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

1,327 篇 会议
11 篇 期刊文献
9 册 图书

馆藏范围

1,347 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

915 篇 工学
- 757 篇 计算机科学与技术...
- 498 篇 软件工程
- 303 篇 信息与通信工程
- 204 篇 电气工程
- 175 篇 生物工程
- 127 篇 生物医学工程（可授...
- 107 篇 光学工程
- 90 篇 控制科学与工程
- 80 篇 电子科学与技术（可...
- 58 篇 仪器科学与技术
- 48 篇 机械工程
- 35 篇 化学工程与技术
- 28 篇 安全科学与工程
- 26 篇 交通运输工程
425 篇 理学
- 189 篇 生物学
- 187 篇 数学
- 151 篇 物理学
- 76 篇 统计学（可授理学、...
- 54 篇 系统科学
- 40 篇 化学
169 篇 管理学
- 106 篇 图书情报与档案管...
- 72 篇 管理科学与工程(可...
- 40 篇 工商管理
146 篇 医学
- 130 篇 临床医学
- 88 篇 基础医学(可授医学...
- 65 篇 药学(可授医学、理...
- 24 篇 公共卫生与预防医...
28 篇 教育学
- 28 篇 教育学
27 篇 法学
- 21 篇 社会学
18 篇 经济学
- 17 篇 应用经济学
16 篇 农学
5 篇 军事学
1 篇 文学
1 篇 历史学

主题

210 篇 machine learning
134 篇 deep learning
78 篇 signal processin...
71 篇 accuracy
70 篇 feature extracti...
68 篇 signal processin...
61 篇 support vector m...
56 篇 machine learning...
45 篇 predictive model...
33 篇 neural networks
32 篇 convolutional ne...
31 篇 random forests
29 篇 computational mo...
29 篇 training
26 篇 natural language...
25 篇 prediction algor...
21 篇 technological in...
20 篇 real-time system...
19 篇 adversarial mach...
18 篇 image processing

机构

16 篇 chitkara centre ...
14 篇 centre of resear...
12 篇 shenyang ligong ...
6 篇 chitkara univers...
6 篇 ai & ds vishwaka...
6 篇 school of inform...
5 篇 department of me...
5 篇 khulna universit...
5 篇 department of co...
4 篇 delhi technologi...
4 篇 heilongjiang uni...
4 篇 department of s&...
4 篇 beijing univ pos...
4 篇 it vishwakarma i...
4 篇 chitkara univers...
4 篇 sri lanka instit...
4 篇 advanced telecom...
4 篇 shenyang ligong ...
4 篇 graduate school ...
3 篇 karunya institut...

作者

6 篇 mehta shiva
5 篇 rasmita panigrah...
4 篇 neelamadhab padh...
4 篇 wang xin
3 篇 m. vigenesh
3 篇 baydeti nagaraju
3 篇 sinambela marzuk...
3 篇 santosh kumar sh...
3 篇 ganapati panda
3 篇 debendra muduli
3 篇 abujar sheikh
3 篇 a. sivanantham
3 篇 li yajie
3 篇 li aihua
3 篇 zhou feng
3 篇 islam md. sanzid...
2 篇 aira kharvel par...
2 篇 nokshangthemba
2 篇 maheswara rao v ...
2 篇 lee tzong-ru

语言

1,187 篇 英文
151 篇 其他
11 篇 中文

检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1347 条记录，以下是721-730 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：

Twin Robot Cooperation in Multi-Robot Environment: An Applied Q-learning 2

Twin Robot Cooperation in Multi-Robot Environment: An Applie...

