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

2,952 篇 会议
39 篇 期刊文献
7 册 图书

馆藏范围

2,998 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,753 篇 工学
- 1,402 篇 计算机科学与技术...
- 683 篇 软件工程
- 488 篇 电气工程
- 394 篇 信息与通信工程
- 200 篇 控制科学与工程
- 130 篇 生物工程
- 78 篇 电子科学与技术（可...
- 65 篇 生物医学工程（可授...
- 58 篇 机械工程
- 54 篇 仪器科学与技术
- 48 篇 光学工程
- 33 篇 网络空间安全
- 30 篇 动力工程及工程热...
- 24 篇 安全科学与工程
- 23 篇 材料科学与工程（可...
- 22 篇 化学工程与技术
- 20 篇 测绘科学与技术
- 19 篇 石油与天然气工程
515 篇 理学
- 225 篇 物理学
- 188 篇 数学
- 143 篇 生物学
- 57 篇 统计学（可授理学、...
- 40 篇 系统科学
- 27 篇 化学
203 篇 管理学
- 135 篇 管理科学与工程(可...
- 81 篇 图书情报与档案管...
- 31 篇 工商管理
135 篇 医学
- 112 篇 临床医学
- 38 篇 基础医学(可授医学...
37 篇 法学
- 20 篇 社会学
18 篇 文学
14 篇 农学
10 篇 教育学
9 篇 经济学
2 篇 军事学

主题

590 篇 neural networks
173 篇 training
167 篇 artificial neura...
164 篇 deep learning
135 篇 computational mo...
124 篇 feature extracti...
115 篇 neural network
114 篇 convolutional ne...
88 篇 machine learning
87 篇 neurons
86 篇 parallel process...
79 篇 deep neural netw...
74 篇 computer archite...
73 篇 distributed comp...
65 篇 signal processin...
62 篇 distributed proc...
62 篇 accuracy
60 篇 recurrent neural...
55 篇 data models
49 篇 convolutional ne...

机构

11 篇 institute of inf...
9 篇 school of cyber ...
8 篇 univ chinese aca...
8 篇 natl univ def te...
7 篇 college of compu...
7 篇 carnegie mellon ...
7 篇 national laborat...
7 篇 beijing universi...
7 篇 natl univ def te...
6 篇 shanghai jiao to...
6 篇 the islamic univ...
6 篇 department of co...
5 篇 science and tech...
5 篇 ibm tj watson re...
5 篇 school of inform...
5 篇 king saud univer...
5 篇 shandong normal ...
5 篇 kaust thuwal
5 篇 university of ch...
5 篇 natl univ def te...

作者

8 篇 li dongsheng
8 篇 dou yong
8 篇 jie liu
6 篇 richtarik peter
6 篇 ribeiro alejandr...
6 篇 liu jie
6 篇 niu xin
6 篇 yong dou
6 篇 shulian yang
6 篇 zhang wei
5 篇 ryabinin max
5 篇 segarra santiago
5 篇 lai zhiquan
5 篇 ding bo
5 篇 qiao peng
5 篇 xu jiren
5 篇 kokkinos yiannis
5 篇 peng yuxing
5 篇 wang huaimin
5 篇 li aiping

语言

2,943 篇 英文
47 篇 其他
8 篇 中文
2 篇 土耳其文

检索条件"任意字段=Conference on Neural Network and Distributed Processing"

共 2998 条记录，以下是551-560 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 Adaptive Fuzzy-Based Clustering Model for Healthcare Wireless Sensor networks 1st

An Adaptive Fuzzy-Based Clustering Model for Healthcare Wire...

引用

1st international conference Ambient Intelligence in Health Care (ICAIHC)

作者： Chithaluru, Premkumar Jena, Lambodar Singh, Debabrata Teja, K. M. V. Ravi Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522302 Andhra Pradesh India SOA Univ ITER Dept CA Bhubaneswar Odisha India Koneru Lakshmaiah Educ Fdn Dept Mech Engn Vaddeswaram 522302 Andhra Pradesh India

