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

2,959 篇 会议
39 篇 期刊文献
7 册 图书

馆藏范围

3,005 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,760 篇 工学
- 1,407 篇 计算机科学与技术...
- 684 篇 软件工程
- 490 篇 电气工程
- 397 篇 信息与通信工程
- 203 篇 控制科学与工程
- 130 篇 生物工程
- 79 篇 电子科学与技术（可...
- 65 篇 生物医学工程（可授...
- 59 篇 机械工程
- 54 篇 仪器科学与技术
- 49 篇 光学工程
- 34 篇 网络空间安全
- 30 篇 动力工程及工程热...
- 24 篇 材料科学与工程（可...
- 24 篇 安全科学与工程
- 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
517 篇 理学
- 226 篇 物理学
- 190 篇 数学
- 143 篇 生物学
- 58 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
168 篇 artificial neura...
165 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
50 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

2,925 篇 英文
72 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 3005 条记录，以下是1061-1070 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：

Sleep Apnea detecting based on ECG and EEG signals using Discrete Wavelet Transform and Artificial neural network

Sleep Apnea detecting based on ECG and EEG signals using Dis...

引用

The 3rd International conference on distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： A. A. Hammal T. Bonney W. A. Nassan K. Obaideen Electrical and Computer Engineering Aleppo University Syria Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Sustainable Energy and Power Systems Research Centre RISE University of Sharjah Sharjah United Arab Emirates

Sleep apnea is a severe sleep disorder that happens when breathing stops and starts during slumber. If it goes untreated, it can cause serious health problems. In recent years, Sleep Apnea (SA) has been diagnosed using either Electrocardiogram (ECG) signals that refer the heart health or Electroencephalogram (EEG) signals that refer to brain health. This research aims to build an Artificial neural network (ANN) classification model to determine sleep disorders using ECG and EEG signals. Features Delta sub-band waves are extracted and processed using a multilayer perceptron (MLP) with two different topologies; hidden layers are the same. The results show that using ECG and EEG signals to classify sleep apnea achieves high accuracy of 99.97% compared to other recent related work. We anticipate our assay to be a starting point for reliable wearable diagnostic devices to control equipment that can help patients with sleep apnea avoid respiratory distress episodes.

关键词： discrete wavelet transforms diseases electrocardiography electroencephalography medical disorders medical signal processing multilayer perceptrons pneumodynamics signal classification sleep

来源：评论

学校读者我要写书评

暂无评论

Modern Methods of Data Preprocessing to Increase the Accuracy of AGV Battery Discharge Forecast

Modern Methods of Data Preprocessing to Increase the Accurac...

引用

International Computer Sciences and Information Technologies (CSIT)

作者： Olena Pavliuk Mykola Medykovskyy Rafal Cupek Myroslav Mishchuk Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland ACS Department Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

To minimize its idle time automated guided vehicle (AGV) is an important task of predicting its battery discharge. This is important for increasing the efficiency of resource use and ensuring business continuity in production environments where people work intensively. To improve the quality of AGV battery discharge data, modern processing methods are used: data cleaning, normalization, dimensionality reduction, interpolation, extrapolation, noise filtering and anomaly detection. Various methods were investigated to supplement the missing instantaneous energy consumption data of the AGV using the Formica 1 example. Data processing methods were applied to improve the prediction accuracy, including simple approaches such as averaging and filtering, as well as more complex methods such as Kalman and Wiener filters. In addition, machine learning algorithms such as RNNs and autoencoders have been used to recover lost data. The results showed that neural network-based methods are the most accurate for complex nonlinear data, while simpler methods have the advantage of speed and ease of use. The MSE values highlight that RNNs have the highest accuracy (0.468), while methods such as Wiener filter (0.501) and autoencoders (0.504) trade speed for slightly lower accuracy. The Kalman filter (0.494) provides good balance for linear systems, especially where noise suppression is key.

关键词： Linear systems Accuracy Wiener filters Filtering Autoencoders Data preprocessing Voltage Discharges (electric) Batteries Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Hardware-aware Partitioning of Convolutional neural network Inference for Embedded AI Applications

Hardware-aware Partitioning of Convolutional Neural Network ...

