检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

6,705 篇 会议
116 册 图书
70 篇 期刊文献

馆藏范围

6,891 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,560 篇 工学
- 3,366 篇 计算机科学与技术...
- 1,736 篇 软件工程
- 1,054 篇 信息与通信工程
- 673 篇 电气工程
- 228 篇 控制科学与工程
- 179 篇 电子科学与技术（可...
- 143 篇 机械工程
- 97 篇 动力工程及工程热...
- 97 篇 生物工程
- 94 篇 测绘科学与技术
- 42 篇 生物医学工程（可授...
- 40 篇 仪器科学与技术
- 39 篇 光学工程
- 34 篇 建筑学
- 29 篇 土木工程
- 28 篇 网络空间安全
- 27 篇 安全科学与工程
771 篇 理学
- 568 篇 数学
- 112 篇 生物学
- 102 篇 统计学（可授理学、...
- 100 篇 物理学
- 75 篇 系统科学
- 37 篇 化学
511 篇 管理学
- 383 篇 管理科学与工程(可...
- 197 篇 工商管理
- 155 篇 图书情报与档案管...
82 篇 医学
- 80 篇 临床医学
- 25 篇 基础医学(可授医学...
42 篇 经济学
- 42 篇 应用经济学
35 篇 法学
- 25 篇 社会学
12 篇 农学
10 篇 文学
8 篇 教育学
8 篇 军事学
1 篇 艺术学

主题

898 篇 distributed comp...
802 篇 computer network...
513 篇 concurrent compu...
450 篇 wireless sensor ...
377 篇 parallel process...
354 篇 computer science
352 篇 routing
344 篇 neural networks
341 篇 computational mo...
304 篇 computer archite...
260 篇 application soft...
255 篇 network topology
253 篇 peer to peer com...
253 篇 protocols
244 篇 bandwidth
235 篇 grid computing
232 篇 software enginee...
189 篇 costs
177 篇 intelligent netw...
176 篇 artificial intel...

机构

28 篇 institute of com...
24 篇 shandong provinc...
20 篇 key laboratory o...
15 篇 shandong fundame...
14 篇 department of co...
13 篇 university of ch...
12 篇 department of co...
11 篇 department of co...
11 篇 department of co...
11 篇 institute of com...
11 篇 national laborat...
11 篇 beijing universi...
10 篇 college of compu...
10 篇 institute of inf...
10 篇 school of cyber ...
10 篇 department of co...
10 篇 school of comput...
9 篇 shandong enginee...
9 篇 department of co...
9 篇 school of comput...

作者

16 篇 jiannong cao
15 篇 m. takizawa
15 篇 li dongsheng
15 篇 shen hong
15 篇 hong shen
14 篇 shietung peng
14 篇 eyas el-qawasmeh
13 篇 lemin li
13 篇 rothermel kurt
12 篇 hai jin
11 篇 dongsheng li
11 篇 wei zhang
10 篇 koibuchi michihi...
10 篇 jack dongarra
10 篇 t. enokido
10 篇 wang yang
10 篇 stephen l. scott
10 篇 j. duato
10 篇 m. ould-khaoua
9 篇 duerr frank

语言

6,711 篇 英文
168 篇 其他
17 篇 中文

检索条件"任意字段=IASTED International Conference on Parallel and Distributed Computing and Networks"

共 6891 条记录，以下是431-440 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

An AI Model for Neurodegenerative Diseases

An AI Model for Neurodegenerative Diseases

引用

11th international conference of Computer Communication and Informatics (ICCCI)

作者： Mishra, Sarthak Bhargavi, K. Siddaganga Inst Technol Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781728158754

Models of neural networks are receiving widespread attention as potential new architectures for computing systems. The models considered here consist of highly interconnected networks of simple computing elements. A computation is performed collectively by the whole network with the activity distributed over all the computing elements. This collective operation results in a high degree of parallel computation and gives the network the potential to solve complex problems quickly. Furthermore, this computational model is meant to be burnt onto a neuromorphic chip and with the use of the surgical procedure, it can then be given to the healthcare department and can thus be used to cure the patients suffering from neurodegenerative diseases. The application software involved maps the patients with the concerned healthcare department for making this model feasible and practical to use.