引用

2nd international conference on signal processing and Communication, ICSPC 2019

作者： Sahu, Bandita Das, P.K. Kabat, M.R. VSSUT Department of Computer Science and Engineering Burla Odisha India VSSUT Department of Information and Technoogy Burla Odisha India

ISBN: (纸本)9781728118499

This paper provides a new approach of executing the twin robot operation with the application of classical Q-learning and improved Q-learning algorithm. This approach has significantly less space and time requirement for execution of the algorithm As only the best Q-value with matched actions are stored for each state, the space requirement is reduced. Similarly, only the best matched state-action pairs with best Q-value are executed, it requires less amount of time for execution. On application of the classical and improved Q-0learning in twin robot operation, the proposed algorithm takes the supremacy with respect to the space requirement and the traversal time. © 2019 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Frequency-Hopping signal Sorting Based on Deep learning 2

Frequency-Hopping Signal Sorting Based on Deep Learning

引用

IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Qu, Yi Pei, Yuhao Song, Depeng Engn Univ PAP Coll Informat Engn Xian Peoples R China

ISBN: (纸本)9781538692981

In order to solve the problem that the sorting threshold of traditional frequency-hopping signal needs to be manually adjusted, Faster-RCNN and clustering algorithm is proposed. In this paper, the Faster-RCNN is firstly used to identify and locate all frequency-hopping points in the time-frequency spectrum diagram, and then AlexNet is used to obtain the number of frequency-hopping signal. Experimental results show that the Faster-RCNN can be effectively used for automatic signal sorting when the number of frequencyhopping signal is small.

关键词： Faster-RCNN AlexNet clustering algorithm Time-frequency diagram Frequency hopping signal

来源：评论

学校读者我要写书评

暂无评论

Automatic Recognition of Radar signal Types Based on Convolutional Neural Network 2

Automatic Recognition of Radar Signal Types Based on Convolu...

引用

2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Ruan, Guoqing Wu, Wei CETC Res Inst 28 Sci & Technol Informat Syst Engn Lab Nanjing 210007 Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In the field of cognitive electronic warfare, automatic feature learning and recognition of radar signal is an important technology to ensure intelligence reconnaissance. This paper analyses the basic structure of convolutional neural network (CNN) and proposes an automatic recognition algorithm for radar signal. Firstly, the radar signal is transformed into time-frequency image, and the principal component information of the image is extracted by image processing method. Then, the designed network CNN-LeNet-5 is used to realize self-learning and recognition of features. The simulation results show that the algorithm can effectively identify eight kinds of radar signals in low signal-to-noise ratio.

关键词： Cognitive electronic warfare Time-frequency analysis Convolutional neural network Automatic recognition

来源：评论

学校读者我要写书评

暂无评论

Homography-based vehicle pose estimation from a single image by using machine-learning for wheel-region and tire-road contact point detection 2nd

Homography-based vehicle pose estimation from a single image...

引用

2nd international conference on Smart Multimedia, ICSM 2019

作者： Radmehr, Nastaran Mehrandezh, Mehran Chan, Christine Faculty of Engineering and Applied Science University of Regina ReginaSKS4S 0A2 Canada

ISBN: (纸本)9783030544065

Image-based metric measurement and development of traffic surveillance systems have attracted wide interests within academia and industry for the past decade due to recent advancements in computer vision and the processing power required for machine-learning. Utilization of camera vision is gaining attention in this realm, particularly due to its unobtrusiveness. The research objective is to develop an image-based photogrammetry system for measuring vehicle lane pose using a single perspective camera with applications in law enforcement and crash-scene investigation. The proposed algorithm comprises of two steps: (1) Developing a Deep-learning-based technique for identifying/classifying the wheels on a vehicle, as Regions of Interests (ROI), and extracting the tire-road contact point from the image, and (2) using a Homography-based approach to extract metric measurements, such as vehicle pose. Our proposed method was tested and evaluated on a large number of images taken at different traffic inspection stations under different lighting conditions and weather differentials to demonstrate its efficiency and robustness. Results are promising. This research can pave the way towards automating the task of flagging truck bypass lanes for law enforcement and also for image-based crash-scene investigation. © Springer Nature Switzerland AG 2020.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Ring partition-based fingerprint indexing algorithm 1

