检索结果-内蒙古大学图书馆

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

作者： Kang, Seung-Gu Park, Seah Seo, Chae Won Jung, Sang-Yong SungKyunKwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350317589

This paper suggests complex parameter mapping method of motor inductance for quality operation. The proposed method considers the rotor deformation. The rotor deformations are analyzed in terms of mechanical stress, thermal expansion, and coupled conditions in sequence. And the effects of deformation are also described about air gap length and inductance variations. Then parameter mapping method including rotating speed and temperature are proposed, and the each map data and the differences are analyzed. Finally, the importance of considering rotor deformation for quality operation is verified. © 2023 IEEE.

关键词： Thermal expansion

来源：评论

学校读者我要写书评

暂无评论

A multi-day and high-quality EEG dataset for motor imagery brain-computer interface

引用

SCIENTIFIC DATA 2025年第1期12卷 1-13页

作者： Yang, Banghua Rong, Fenqi Xie, Yunlong Li, Du Zhang, Jiayang Li, Fu Shi, Guangming Gao, Xiaorong Shanghai Univ Res Ctr Brain Comp Engn Sch Mechatron Engn & Automation Shanghai Peoples R China Minist Educ Engn Res Ctr Tradit Chinese Med Intelligent Rehabi Shanghai Peoples R China Tianjin Univ Med Sch Tianjin Peoples R China Xidian Univ Sch Artificial Intelligence Xian Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing Peoples R China

A key challenge in developing a robust electroencephalography (EEG)-based brain-computer interface (BCI) is obtaining reliable classification performance across multiple days. In particular, EEG-based motor imagery (MI) BCI faces large variability and low signal-to-noise ratio. To address these issues, collecting a large and reliable dataset is critical for learning of cross-session and cross-subject patterns while mitigating EEG signals inherent instability. In this study, we obtained a comprehensive MI dataset from the 2019 World Robot conference Contest-BCI Robot Contest. We collected EEG data from 62 healthy participants across three recording sessions. This experiment includes two paradigms: (1) two-class tasks: left and right hand-grasping, (2) three-class tasks: left and right hand-grasping, and foot-hooking. The dataset comprises raw data, and preprocessed data. For the two-class data, an average classification accuracy of 85.32% was achieved using EEGNet, while the three-class data achieved an accuracy of 76.90% using deepConvNet. Different researchers can reuse the dataset according to their needs. We hope that this dataset will significantly advance MI-BCI research, particularly in addressing cross-session and cross-subject challenges.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SpDISTAL: Compiling Distributed Sparse Tensor Computations

SpDISTAL: Compiling Distributed Sparse Tensor Computations

引用

International conference for high performance Computing, Networking, Storage and Analysis (HPC)

作者： Yadav, Rohan Aiken, Alex Kjolstad, Fredrik Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665454445

We introduce SpDISTAL, a compiler for sparse tensor algebra that targets distributed systems. SpDISTAL combines separate descriptions of tensor algebra expressions, sparse data structures, data distribution, and computation distribution. Thus, it enables distributed execution of sparse tensor algebra expressions with a wide variety of sparse data structures and data distributions. SpDISTAL is implemented as a C++ library that targets a distributed task-based runtime system and can generate code for nodes with both multi-core CPUs and multiple GPUs. SpDISTAL generates distributed code that achieves performance competitive with hand-written distributed functions for specific sparse tensor algebra expressions and that outperforms general interpretation-based systems by one to two orders of magnitude. © 2022 IEEE.

关键词： computer Science programming Parallel programming

来源：评论

学校读者我要写书评

暂无评论

Learning Frequency Adaptation for Cross-domain Medical Image Segmentation

Learning Frequency Adaptation for Cross-domain Medical Image...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Xiaoming Liu, Lei Su, Xiangdong Yang, Yuhan Gao, Guanglai Inner Mongolia University National Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian College of Computer Science Hohhot China

ISBN: (纸本)9798350386226

In medical image segmentation, some recent methods improve domain adaptation performance through frequency domain adaptation and frequency mixup. However, these approaches have two limitations: (1) frequency domain adaptation ignores the adverse effects of high-frequency noise on model generalization, and (2) frequency mixup confuses semantic information. To address these issues, we propose a novel frequency adaptation approach for medical image segmentation including low-frequency component alignment (LFCA) and random amplitude cutmix (RAC). Since low-frequency contains major image information and high-frequency noise affects adaptation, we leverage discrete wavelet transform to decompose images into low and high-frequency components. LFCA aligns the domain distribution of low frequencies and high frequencies are passed to the decoder via skip connections. In addition, we design RAC to generate diverse augmented samples through amplitude cutmix while avoiding distortion of the original distributions. Experiments on benchmark datasets validate the efficacy of our proposed approach. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cerebral Microbleed Segmentation with Pretrained UNETR++