关键词： Neural network model Synapse parallel computation Software Development Neuromorphic chips Surgical procedure Smart Devices Human brain

来源：评论

学校读者我要写书评

暂无评论

Detecting Design Patterns in UML Class Diagram Images using Deep Learning 24

Detecting Design Patterns in UML Class Diagram Images using ...

引用

24th IEEE/ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD 2022

作者： Zhang, Xun Washizaki, Hironori Yoshioka, Nobukazu Fukazawa, Yoshiaki Waseda University Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350310412

Detecting software design pattern is an important part of software reverse engineering because design patterns can provide the most intuitive design idea of software products, which can be useful for maintenance engineers. In past studies, a lot of approaches have been proposed to detect design patterns, and the machine learning-based approach is a new trend in recent years. In this paper, we propose a preliminary idea of a deep learning-based approach to detect design patterns from UML class diagrams of software products, which can be used in some cases that traditional approaches may not work. We propose an overall process, which is divided into preparation phase and application phase. In preparation phase, we train a deep learning-based classifier to do the image classification task. In application phase, users may input the UML class diagram of a micro-architecture into the model and get the pattern it belongs to. We conduct a preliminary experiment to show the effectiveness of our approach, we train a Convolutional Neural Network (CNN) as the classifier and test it on our image dataset, which is constructed with UML images we collected from the Internet. We also use Gradient-weighted Class Activation Mapping (Grad-CAM) to do the visualization and use it to explain why our approach works. Lastly, we analyze the potential advantages and disadvantages of our approach. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yizhou Wang, Yijie Xu, Hongzuo Zhou, Xiaohui National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Beijing100091 China

ISBN: (纸本)9798350368741

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors. © 2025 IEEE.

关键词： Local Temporal Pattern Self-supervised Learning Time Series Anomaly Detection

来源：评论

学校读者我要写书评

暂无评论

Fully Decentralized Data Distribution for Exascale-HPC: End of the Provider-Demander Matching Puzzle

Fully Decentralized Data Distribution for Exascale-HPC: End ...

引用

IEEE international conference on Cluster computing

作者： Mingtian Shao Wenzhe Zhang Ruibo Wang Huijun Wu Yiqin Dai Kai Lu National University of Defense Technology China

ISBN: (数字)9798350358711

ISBN: (纸本)9798350358728

For many years, in the HPC data distribution scenario, as the scale of the HPC system continues to increase, manufacturers have to increase the number of data providers to improve the IO parallelism to match the data demanders. In the era of Exascale computing, this mode of decoupling the demander and provider has limited scalability and huge costs. In our view, only a distribution model in which the demander also acts as the provider can fundamentally cope with changes in scale and have the best scalability, which is called all-to-all data distribution mode in this paper. We design and implement the BitTorrent protocol on computing networks in HPC systems and propose FD3, a fully decentralized data distribution method. We design the Requested-to-Validated Table (RVT) and the Nearest and Longest consecutive piece Segment First (NLSF) policy based on the features of the HPC networking environment to improve the performance of FD3. Experimental results show that FD3 can scale smoothly to 11k+ computing nodes, and its performance is much better than that of the parallel file system. Compared with the original BitTorrent, the performance is improved by 7–11 times. FD3 shows the great potential of the all-to-all model in HPC data distribution scenarios. At the same time, the work of this paper can further stimulate the exploration of future distributed parallel file systems and provide a foundation and inspiration for the design of data access patterns for Exscale HPC systems.

关键词： Knowledge engineering Protocols File systems Exascale computing Computational modeling Scalability Design methodology distributed databases parallel processing Data models

来源：评论

学校读者我要写书评

暂无评论

Quantum Circuit Switching with One-Way Repeaters in Star networks

Quantum Circuit Switching with One-Way Repeaters in Star Net...

