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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10372 条记录，以下是431-440 订阅

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Comparative Analysis of Yokeless and Segmetned armature Axial Flux Permanent Magnet Motor According to Eccentricity Conditions 26

Comparative Analysis of Yokeless and Segmetned armature Axia...

引用

26th International conference on Electrical Machines and Systems, ICEMS 2023

作者： Ji, Tae-Hyuk Kim, Hyung-Woo Kim, Sung-Sik Park, Seah Kim, Yong-Jae Jung, Sang-Yong Sunkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Chosun University Department of Electrical Engineering Gwanju Korea Republic of

ISBN: (纸本)9798350317589

In this paper, the effects of eccentricity on a double rotor type axial flux permanent magnet motor (AFPMM) with a yokeless and segmented armature (YASA) are investigated. The eccentricity on AFPMMs could lead to adverse effect on the electromagnetic performance and undesirable vibration by unbalanced magnetic force (UMF). Therefore, analysis according to different kinds of eccentricity is necessary to identify and improve the impact. This paper covered three types of eccentricity on YASA double rotor type AFPMM. According to the eccentricity conditions, the electromagnetic performance and degree of UMF are analyzed using 3D-finite method analysis. © 2023 IEEE.

关键词： Permanent magnets

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Domestic waste image classification algorithm based on improved EfficientNetV2 8

Domestic waste image classification algorithm based on impro...

引用

8th International conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Lou, Li Gou, Nan Xi'an Shiyou University School of Computer Science Shaanxi Xi'an China

ISBN: (纸本)9798350302455

Aiming at the problem of low efficiency and poor real-time performance of domestic waste classification, this paper proposes a domestic waste classification model based on improved EfficientNetV2. The model was trained by introducing transfer learning, and ECA (Efficient Channel Attention) attention mechanism was introduced to enhance the feature extraction of the model. LeakyReLU activation function and Adam optimization algorithm were selected to improve the generalization ability of the model. Thus, a high-performance and lightweight garbage classification model ECAE-Net (Efficient Channel Attention EfficientNetV2) is obtained. Experimental results show that the accuracy of the proposed model reaches 96.8%, which is 1.3%higher than that of the initial model EfficientNetV2-S. At the same time, the number of parameters and floating point calculations are reduced by 57.3%and 52.3%respectively. The improved model has achieved excellent performance in the domestic waste classification task. © 2023 IEEE.

关键词： Image classification

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Dual Attention Entropy Model for Efficient Neural Image Compression 21

Dual Attention Entropy Model for Efficient Neural Image Comp...

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tianwen, Wu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611-731 China

ISBN: (纸本)9798331519254

In the field of neural image compression, it is essential to develop an efficient entropy model which predicts the probability distribution of the quantized latent representation. Previous CNN based or transformer based entropy models achieve suboptimal estimation because CNN based model cannot capture long-range dependencies, while transformer based model suffers from excessive memory and computational cost. To address this issue, we propose a novel Entropy model based on Dual Attention aggregation (EDA), which takes into account both global and local dependencies effectively and efficiently. Specifically, a Dual Attention Aggregation (DAA) block is proposed. Within the DAA block, Deformable Attention Transformer (DAT) is proposed to capture global dependencies efficiently by using sparse key-value pairs to filter out large amounts of irrelevant contexts, and then Neighborhood Attention Transformer (NAT) is used for learning the complementary local dependencies. Experiments demonstrate that our model (EDA) delivers the state-of-the-art rate-distortion performance while being time-efficient and memory-friendly. The source code can be accessed at https://***/TNTWEN/EDA. © 2024 IEEE.

关键词： Image compression

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Enabling Real World Scale Structural Superlubricity All-Atom Simulation on the Next-Generation Sunway Supercomputer 23

Enabling Real World Scale Structural Superlubricity All-Atom...

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2023 International conference for high performance Computing, Networking, Storage and Analysis, SC 2023

作者： Duan, Xiaohui Wang, Jin Gao, Ping Ma, Ming Gan, Lin Liu, Xin Fu, Haohuan Xue, Wei Chen, Dexun Yang, Guangwen Liu, Weiguo School of Software Shandong University China International School for Advanced Studies Trieste Italy Tsinghua University Department of Computer Science and Technology Beijing China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China National Supercomputing Center in Wuxi Wuxi China Ministry of Education Key Lab for Earth System Modeling Department of Earth System Science Tsinghua University Beijing China

