experiments on three benchmark datasets demonstrate that our method not only achieves state-of-the-art performance but also enhances the quality of low-light images in practical applications, such as nighttime object recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image denoising

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615445

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Optimal Transport Flow Distribution and Construction of Wardrop Optimal networks 4th

Optimal Transport Flow Distribution and Construction of Wa...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Bagdasaryan, Armen Kalampakas, Antonios Saburov, Mansoor College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Institute of Mathematics National Academy of Sciences of Armenia 24/5 Baghramyan ave. Yerevan0019 Armenia

ISBN: (纸本)9783031812439

This paper is motivated by the problem of optimal flow allocation in transportation networks. We consider equilibrium and optimal flow distribution on networks of parallel links. In a parallel network, a user (or Wardrop) equilibrium is the optimal distribution of flow across alternative parallel links that minimize the effective costs of the links, while the system optimum is the optimal distribution of flow for which the average effective cost is minimal. We study Wardrop optimal networks, i.e., the networks that admit Wardrop optimal flows which satisfy both the Wardrop equilibrium and the system optimum. We first present the characterization of Wardrop optimal flows, and then propose a matrix approach for constructing Wardrop optimal networks by means of convex, strictly increasing, and continuously differentiable functions. Some special classes of Wardrop optimal networks are also introduced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Construction of Wardrop optimal networks Optimal flow distribution Price of anarchy Transport network Wardrop optimal flow Wardrop optimal network

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A Hybrid parallelism Framework of SPH for the Applications in Automobile Gearbox 30th

A Hybrid Parallelism Framework of SPH for the Applications i...

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30th international conference on Computational and Experimental Engineering and Sciences

作者： Zhang, Xiang Sun, Peng-Nan Xu, Yang Sun Yat Sen Univ Sch Ocean Engn & Technol Zhuhai 519082 Peoples R China

ISBN: (纸本)9783031774911;9783031774898;9783031774881

The rotating machineries of automobile, such as gears and motors, are involved with complicated interactions between fluids and structures, resulting in flow phenomenon such as free-surface, moving boundary, thermal conduction etc. Smoothed Particle Hydrodynamics (SPH), due to its Lagrangian nature, is preferred to simulate such phenomenon. The complexities of automobile structures require small particle distancing and therefore large number of particles are needed to discretize both fluid and structure. The small time step of SPH simulation is also required due to intense flowsplashing resulting from high-speedmoving boundary. Both two points above lead to large amount of computation duringSPHsimulation. In this paper, a parallelism framework of weakly compressible SPH(WCSPH) is proposed to accelerate SPH simulation by high-performance computing cluster. A hybrid parallelism strategy, with both Message Passing Interface (MPI) and Intel Threading Building Blocks (TBB), is used to reduce the total number of processes and therefore reduce the latency due to communication among computing clusters network. METIS is used to decompose the computational domain enabling dynamic domain decomposition and load balancing. The oil motion inside a gearbox is successfully simulated using the proposed framework, showing that the proposed parallelism framework is applicable to complex industry application and can accelerate SPH simulation efficiently.

关键词： SPH parallel computing fluid-structure interaction gearbox

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