ISBN: (纸本)9789811960703;9789811960680;9789811960673

Healthcare wireless sensor networks (WSNs) have made their way into a wide range of applications and technologies with significantly different requirements and features in recent years. The integration of sensor nodes into intelligent sensing, information processing and information exchange infrastructures form healthcare WSNs will have a significant impact on a variety of applications, including telemedicine, habitat monitoring, structure health monitoring, human-centric applications and medical applications, among others. Monitoring, identification of events and responding to an event which requires a continuous access to real-time information either partial or fully distributed environment is a challenging issue. In order to overcome the challenges of healthcare wireless sensor networks (H-WSN), fuzzy-based clustering provides a cost-effective and efficient solution. Most of the problems in WSN are real time based that require fast computation, real-time optimal solution and need to be adaptive to the situation of the events and data traffic to achieve desired goals. Hence, neural networks and fuzzy sets would form appropriate candidates for implementing most of the computations involved in the issues of resource management in sensor networks. A real-time event detection is simulated and implemented on Crossbow mote (sensor node) using TinyOS.

关键词： Fuzzy H-WSN Habitat monitoring Human-centric applications neural network Structure health monitoring Telemedicine

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on Low-rate DDoS Attacks Detection Based on Deep Learning

A Comprehensive Survey on Low-rate DDoS Attacks Detection Ba...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 International conference on

作者： Zengguang Liu Xiaochun Yin Deyong Liu Information Engineering College Shandong Vocational College of Science and Technology Weifang China Computer Science and Engineering College Weifang University of Science & Technology Shouguang China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Low-rate distributed Denial-of-Service attacks, abbreviated as LDDoS, are experiencing an explosive and continuous growth in recent years. Meanwhile, people worked hard for making great contributions to prevent LDDoS attacks by deep learning methods. However, the existing surveys don't give detailed summaries on all aspects of neural network for LDDoS attacks. This paper investigates the trend and the challenges of LDDoS attacks. And then, it classifies the neural network methods into DL-based cluster, RL-based cluster, FL-based cluster and GNN-based cluster. And it presents the comprehensive survey along with state-of-the-art detection solutions for each of them. At last, the future research directions about developing an LDDoS detection system with broad-spectrum capabilities, cost-effective mechanisms and robustness against adversarial attacks are given.

关键词： Surveys Deep learning Fuses Computational modeling neural networks Information processing NSL-KDD Market research Feature extraction Robustness

来源：评论

学校读者我要写书评

暂无评论

Transformer Fault Diagnosis Model Based on FI-CNN Method

Transformer Fault Diagnosis Model Based on FI-CNN Method

引用

International conference on Intelligent Traffic Systems and Smart City (ITSSC)

作者： Lin, Nan Guo, Zhengwei State Grid Sichuan Elect Power Co Chengdu Peoples R China Sichuan Univ Coll Elect Engn Chengdu Peoples R China

ISBN: (数字)9781510652071

ISBN: (纸本)9781510652071;9781510652064

At present, the three-ratio method and artificial intelligence algorithm are widely used in transformer fault diagnosis. However, the three-ratio method based on DGA has problems such as lack of coding and insufficient anti-interference ability of various artificial intelligence methods, resulting in a high rate of misdiagnosis. This paper proposes a transformer fault diagnosis model based on FI (Feature Image)-CNN. It takes the percentage of dissolved gas in the oil as an input parameter, constructs a feature vector, and extracts the two most widely distributed data as two-dimensional coordinates based on the characteristics of the fault. Axis, and then uses the RGB principle to construct a three-layer neuron structure with the remaining gas data to form a "RGB dynamic map" of the training sample;build a convolutional neural network model, combine the faulty DGA data, and use the DNN full-link network for fitting, and finally Realize the fault diagnosis of the power transformer. Finally, it is compared with the diagnostic performance of various optimization algorithms. The results show that the FI-CNN method can obtain high-dimensional curves that divide the fault categories according to the "RGB dynamic map" of the training samples and the corresponding spatial locations, and has higher diagnostic accuracy.

关键词： transformer fault diagnosis convolutional neural network deep learning image processing

来源：评论

学校读者我要写书评

暂无评论

Data Analysis in Wireless Sensor networks with distributed Self Organizing Map

Data Analysis in Wireless Sensor Networks with Distributed S...