ISBN: (纸本)9798400701092

Molecular dynamics (MD) simulation can provide an affordable way for inspecting microscopic phenomena, which is a powerful complement to real-world experiments. But the spatial scale of MD simulations is usually magnitudes smaller than experiment systems. In this paper, we present our work, redesigning the widely used inter-layer potential in structural superlubricity. By carrying out a specialized neighbor list for inter-layer potential computation, the total memory access amount is reduced significantly. Besides, a simple but efficient vectorization strategy is implemented based on the new neighbor list. In the extreme case, our work can scale to 38 million cores to achieve a sustainable performance of 61 PFLOPS, enabling a simulation of a superlubricity system of 32 μm2 with 7.2 billion atoms at 4.75 ns/day, which is 11,834 times of reported largest scale simulation in superlubricity systems in contact area and almost ten times faster in time-to-solution. Furthermore, we have done a simulation at 9 μm2 which results in consistency with real-world experiments and verified some theoretical predictions in the mesoscopic scale. © 2023 ACM.

关键词： Molecular dynamics

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Syntax Analysis of Protocol Fields Based on Graph Learning 21

Syntax Analysis of Protocol Fields Based on Graph Learning

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Xinglong, Chen Kegang, Hao Xuehang, Li Jiacheng, Gong Yukun, Zhu Erfan, Zhao Southwest China Research Institute of Electronic Equipment Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

In network trace-based protocol reverse engineering, multi-sequence alignment and sequence clustering algorithms are commonly used for message analysis. However, these approaches primarily focus on the characteristics and variation patterns of field values, making it challenging to conduct an in-depth semantic analysis of fields. Additionally, existing message analysis methods model messages solely through sequence models, neglecting the inter-field relationships resulting from the behaviors of both parties in message exchanges. To address these limitations, we propose a field semantic analysis method based on message interaction graphs. Specifically, we introduce a method for modeling field sequences using message interaction graphs, which leverage the graph's topological structure to represent correlations between fields induced by message interaction behaviors while preserving the inherent characteristics of field values through node features. Furthermore, we present GraphProtocol, a protocol field semantic analysis framework based on graph neural networks. The experiment results demonstrate that GraphProtocol achieves superior identification performance compared to traditional sequence-based approaches, highlighting its effectiveness in enhancing protocol security analysis and vulnerability detection. © 2024 IEEE.

关键词： Graph neural networks

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Exploration and Image Display of Fertilizer Input Based on Grey Prediction Algorithm and Decision Tree Classification 24

Exploration and Image Display of Fertilizer Input Based on G...

引用

4th International conference on Artificial Intelligence, Automation and high performance Computing, AIAHPC 2024

作者： Liu, Lidong Luo, Yongbin Zhang, Rong Chen, Zilin Feng, Jinghui Department of Computer Science Guangdong University of Science and Technology China

ISBN: (纸本)9798400710049

Prediction algorithms and classification algorithms, as important algorithms in the field of computer science, can automatically extract patterns and patterns from data to predict future events or classify unknown data, thereby improving decision-making efficiency and accuracy. This article uses the grey prediction algorithm to predict and analyze the nutrient input of agricultural fertilizers in Guangdong Province over the past 40 years, in order to determine the future use of organic fertilizers and chemical fertilizers in nitrogen, phosphorus, and potassium fertilization. To ensure the accuracy of the algorithm prediction, residual and relative error algorithm tests are conducted to determine the practicality of the algorithm. At the same time, using decision tree classification algorithm, analyze the correlation between organic fertilizer, chemical fertilizer, time, and simulate the decision classification process. The research results of this article can provide valuable references for decision-makers in the use of different fertilizers in the agricultural cultivation process. In order to facilitate rapid analysis of complex data for decision-makers, all results in this article are presented through computer-generated graphical illustrations. © 2024 Copyright held by the owner/author(s).

关键词： algorithm verification decision classification decision tree classification algorithm graphical display Grey prediction algorithm

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Latent Graph Neural Networks for Human Skeleton-based Action Recognition 2

Latent Graph Neural Networks for Human Skeleton-based Action...

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2nd IEEE International conference on Sensors, Electronics and computer Engineering, ICSECE 2024

作者： Wei, Xianlong Li, Ce Zhao, Kaipan Jiang, Zhongbo School of Artificial Intelligence China University of Mining & Technology Beijing Computer Science and Technology Xueyuan Rd Beijing Haidian District100089 China

ISBN: (纸本)9798350373646

The exploration of spatial-temporal graph neural networks has been extensive, yet it remains a challenge due to the intricate variations in spatiotemporal dependencies across skeleton joint flows. In response, we propose a novel guidance mechanism within the latent 3D spatial-temporal space, termed Latent Graph Neural Networks (LGNN). Our approach involves designing a long-term spatio-temporal model with a high k-value to capture action information effectively within the latent spatial-temporal domain, thereby representing the spatiotemporal dynamics of skeleton sequences. To enhance feature information between distant nodes and address redundant information, we introduce a pair distance normalization operation in the latent space. This operation facilitates the formation of LGNN, where two streams of base LGNN seamlessly merge into a single network, enabling end-to-end training. Notably, our method significantly improves performance compared to state-of-the-art approaches and enhances accuracy across three widely used and challenging datasets for action recognition. © 2024 IEEE.