Enhancing Cerebral Microbleed Segmentation with Pretrained U...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kwon, Junmo Seo, Sang Won Park, Hyunjin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Institute for Basic Science Center for Neuroscience Imaging Research Suwon16419 Korea Republic of Sungkyunkwan University School of Medicine Department of Neurology Seoul06351 Korea Republic of

ISBN: (纸本)9798350386226

Accurate segmentation of cerebral microbleeds (CMBs) is important for diagnosing small vessel diseases, yet it presents significant challenges due to their tiny size and especially the high risk of false positives (e.g., calcifications and blood flow in pial vessels) in magnetic resonance imaging (MRI). While existing studies have employed multi-stage deep neural networks to reduce false positives, these approaches often rely heavily on the performance of the false positive reduction module. To address this and move towards an end-to-end learning scheme, we propose a novel approach that incorporates self-supervised learning through masked image modeling to enrich both encoder and decoder features in UNETR++ using gradient recalled echo T2*-weighted MRI. Noting that existing pre-training strategies often focus on the encoder while neglecting the decoder, our approach pre-trains both components by introducing segmentation and reconstruction heads. We evaluated our model on an in-house dataset and external validation set, demonstrating superior performance compared to the state-of-the-art nnUNet and nnDetection. Additionally, ablation studies revealed that pre-training both the encoder and decoder subsequently benefits the overall performance of our framework. Our code is available at https://***/junmokwon/UNETRppCMBSeg. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

FPGA-based Implementation of A Dynamic Hand Gesture Recognition System

FPGA-based Implementation of A Dynamic Hand Gesture Recognit...

引用

2023 IET International conference on Engineering Technologies and Applications, ICETA 2023

作者： Tsai, Yi-Chen Lai, Yu-Hsuan Xu, Chong-Hao Ruan, Shanq-Jang Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781839539848

This paper presents a low-power dynamic hand gesture recognition system based on an FPGA platform. The system consists of two main components: hand tracking and gesture recognition. A concatenation of image processing methods is proposed for hand tracking to achieve accurate tracking with minimal computational resources. Gesture recognition is performed using a Convolutional Neural Network (CNN) model trained on the NIST dataset. The implementation is done on the PYNQ-Z2 board, utilizing its Python packages to efficiently program image processing algorithms. The power consumption of the system is measured at 2.15 W, which is lower than similar FPGA-based implementations. This research demonstrates the feasibility of achieving low power consumption in hand gesture recognition systems while maintaining high accuracy and performance. © The Institution of Engineering & Technology 2023.

关键词： computer software

来源：评论

学校读者我要写书评

暂无评论

Orinoco: Ordered Issue and Unordered Commit with Non-Collapsible Queues 23

Orinoco: Ordered Issue and Unordered Commit with Non-Collaps...

引用

50th Annual International Symposium on computer Architecture (ISCA)

作者： Chen, Dibei Zhang, Tairan Huang, Yi Zhu, Jianfeng Liu, Yang Gou, Pengfei Feng, Chunyang Li, Binghua Wei, Shaojun Liu, Leibo Tsinghua Univ Beijing Peoples R China Innovat Inst High Performance Server Beijing Peoples R China HEXIN Technol Xiamen Peoples R China

ISBN: (纸本)9798400700958

Modern out-of-order processors call for more aggressive scheduling techniques such as priority scheduling and out-of-order commit to make use of increasing core resources. Since these approaches prioritize the issue or commit of certain instructions, they face the conundrum of providing the capacity efficiency of scheduling structures while preserving the ideal ordering of instructions. Traditional collapsible queues are too expensive for today's processors, while state-of-the-art queue designs compromise with the pseudo-ordering of instructions, leading to performance degradation as well as other limitations. In this paper, we present Orinoco, a microarchitecture/circuit co-design that supports ordered issue and unordered commit with non-collapsible queues. We decouple the temporal ordering of instructions from their queue positions by introducing an age matrix with the bit count encoding, along with a commit dependency matrix and a memory disambiguation matrix to determine instructions to prioritize issue or commit. We leverage the Processingin-Memory (PIM) approach and efficiently implement the matrix schedulers as 8T SRAM arrays. Orinoco achieves an average IPC improvement of 14.8% over the baseline in-order commit core with the state-of-the-art scheduler while incurring overhead equivalent to a few kilobytes of SRAM.