引用

Advances in Signal processing, Power, Communication, and Computing (ASPCC), IEEE International conference on

作者： Anita Panwar Satyasai Jagannath Nanda Dept. of Electronics & Communication Engineering Malaviya National Institute of Technology Jaipur Rajasthan-302017 India

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

distributed clustering algorithms are employed in wireless sensor network (WSN) to improve the local data analysis. This process is carried out collaboratively with the help of nearby neighbours without a central controller. In this paper, distributed clustering is performed with Self Organizing Map (SOM). The SOM is a popular unsupervised neural network model that maps input data to a lower-dimensional grid. On this grid map similar input patterns are placed closer to each other. This process helps in discovering patterns and relationships in the data without prior labeling, thus making proposed distributed Self Organizing Map (DSOM) useful for unknown local data analysis at the WSNs. The proposed algorithm is applied to analyze two real life WSN datasets: Water quality monitoring of Thames river, Weather monitoring dataset of various stations at Canada. Comparative analysis is carried out with distributed Particle Swarm optimization algorithm and distributed K-means algorithm. The proposed DSOM has superior performance, as indicated by Silhouette Index and Quantization Error measurements.

关键词： Self-organizing feature maps Wireless sensor networks Data analysis Quantization (signal) Signal processing algorithms Clustering algorithms Water quality Rivers Indexes Monitoring

来源：评论

学校读者我要写书评

暂无评论

N-MlpE: Optimizing Multilayer Perceptron network-based Knowledge Graph Embedding Model with Neighborhood Information 29

N-MlpE: Optimizing Multilayer Perceptron Network-based Knowl...

引用

29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Xu, Qing Ren, Xiaoli Ren, Kaijun Lin, Jiarun Li, Xiaoyong National University of Defense Technology College of Meteorology and Oceanography Changsha China

ISBN: (纸本)9798350330717

As an effective knowledge organizing and modeling technique, knowledge graph has become a key topic in graph research, but the practical application of KG is limited by its incompleteness. In recent years, many knowledge graph embedding(KGE) methods for knowledge graph completion(KGC) based on graph neural networks(GNN) have been proposed. However, most GNN-based KGC models are still suffer from the encoder-decoder structure of low efficiency in aggregating neighborhood information and the difficulty of model training. This paper present an optimized model that incorporates Neighborhood information into knowledge inference, to improve the performance of KGC models based on multilayer perceptron network(MLP), which is named N-MlpE. We generate an input sequence that includes the query triplet and its neighbor entities and relationships, and then feed it to an adaptive filter module to remove useless neighbors for the inference to improve the accuracy of the inference, and reduce the computational complexity of training the model. The filtered sequence is then fed into a weight calculation module and a feature extraction module simultaneously, the former is designed based on selfattention to model the relevant rule inference, which enhances the interpretability of KGE models, and the latter is based on MLP and used to capture the long-distance interactions between triplets, which can significantly improve the accuracy of inference. Extensive experiments are conducted on two standard KG datasets WN18RR and FB15k237 to verify the effectiveness of N-MlpE, the results show that the accuracy of N-MlpE model outperforms most GNN-based models. © 2023 IEEE.

关键词： distributed representation inference knowledge graph completion knowledge graph embedding link prediction

来源：评论

学校读者我要写书评

暂无评论

Research on Feature Extraction Algorithm of Graph Data Based on Graph neural network

Research on Feature Extraction Algorithm of Graph Data Based...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International conference on

作者： Shuang Qiu Hairong Hao Zhou Dong School of Information Engineering Eurasia University Xi'an China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

This paper proposes an optimization model that integrates Hadoop, Graph Convolutional networks (GCN), and Ant Colony Optimization (ACO) to enhance GNN performance in large-scale networks. Hadoop is utilized for distributed storage and processing of large graph datasets, dividing them into subgraphs across multiple computing nodes. The MapReduce framework accelerates the training and inference of GCN through parallel computing. GCN is employed to extract node features by performing convolution on the graph's adjacency matrix. ACO is then applied for global hyperparameter tuning and network structure optimization. By exploring the parameter space, ACO identifies the optimal configuration for GCN. This model leverages Hadoop's distributed computing power, GCN's effective feature extraction, and ACO's optimization capabilities. Experimental results demonstrate improved efficiency and performance on large-scale network datasets compared to traditional methods.