关键词： Graph neural networks

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computer System for Modeling Fluid Flow Around Bodies and Its Potential in Industry 1

引用

7th International Russian Automation conference, RusAutoCon 2024

作者： Tsareva, P.E. St. Petersburg State Marine Technical University 3 Lotsmanskaya Str Saint-Petersburg190121 Russia

ISBN: (数字)9783031824944

ISBN: (纸本)9783031824937

Mathematical modeling plays an important role in industry when a physical model is unprofitable, cannot be implemented due to the complexity of the problem, or more accurate values need to be obtained. In particular, the pressing issue of physically plausible fluid modeling is not a new challenge for industry. Despite a lot of research in recent decades, this area remains relatively unexplored and many phenomena lack a general description. As a result, in the industry the main difficulty in fluid modeling lies in the lack of a universal approach and the low predictability of emerging phenomena such as turbulence. For most systems of differential equations, which become the basis of models, it is impossible to find solutions in a general form, so it is necessary to consider individual cases. The most common approaches use the Navier-Stokes equation, however, for three dimensions, solutions can only be found for special cases. Nevertheless, it is the most complete way to describe the behavior of a fluid, which takes into account viscosity and, if necessary, can be supplemented with equations to describe the phenomena of electro- and magnetohydrodynamics, as well as dynamic meteorology. Although high-precision models allow saving physical resources, they consume a large amount of computing resources, so it is advisable to carry out modeling on a high-performance computer or super computer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Navier Stokes equations

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PC-Yolo: Enhanced YOLOv5-Based Defect Detection System with Improved Partial Convolution for Ham Sausage Inspection 4

PC-Yolo: Enhanced YOLOv5-Based Defect Detection System with ...

引用

4th International conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Cai, Hexue Shangguan, Haonan Wang, Lulin Faculty of Robot Science and Engineering Northeastern University Liaoning Shenyang110167 China School of Computer Science and Engineering Northeastern University Liaoning Shenyang110167 China School of Metallurgy Northeastern University Liaoning Shenyang110819 China

ISBN: (纸本)9781510682528

Advances in automation for the packaging industry necessitate robust quality control measures, particularly in food appearance inspection. This paper introduces PC-Yolo, an advanced defect detection system based on an enhanced version of the YOLOv5 algorithm, tailored for the inspection of ham sausage products. PC-Yolo incorporates Partial Convolution (PConv) to improve the defect detection process, offering substantial gains in speed and accuracy over traditional methods. A custom dataset comprising high-resolution images of ham sausages, with and without defects, was used to train and validate the model. The proposed system efficiently handles variations in defect size through optimal prior box dimensionality and employs a combination of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) for effective feature fusion. The resultant PC-Yolo model demonstrates superior real-time detection capabilities, with robust performance in complex scenarios, thereby addressing the industry's zero-defect challenge. © 2024 SPIE.

关键词： Inspection

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An FPGA-based Key-Switching Accelerator with Ultra-high Throughput for FHE 24

An FPGA-based Key-Switching Accelerator with Ultra-High Thro...

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43rd International conference on computer-Aided Design, ICCAD 2024

作者： Lu, Zhaojun Xu, Peng Wang, Yijie Yang, Yifan Chen, Qidong Yu, Weizong Qu, Gang Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Wuhan JinYinHu Laboratory China University of Maryland College Park United States

ISBN: (纸本)9798400710773

Fully Homomorphic Encryption (FHE) enables computations directly on encrypted numbers, thereby preserving the privacy of sensitive information even in untrusted environments. However, the substantial computational overhead associated with homomorphic evaluations restricts the practical application of FHE schemes. To deal with the performance challenges, this paper proposes a hardware/software pipeline framework with a three-level cache architecture to accelerate the costly Key-Switching operation in FHE. This framework supports the dynamically reconfigurable processing mode, two parallelism strategies, and flexible control flow, effectively breaking the compute-bound and the memory-bound limitations. A no-stall and conflict-free memory mapping algorithm is implemented on the Xilinx U55C FPGA platform that boosts the throughput of ciphertext-ciphertext multiplication to 395 operations per second, which 1.4× and 2.5× speeds up the FPGA-based (HPCA’23) and GPU-based (HPCA’23) schemes with the same parameter set and precision. © 2024 Copyright is held by the owner/author(s).

关键词： Sensitive data

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