关键词： microarchitecture out-of-order execution instruction scheduling out-of-order commit processing-in-memory

来源：评论

学校读者我要写书评

暂无评论

performance Analysis of a 3D Elliptic Solver on Intel Xeon computer System 18

Performance Analysis of a 3D Elliptic Solver on Intel Xeon C...

引用

18th conference on computer Science and Intelligence Systems, FedCSIS 2023

作者： Lirkov, Ivan Paprzycki, Marcin Ganzha, Maria Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev bl. 25A Sofia1113 Bulgaria Systems Research Institute Polish Academy of Sciences Ul. Newelska 6 Warsaw01-447 Poland

ISBN: (纸本)9788396744784

It was shown that block-circulant preconditioners, applied to a conjugate gradient method, used to solve structured sparse linear systems, arising from 2D or 3D elliptic problems, have very good numerical properties and a potential for good parallel efficiency. In this contribution, hybrid parallelization based on MPI and OpenMP standards is experimentally investigated. Specifically, the aim of this work is to analyze parallel performance of the implemented algorithms on a supercomputer consisting of Intel Xeon processors and Intel Xeon Phi coprocessors. While obtained results confirm the positive outlook of the proposed approach, important open issues are also identified. © 2023 Polish Information Processing Society.

关键词： Conjugate gradient method

来源：评论

学校读者我要写书评

暂无评论

Gamma-enhanced Spatial Attention Network for Efficient high Dynamic Range Imaging

Gamma-enhanced Spatial Attention Network for Efficient High ...

引用

IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Li, Fangya Gang, Ruipeng Li, Chenghua Li, Jinjing Ma, Sai Liu, Chenming Cao, Yizhen State Key Lab Media Convergence & Commun CUC Beijing 100024 Peoples R China NRTA Acad Broadcasting Sci Beijing 100866 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

high dynamic range(HDR) imaging is the task of recovering HDR image from one or multiple input Low Dynamic Range (LDR) images. In this paper, we present Gamma-enhanced Spatial Attention Network(GSANet), a novel framework for reconstructing HDR images. This problem comprises two intractable challenges of how to tackle overexposed and underexposed regions and how to overcome the paradox of performance and complexity trade-off. To address the former, after applying gamma correction on the LDR images, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range images for fusion. For the latter one, we propose an efficient channel attention module, which only involves a handful of parameters while bringing clear performance gain. Experimental results show that the proposed method achieves better visual quality on the HDR dataset. The code will be available at: https://***/fancyicookie/GSANet

关键词： Visualization Codes conferences Pipelines Imaging Dynamic range performance gain

来源：评论

学校读者我要写书评

暂无评论

Efficient thread-to-core mapping alternatives for application-level redundant multithreading

Efficient thread-to-core mapping alternatives for applicatio...

引用

作者： Arslan, Sanem Ünsal, Osman Computer Engineering Department Marmara University Istanbul Turkey Computer Science - Computer Architecture For Parallel Paradigms Barcelona Supercomputing Center Barcelona Spain

Redundant multithreading (RMT) is an effective thread-level replication method to improve the reliability requirements of applications. Although it significantly improves the robustness of applications, it comes with additional performance overhead since the redundant threads might share the same core resources. In our previous study [Efficient selective replication of critical code regions for SDC mitigation leveraging redundant multithreading. J Supercomput 2021;77:14130-14160], we presented an efficient software-level RMT approach, where we execute the most critical code regions with three threads to correct errors. In this study, we focus on further improving the performance of our software-level RMT method by presenting a set of different thread-to-core mapping alternatives. We provide different static mapping methods, which require preliminary information about the applications, such as execution time, instruction-per-cycle (IPC), or cache usage patterns, and a set of dynamic mapping methods, which map threads to cores dynamically at runtime without requiring any additional information. The dynamic mapping methods decide which threads are mapped to which cores at each scheduling point based on the IPC, cache miss rate, or cache access values of each thread as well as each core. Experimental results show that the dynamic mapping method, which maps threads to cores based on IPC values, outperforms all other static and dynamic methods. It also outperforms our baseline model, where the operating system handles the thread-to-core mappings by 8%, 7%, and 20% based on average speedup, harmonic speedup, and mean slowdown metrics. © 2023 John Wiley & Sons, Ltd.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：