引用

Quantum computing and Engineering (QCE), IEEE international conference on

作者： Álvaro G. Iñesta Hyeongrak Choi Dirk Englund Stephanie Wehner QuTech Delft University of Technology Delft The Netherlands Quantum Computer Science EEMCS Delft University of Technology Delft The Netherlands Kavli Institute of Nanoscience Delft University of Technology Delft The Netherlands Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Distributing quantum states reliably among distant locations is a key challenge in the field of quantum networks. One-way quantum networks address this by using one-way communication and quantum error correction. Here, we analyze quantum circuit switching as a protocol to distribute quantum states in one-way quantum networks. In quantum circuit switching, pairs of users can request the delivery of multiple quantum states from one user to the other. After waiting for approval from the network, the states can be distributed either sequentially, forwarding one at a time along a path of quantum repeaters, or in parallel, sending batches of quantum states from repeater to repeater. Since repeaters can only forward a finite number of quantum states at a time, a pivotal question arises: is it advantageous to send them sequentially (allowing for multiple requests simultaneously) or in parallel (reducing processing time but handling only one request at a time)? We compare both approaches in a quantum network with a star topology. Using tools from queuing theory, we show that requests are met at a higher rate when packets are distributed in parallel, although sequential distribution can generally provide service to a larger number of users simultaneously. We also show that using a large number of quantum repeaters to combat channel losses limits the maximum distance between users, as each repeater introduces additional processing delays. These findings provide insight into the design of protocols for distributing quantum states in one-way quantum networks.

关键词： Protocols Network topology Stars Quantum state Repeaters Topology Quantum circuit Quantum repeaters Queueing analysis Switching circuits

来源：评论

学校读者我要写书评

暂无评论

Exploring the StyleGANEX for Morphing Faces with Indian Ethnicity

Exploring the StyleGANEX for Morphing Faces with Indian Ethn...

引用

international conference on parallel distributed and Grid computing (PDGC)

作者： Sanat Walia Neetu Singh Ayush Shetty Richa Gupta Abdul Manaf F Rahul Prasad Computer Science and Engineering Jaypee Institute of Information Technology Noida India Artificial Intelligence Bobble AI Gurugram India Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This research explores an approach to face morphing using StyleGANEX, a cutting-edge Generative Adversarial Network (GAN). By projecting source and target images into StyleGANEX’s latent space and interpolating their latent vectors, we achieve a seamless transition between faces, emphasizing realism and visual fidelity. The pre-processing stage integrates facial landmark detection for precise image cropping, ensuring optimal facial feature alignment. The resultant morphed images effectively blended characteristics from both source and target, demonstrating potential applications in entertainment and advanced image manipulation techniques. In this analysis, we specifically blended Indian faces to generate morphing in Indian ethnicity. We observed that StyleGANEX generates high-quality visual results on Indian faces when compared with StyleGAN due to the optimization in latent spaces. Further, StyleGANEX is faster, requires less time for the same number of optimization steps in morphing.

关键词： Visualization Interpolation Ethnicity Entertainment industry Generative adversarial networks Grid computing Vectors Faces Optimization Facial features

来源：评论

学校读者我要写书评

暂无评论

Plant Diseases Detection Optimization with Deep Learning and Data Augmentation

Plant Diseases Detection Optimization with Deep Learning and...

引用

international conference on parallel distributed and Grid computing (PDGC)

作者： Kanika Vashist Richa Gupta Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Plant disease detection is crucial for food security and sustainable agriculture. This study focuses on Tomato plants with five classes: Bacterial Spot, Yellow Leaf Curl Virus, Septoria Leaf Spot, Tomato Healthy and Late Blight. Utilizing Convolutional Neural Network (CNN) architectures including simple CNN with dropouts and regularization, CNN with Data Augmentation (DA), and CNN with Autoencoder or Convolutional Autoencoder (CAE), trade-offs between Accuracy, F1 score, Precision and Recall are explored. While the CNN achieves 98.12% accuracy, DA boosts it to 98.75%, albeit with increased training time. The hybrid CNN & CAE model strikes a balance at 97.08% accuracy. Extending the evaluation to Potato plants, the CNN & DA model achieves 90% accuracy. These findings demonstrate model adaptability across different crops and disease classes.