引用

2nd international conference on machine Intelligence and signal processing, MISP 2019

作者： Gogoi, Manas Jyoti Trivedi, Amit Kumar Thounaojam, Dalton Meitei Bhattarchajee, Aniruddha Debnath, Rahul Borah, Kaushik Computer Vision Lab Department of Computer Science and Engineering National Institute of Technology Silchar Assam788010 India

ISBN: (数字)9789811513664

ISBN: (纸本)9789811513657

The shear number of fingerprints in a modern database makes exhaustive search, a computationally an expensive process. It is in this context a new indexing mechanism is proposed to speed up the process of identification of fingerprints. In this model, concentric circles are made around the core of a fingerprint, and by grouping the fingerprints according to the number of minutiae points in each of these rings, we can select the best prospective fingerprints for a particular query fingerprint, thus greatly reducing the number of fingerprints in which exhaustive search is to be employed and increasing the speed of Automated Fingerprint Identification Systems (AFIS). The proposed model was tested on different datasets of FVC2000 database, and the results show that the model achieves high CIP with a low penetration rate and our model was able to significantly speed up the process. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Query processing

来源：评论

学校读者我要写书评

暂无评论

Detecting unhealthy wheat plants using transfer learning method 1

引用

2nd international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： El Ouardi, Meriem Lahrache, Souad Amiri, Saïd El Belrhiti El Alaoui, Abdelbaki El Ouazzani, Rajae TIM Team High School of Technology Moulay Ismail University Meknes Morocco Department of Plant Protection National School of Agriculture Moulay Ismail University Meknes Morocco RSI Team Faculty of Sciences Moulay Ismail University Meknes Morocco

ISBN: (数字)9783030366643

ISBN: (纸本)9783030366636

Plenty of studies use image processing techniques to detect unhealthy wheat plants, but each disease of wheat have different symptoms. Thus, it is necessary to use different algorithms to have an accurate results, which is so consuming in energy and time and need a very powerful machine to do so. In this paper, we apply an intelligent system which uses deep learning methods that proved their accuracy in image classification. We use 300 digital images of unhealthy and healthy wheat plants, from different platforms of images on the net and TensorFlow as an intelligent system based on deep learning and transfer learning. Experimental results indicate that: significant classification performance (with an average accuracy of 70.66%) was achieved by the proposed method. © Springer Nature Switzerland AG 2020.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Image Based Automatic Traffic Surveillance System Through Number-Plate identification And Accident Detection

Image Based Automatic Traffic Surveillance System Through Nu...

引用

international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： S. M. Sunny Towhidur Rahman S. M. Zohurul Islam Asif Mujtaba Kazi Firoz Ahmed Shuvra Saha American International University - Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

The unpredictability and blockage of current transportation frameworks frequently produce traffic circumstances that endanger the security of the individuals in question. A simple invention can make easy to control the traffic system. This paper presents a programmed traffic observation framework to gauge significant traffic boundaries from video arrangements utilizing just captures from cameras. A traffic control kit is developed to detect over speeding cars on highways, number plates in Bengali, and initiating emergency call to 999 on detecting accidents. A GPS enabled traffic surveillance camera can detect the location of accident and send message to the traffic control room with location information of accident. A Python program is used to detect over speed which provides accurate speed of a vehicle very fast. OCR Tesseract is used to detect number plate which has very high performance in detecting noisy texts. To identify a case of accident, a simple Python code with Dens-net Architecture is used. A GSM module of the experimental kit initiate the call and message after analyzing the data through a code of C language. machine learning (ML) is used to train the program in identifying number plates. It is done by Anaconda.

关键词： GSM Surveillance Traffic control Cameras Optical character recognition software Accidents Python

来源：评论

学校读者我要写书评

暂无评论

Improved extreme learning machine and its application in SAR target recognition 2

Improved extreme learning machine and its application in SAR...