引用

International conference on distributed Computing in Sensor Systems (DCOSS)

作者： Fabian Kreß Julian Hoefer Tim Hotfilter Iris Walter Vladimir Sidorenko Tanja Harbaum Jürgen Becker Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Embedded image processing applications like multicamera-based object detection or semantic segmentation are often based on Convolutional neural networks (CNNs) to provide precise and reliable results. The deployment of CNNs in embedded systems, however, imposes additional constraints such as latency restrictions and limited energy consumption in the sensor platform. These requirements have to be considered during hardware/software co-design of embedded Artifical Intelligence (AI) applications. In addition, the transmission of uncompressed image data from the sensor to a central edge node requires large bandwidth on the link, which must also be taken into account during the design ***, we present a simulation toolchain for fast evaluation of hardware-aware CNN partitioning for embedded AI applications. This approach explores an efficient workload distribution between sensor nodes and a central edge node. Neither processing all layers close to the sensor nor transmitting all uncompressed raw data to the edge node is an optimal solution for each use case. Hence, our proposed simulation toolchain evaluates power and performance metrics for each reasonable partitioning point in a CNN. In contrast to the state of the art, our approach does not only consider the neural network architecture. In the evaluation, our simulation toolchain additionally takes into account hardware components such as special accelerators and memories that are implemented in the sensor ***, we show the simulation results for three commonly used CNNs in embedded systems. Thereby, we identify advantageous partitioning points regarding inference latency and energy consumption. With the support of the toolchain, we are able to identify three beneficial partitioning points for FCN ResNet-50 and two for GoogLeNet as well as for SqueezeNet V1.1.

关键词： Measurement Energy consumption Embedded systems Computational modeling Simulation neural networks Hardware

来源：评论

学校读者我要写书评

暂无评论

A Deep Residual network-Based Approach to Reidentify Persons via Face Features Extraction from Video Frames

A Deep Residual Network-Based Approach to Reidentify Persons...

引用

Blockchain and distributed Systems Security (ICBDS), IEEE International conference on

作者： Kedar Nagnathrao Ghogale Ravindra Sadashivrao Apare Ravindra H. Borhade Department of Computer Engineering Sinhagad Technical Education Society’s Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University Pune Maharashtra India Department of Information Technology KJEI's Trinity College of Engineering and Research Pune Savitribai Phule Pune University Pune Maharashtra India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

The rise of unwanted situations in today's environment has made video security more important. Video surveillance helps with anomaly identification and improves security in a number of areas, such as businesses, educational institutions, and city monitoring systems. The task of recognizing and re-identifying moving subjects, particularly those captured by many cameras, are extremely difficult and are made more problematic by things like low-quality video, inconsistent illumination, and congested spaces. A model database, a feature descriptor, and a classifier are three essential elements of digital image processing that are used in the process of identifying people in video. This document aims to give a general summary of the procedures utilized in video footage-based person re-identification (Re-ID) The arena of person Re-ID study includes a range of approaches and techniques. These methods include the identification of faces through feature recognition, the creation of detailed profiles through multiple component matching representations, and the use of multiple component dissimilarity representations to capture the subtleties of a person's appearance. Using deep residual networks is one noteworthy method that has shown to be remarkably effective in extracting face characteristics. After these traits are recovered, they may be used to compare and match people's faces between various video frames and camera angles. In addition, an innovative deep learning-based technique for person Re-ID using face traits is presented in the research. Furthermore, the research examines the main obstacles related to human Re-ID in video surveillance and considers possible ways to overcome these obstacles.

关键词： Face recognition Urban areas Lighting Video surveillance Feature extraction Cameras Security Faces Monitoring Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Removal of Gaussian distributed Noise in Images with Deep neural network Models

Removal of Gaussian Distributed Noise in Images with Deep Ne...

引用

IEEE Signal processing and Communications Applications (SIU)

作者： Zeki Kuş Musa Aydin Bilgisayar M&#x00FC hendisli&#x011F i Fatih Sultan Mehmet Vak&#x0131 f &#x00DC niversitesi &#x0130 stanbul T&#x00FC rkiye

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

The removal of noise caused by environmental factors in microscopic imaging studies has become an important research topic in the field of medical imaging. In the medical imaging stage made with any digital microscopy method (Confocal, Fluorescence, etc.), undesirable noises are added to the image obtained due to factors stemming from excessive or low illumination, high or low temperature, or electronic circuit equipment. The most basic noise model formed due to these environmental factors mentioned is the Gaussian normal distribution or a characteristic function close to this distribution. It is widely known that spatial filters (mean, median, Gaussian smoothing) are applied to eliminate Gaussian noise in digital image processing. However, undesirable results may occur in the images obtained when spatial filters are used to remove the noise in the images. In particular, because high frequencies are suppressed in images where spatial filters are applied, details are lost in the final image, and a blurred image is obtained. For this reason, four different convolutional neural network-based models are used for noise removal and to improve the PSNR values in this study. As a result, the modified U-Net improved the PSNR values for different noise levels as follows: +6.23, +7.88 and +10.52 dB

关键词： Temperature Smoothing methods Noise reduction neural networks Lighting Signal processing Spatial filters

来源：评论

学校读者我要写书评

暂无评论

Query-driven video event processing for the internet multimedia things 47th

Query-driven video event processing for the internet multime...