关键词： Training Plant diseases Adaptation models Accuracy Computational modeling Autoencoders Computer architecture Predictive models Data augmentation Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Sparse Matrix Reordering Method Selection with parallel computing and Deep Learning

Sparse Matrix Reordering Method Selection with Parallel Comp...

引用

international Joint conference on Neural networks (IJCNN)

作者： Rui Xia Jihu Guo Huajian Zhang Shun Yang Qinglin Wang Jie Liu College of Computer Science and Techonology National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Sparse matrix reordering is an important step in Cholesky decomposition. By reordering the rows and columns of the matrix, the time of computation and storage cost can be greatly reduced. With the proposal of various reordering algorithms, the selection of suitable reordering methods for various matrices has become an important research topic. In this paper, we propose a method to predict the optimal reordering method by visualizing sparse matrices in chunks in a parallel manner and feeding them into a deep convolutional neural network. The results show that the theoretical performance can reach 95% of the optimal performance, the prediction accuracy of the method can reach up to 85%, the parallel framework achieves an average speedup ratio of 11.35 times over the serial framework, and the performance is greatly improved compared with the traversal selection method on large sparse matrices.

关键词： Deep learning Visualization Accuracy Costs Neural networks parallel processing Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Dynamic DNN Decomposition for Lossless Synergistic Inference 41

Dynamic DNN Decomposition for Lossless Synergistic Inference

引用

IEEE 41st international conference on distributed computing Systems (ICDCS)

作者： Zhang, Beibei Xiang, Tian Zhang, Hongxuan Li, Te Zhu, Shigiang Gu, Jianjun Zhejiang Lab Intelligent Robot Res Ctr Hangzhou Peoples R China

ISBN: (纸本)9781665449328

Deep neural networks (DNNs) sustain high performance in today's data processing applications. DNN inference is resource-intensive thus is difficult to fit into a mobile device. To solve this problem, recent advances unleash DNN services using the edge computing paradigm. The existing approaches split a DNN into two parts and deploy the two partitions to computation nodes at two edge computing tiers. Nonetheless, these methods overlook collaborative device-edge-cloud computation resources. Besides, previous algorithms demand the whole DNN re-partitioning to adapt to computation resource changes and network dynamics. Moreover, for resource-demanding convolutional layers, prior works do not give a parallel processing strategy without loss of accuracy at the edge side. To tackle these issues, we propose D-3, a dynamic DNN decomposition system for synergistic inference without precision loss. The proposed system introduces a heuristic algorithm named horizontal partition algorithm to split a DNN into three parts. The algorithm partially adjust the partitions at run time according to processing time and network conditions. At the edge side, a vertical separation module separates feature maps into tiles that can be independently run on different edge nodes in parallel. Extensive quantitative evaluation of five popular DNNs illustrates that D-3 outperforms the stateof-the-art counterparts up to 3.4X in end-to-end DNN inference time and reduces backbone network communication overhead up to 3.68X.

关键词： distributed computing Edge computing DNN inference acceleration

来源：评论

学校读者我要写书评

暂无评论

Optimizing Transformer Training Based on Computation and Accessing Memory Features with Deep Learning Processor

Optimizing Transformer Training Based on Computation and Acc...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Zhou Lei Qin Fang Jingfeng Qian Shengbo Chen Qingguo Xu Ninghua Yang School of Computer Engineering and Science Shanghai University Shanghai China Ningbo Zhongke Intelligent Computing Technology Co. Ltd Zhejiang China

The Transformer model, which has significantly advanced natural language processing and computer vision, overcomes the limitations of recurrent neural networks and convolutional neural networks. However, it faces challenges with computational efficiency and memory management due to complex computations and variable-length inputs. Despite research efforts, these issues persist. This paper presents a novel optimization of the Transformer model, following an indepth analysis of its computational graph structure. Firstly, we utilize Deep computing Unit (DCU) as our hardware platform. Secondly, we optimize element-wise and reduction operators through operator fusion and rewriting. Thirdly, we develop a fine-grained memory management algorithm using a greedy strategy. As a result, the training speed of the Transformer model increases by 1.2x - 1.4x without compromising accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 40 41 42 43 44 45 46 47 48 49 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：