引用

2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Jian, Chen Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In this paper, An improved algorithm for the extreme learning machine is proposed and applied to SAR target *** order to solve the influence of the noise and spatial distribution of the training samples on the calculation of the classification plane, different penalty factors are given to different training samples,and according to this, the "weighted extreme learning machine" is proposed. And then,the kernel function is introduced into the "extreme learning machine" to improve the ability of nonlinear function approximation. Considering that the general training algorithm of the weighted extreme learning machine is slow and consumes a lot of computer memory when the number of training samples is large, a training method based on conjugate gradient algorithm is proposed. The test on "banana benchmark data" shows that the weighted extreme learning machine based on the conjugate gradient method can complete the convergence in the number of iterations far less than the number of samples, and the calculation speed is much faster than the traditional algorithm. Finally, this proposed algorithm is applied to SAR target recognition. The test on MSTAR data set shows that the proposed algorithm is not only extremely fast in SAR target recognition, but also has better recognition performance than support vector machine. general limit learning machine. BP neural network and other algorithms.

关键词： Extreme learning machine Conjugate Gradient Method Synthetic aperture radar Target recognition

来源：评论

学校读者我要写书评

暂无评论

A study of Spoken Word Recognition using Unsupervised learning with reference to Assamese Language 2

A study of Spoken Word Recognition using Unsupervised Learni...

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Nath, Dipen Kalita, Sanjib Kr. Mangaldai College Dept. of Computer Science IT MangaldaiAssam India Gauhati University Dept. of Computer Sience Guwahati-14Assam India

ISBN: (纸本)9781728107448

The aim of this paper is to measure the performance of Auto Associative Neural Network (AANN) in terms of recognition rate of spoken words with reference to Assamese language. One AANN models is built in the present study for spoken word recognition using continuous speech database. The recognition is measured by taking Mel Frequency Cepstral Coefficients (MFCC) as the feature vector along with its first and second derivatives. In this study, one database is created by using continuous speech. In the continuous database, there are 240 continuous speech files each around two minutes of duration to train and test the AANN model. AANN is trained using the continuous speech database and the recognition of certain words present within the continuous speech signal is measured. In this study, fifteen native Assamese speakers are used which involves eight Male speakers and seven feMale speakers. It is observed that the performance of AANN is 85.08% in terms of accuracy and the performance of AANN is 93.61% in terms of sensitivity. © 2019 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Acoustic Scene Classification Using Convolutional Neural Network 2nd

Acoustic Scene Classification Using Convolutional Neural Net...

引用

2nd international conference on Soft Computing and signal processing, ICSCSP 2019

作者： Akshara, S. Hemapriyalakshmi, R. Keerthana, S. Bharathi, B. Kavitha, S. Department of CSE SSN College of Engineering Chennai India

ISBN: (纸本)9789811524745

This proposed research work presents acoustic scene classification (ASC) which is an errand to relate a semantic name to a sound stream that distinguishes the environment in which it has been delivered. ASC can be applied in many areas including mobile robot navigation systems and context-aware devices, such as an automatically mode-switching smart phones according to the current acoustic environment. Proposing a strong ASC system is difficult because the sound from natural setting compromises numerous audio sources and also the microphones do not seem to be organized in a very controlled condition. Furthermore, not all sounds from long-duration audio data are relevant for identifying scene label. The dataset for this assignment is that the DCASE 2018 dataset collected from Tampere University of Technology, comprising of sound recordings from different scenes like airport, metro station, shopping mall, etc. For each location, there are 5–6 min of audio files. We propose to implement the ASC task using convolutional neural network (CNN) that performs the task of classification. The audio files are converted to log mel-spectrograms which are provided as input to CNN. Upon training the CNN model by varying the number of layers and the hyperparameters, it is observed that significant accuracy of 78.4 and 73.84% has been achieved for the inputs RGB scale spectrograms and grayscale spectrograms, respectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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135页 << < 69 70 71 72 73 74 75 76 77 78 > >>

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：