引用

47th International conference on Very Large Data Bases, VLDB 2021

作者： Yadav, Piyush Salwala, Dhaval Pontes, Felipe Arruda Dhingra, Praneet Curry, Edward Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland

Advances in Deep neural network (DNN) techniques have revolutionized video analytics and unlocked the potential for querying and mining video event patterns. This paper details GNOSIS, an event processing platform to perform near-real-time video event detection in a distributed setting. GNOSIS follows a serverless approach where its component acts as independent microservices and can be deployed at multiple nodes. GNOSIS uses a declarative query-driven approach where users can write customize queries for spatiotemporal video event reasoning. The system converts the incoming video streams into a continuous evolving graph stream using machine learning (ML) and DNN models pipeline and applies graph matching for video event pattern detection. GNOSIS can perform both stateful and stateless video event matching. To improve Quality of Service (QoS), recent work in GNOSIS incorporates optimization techniques like adaptive scheduling, energy efficiency, and content-driven windows. This paper demonstrates the Occupational Health and Safety query use cases to show the GNOSIS efficacy. © The authors.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

HF-Mid: A Hybrid Framework of network Intrusion Detection for Multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

引用

IEEE International conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep neural network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Poster Abstract: Model Average-based distributed Training for Sparse Deep neural networks

Poster Abstract: Model Average-based Distributed Training fo...

引用

39th IEEE International conference on Computer Communications (IEEE INFOCOM)

作者： Yang, Yuetong Lai, Zhiquan Cai, Lei Li, Dongsheng Natl Univ Def Technol Sch Comp Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186955

distributed training of large-scale deep neural networks(DNNs) is a challenging work for it's time costing and complicated communication. Existing works have achieved scalable performance on GPU clusters for dense DNNs in the computer vision area. However, little progress has been made on the distributed training of sparse DNNs which is commonly used in the area of natural language processing (NLP). In this poster, we introduce SA-HMA, a sparsity -aware hybrid training method for sparse deep models. SA-HMA combines Model Average (MA) and synchronous optimization methods together, expecting to reduce the communication cost for spare model training. The experimental results show that SA-HMA achieves 133x speedup over the state-of-the-art work.

关键词： Training Natural language processing Convergence Throughput Computational modeling neural networks Machine learning

来源：评论

学校读者我要写书评

暂无评论

Minibatch vs Local SGD for Heterogeneous distributed Learning 34

Minibatch vs Local SGD for Heterogeneous Distributed Learnin...

引用

34th conference on neural Information processing Systems (NeurIPS)

作者： Woodworth, Blake Patel, Kumar Kshitij Srebro, Nathan Toyota Technol Inst Chicago Chicago IL 60637 USA

ISBN: (纸本)9781713829546

We analyze Local SGD (aka parallel or federated SGD) and Minibatch SGD in the heterogeneous distributed setting, where each machine has access to stochastic gradient estimates for a different, machine-specific, convex objective;the goal is to optimize w.r.t. the average objective;and machines can only communicate intermittently. We argue that, (i) Minibatch SGD (even without acceleration) dominates all existing analysis of Local SGD in this setting, (ii) accelerated Minibatch SGD is optimal when the heterogeneity is high, and (iii) present the first upper bound for Local SGD that improves over Minibatch SGD in a non-homogeneous regime.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Continuous, Real-Time Object Detection on Mobile Devices without Offloading 40

Continuous, Real-Time Object Detection on Mobile Devices wit...

引用

40th IEEE International conference on distributed Computing Systems (ICDCS)

作者： Liu, Miaomiao Ding, Xianzhong Du, Wan Univ Calif Merced Dept Comp Sci & Engn Merced CA 95343 USA

ISBN: (纸本)9781728170022

This paper presents AdaVP, a continuous and real-time video processing system for mobile devices without offloading. AdaVP uses Deep neural network (DNN) based tools like YOLOv3 for object detection. Since DNN computation is time-consuming, multiple frames may be captured by the camera during the processing of one frame. To support real-time video processing, we develop a mobile parallel detection and tracking (MPDT) pipeline that executes object detection and tracking in parallel. When the object detector is processing a new frame, a light-weight object tracker is used to track the objects in the accumulated frames. As the tracking accuracy decreases gradually, due to the accumulation of tracking error and the appearance of new objects, new object detection results are used to calibrate the tracking accuracy periodically. In addition, a large DNN model produces high accuracy, but requires long processing latency, resulting in a great degradation for tracking accuracy. Based on our experiments, we find that the tracking accuracy degradation is also related to the variation of video content, e.g., for a dynamically changing video, the tracking accuracy degrades fast. A model adaptation algorithm is thus developed to adapt the DNN models according to the change rate of video content. We implement AdaVP on Jetson TX2 and conduct a variety of experiments on a large video dataset. The experiment results reveal that AdaVP improves the accuracy of the state-of-the-art solution by up to 43.9%.

关键词： Deep neural networks

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301页 << < 103 104 105 106 107 108 109 110 111 112 > >>

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：