关键词： Training Graph convolutional networks Convolution Optimization models Computational modeling Parallel processing Feature extraction distributed computing Optimization Tuning

来源：评论

学校读者我要写书评

暂无评论

Attention Approximates Sparse distributed Memory 35

Attention Approximates Sparse Distributed Memory

引用

35th conference on neural Information processing Systems (NeurIPS)

作者： Bricken, Trenton Pehlevan, Cengiz Harvard Univ Syst Synthet & Quantitat Biol Cambridge MA 02138 USA Harvard Univ Appl Math Cambridge MA 02138 USA

ISBN: (纸本)9781713845393

While Attention has come to be an important mechanism in deep learning, there remains limited intuition for why it works so well. Here, we show that Transformer Attention can be closely related under certain data conditions to Kanerva's Sparse distributed Memory (SDM), a biologically plausible associative memory model. We confirm that these conditions are satisfied in pre-trained GPT2 Transformer models. We discuss the implications of the Attention-SDM map and provide new computational and biological interpretations of Attention.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Neighbor Selecting Cooperation Strategies for distributed Learning

Neighbor Selecting Cooperation Strategies for Distributed Le...

引用

European Signal processing conference (EUSIPCO)

作者： Evangelos Georgatos Christos Mavrokefalidis Kostas Berberidis Computer Engineering and Informatics Department University of Patras Greece

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In distributed learning, a network of agents co-operate for solving a common task, like training a particular neural network. The devices usually adopt an iterative procedure with two steps, namely, they, first, perform local optimization, using, e.g., stochastic gradient descent, and, then, they exchange information among them in order to achieve consensus on the final solution. In current literature, the proposed distributed algorithms achieve consensus using averaging rules over the received information at each agent. Here, the paper departs from this paradigm and focuses on “single agent” cooperation strategies in which each agent selects a particular neighbor at each iteration and uses only that information during the local optimization step. Three selection rules are designed and it is shown that they inherit the convergence properties of commonly used averaging rules. Moreover, their effectiveness is demonstrated experimentally in classification tasks over other algorithms using well-known datasets for a wide range of scenarios, capturing factors like non-IID datasets, network size, and AI model size.

关键词： Training Performance evaluation Computer aided instruction Distance learning Signal processing algorithms Stochastic processes Signal processing Classification algorithms Partitioning algorithms Optimization

来源：评论

学校读者我要写书评

暂无评论

Positional Differential Encoding for distributed Learning

Positional Differential Encoding for Distributed Learning

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Leah Woldemariam Anna Scaglione Department of Electrical and Computer Engineering Cornell University New York NY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A growing amount of available data and computational power makes training neural networks over a network of devices, and distribution optimization in general, more realizable. As a consequence, efficient communication becomes more important and can often be the bottleneck of such algorithms. Traditional data compression techniques for these scenarios consider the correlation of the data values over time: although the transmitted data itself may not be sparse, the changes within the data are increasingly sparse as the algorithm converges. In this work, we propose an encoding scheme for discrete symbols that adapts a source encoder to consider the positional correlations through time. We provide a bound on the bit rate yielded by our scheme and verify our results through experiments.

关键词： Training Computer aided instruction Correlation Distance learning Bit rate neural networks Signal processing algorithms Symbols Encoding Speech processing

来源：评论

学校读者我要写书评

暂无评论

Congestion-Aware distributed Task Offloading in Wireless Multi-Hop networks Using Graph neural networks

Congestion-Aware Distributed Task Offloading in Wireless Mul...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhongyuan Zhao Jake Perazzone Gunjan Verma Santiago Segarra Rice University USA US Army’s DEVCOM Army Research Laboratory USA

Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. Existing offloading schemes mainly focus on mobile devices and servers, while ignoring the potential network congestion caused by tasks from multiple mobile devices, especially in wireless multi-hop networks. To fill this gap, we propose a low-overhead, congestion-aware distributed task offloading scheme by augmenting a distributed greedy framework with graph-based machine learning. In simulated wireless multi-hop networks with 20-110 nodes and a resource allocation scheme based on shortest path routing and contention-based link scheduling, our approach is demonstrated to be effective in reducing congestion or unstable queues under the context-agnostic baseline, while improving the execution latency over local computing.

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300页 << < 52 53 54 55 56 57 58 59 60 61 > >>

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：