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

157 篇 期刊文献
92 篇 会议
2 篇 学位论文

馆藏范围

251 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

234 篇 工学
- 123 篇 计算机科学与技术...
- 86 篇 电气工程
- 40 篇 控制科学与工程
- 32 篇 信息与通信工程
- 24 篇 机械工程
- 18 篇 软件工程
- 15 篇 生物医学工程（可授...
- 14 篇 仪器科学与技术
- 12 篇 石油与天然气工程
- 8 篇 材料科学与工程（可...
- 8 篇 动力工程及工程热...
- 8 篇 电子科学与技术（可...
- 8 篇 测绘科学与技术
- 5 篇 土木工程
- 4 篇 化学工程与技术
- 2 篇 建筑学
- 2 篇 纺织科学与工程
- 2 篇 农业工程
47 篇 理学
- 19 篇 生物学
- 11 篇 物理学
- 9 篇 化学
- 8 篇 地球物理学
- 4 篇 数学
- 3 篇 地理学
- 3 篇 系统科学
- 2 篇 统计学（可授理学、...
30 篇 医学
- 13 篇 基础医学(可授医学...
- 13 篇 临床医学
- 3 篇 医学技术（可授医学...
23 篇 管理学
- 21 篇 管理科学与工程(可...
8 篇 农学
1 篇 法学
1 篇 教育学

主题

251 篇 sparse autoencod...
62 篇 deep learning
24 篇 fault diagnosis
17 篇 feature extracti...
16 篇 deep neural netw...
11 篇 machine learning
8 篇 dimensionality r...
7 篇 support vector m...
7 篇 deep belief netw...
7 篇 convolutional ne...
6 篇 image classifica...
6 篇 feature selectio...
6 篇 unsupervised lea...
5 篇 compressed sensi...
5 篇 anomaly detectio...
5 篇 face recognition
5 篇 autoencoder
5 篇 unsupervised fea...
5 篇 convolutional au...
5 篇 convolutional ne...

机构

6 篇 xian univ techno...
6 篇 tianshui normal ...
3 篇 nanyang technol ...
2 篇 nanjing normal u...
2 篇 zhongnan univ ec...
2 篇 northeast petr u...
2 篇 chinese acad sci...
2 篇 hong kong univ s...
2 篇 northeastern uni...
2 篇 china univ petr ...
2 篇 northwestern pol...
2 篇 hebei univ affil...
2 篇 kasetsart univ g...
2 篇 ming chuan univ ...
2 篇 hubei key lab ad...
2 篇 shenzhen institu...
2 篇 china univ geosc...
2 篇 zhengzhou univ l...
2 篇 natl cent univ d...
2 篇 natl inst techno...

作者

7 篇 xie guo
7 篇 yang jing
7 篇 yang yanxi
3 篇 hu jun
3 篇 nair madhu s.
3 篇 zhang yu-dong
3 篇 wang tianzhen
3 篇 xin bin
3 篇 wang shui-hua
2 篇 zhu zhongkui
2 篇 chen kunjin
2 篇 li xin
2 篇 wu min
2 篇 shen changqing
2 篇 kim ji-seon
2 篇 liu ming
2 篇 liu hongyi
2 篇 lin feng
2 篇 hou xiao-xia
2 篇 hou zengguang

语言

241 篇 英文
5 篇 中文
4 篇 其他
1 篇 法文

检索条件"主题词=Sparse AutoEncoder"

共 251 条记录，以下是61-70 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：

Face recognition based on deep aggregated sparse autoencoder network 37

Face recognition based on deep aggregated sparse autoencoder...

引用

37th Chinese Control Conference (CCC)

作者： Zou, Guofeng Lin, Dingyi Fu, Gui-xia Shen, Jin Gao, Mingliang Shandong Univ Technol Coll Elect & Elect Engn Zibo 255049 Peoples R China

ISBN: (纸本)9789881563958

sparse autoencoder network is sensitive to face noise, and the learning process is easy to ignore the face structure information. Address this problem, we propose a face recognition approach fused sub-region LBP feature and deep aggregated sparse autoencoder network. Firstly, the face image is divided into different sub-regions, and the local binary pattern is used to pre-process the face image in order to obtain the LBP features of different sub-region faces. Then, the deep features for different sub-region LBP features are extracted with different sparse autoencoders. Finally, the output features of different sparse autoencoders are aggregated by full connection, and the face feature vectors are obtained for classification. We get the optimal aggregated network structure and network parameters through lots of experiments, while the face recognition accuracy is also improved.

关键词： Unsupervised deep learning sparse autoencoder deep aggregated network LBP feature sub-region division face recognition

来源：评论

学校读者我要写书评

暂无评论

STACKED sparse autoencoder (SSAE) BASED FRAMEWORK FOR NUCLEI PATCH CLASSIFICATION ON BREAST CANCER HISTOPATHOLOGY 11

STACKED SPARSE AUTOENCODER (SSAE) BASED FRAMEWORK FOR NUCLEI...

引用

11th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Xu, Jun Xiang, Lei Hang, Renlong Wu, Jianzhong Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China Jiangsu Canc Hosp Nanjing 210000 Jiangsu Peoples R China

ISBN: (纸本)9781467319614

In this paper, a Stacked sparse autoencoder (SSAE) based framework is presented for nuclei classification on breast cancer histopathology. SSAE works very well in learning useful high-level feature for better representation of input raw data. To show the effectiveness of proposed framework, SSAE+Softmax is compared with conventional Softmax classifier, PCA+Softmax, and single layer sparse autoencoder (SAE)+Softmax in classifying the nuclei and non-nuclei patches extracted from breast cancer histopathology. The SSAE+Softmax for nuclei patch classification yields an accuracy of 83.7%, F1 score of 82%, and AUC of 0.8992, which outperform Softmax classifier, PCA+Softmax, and SAE+Softmax.

关键词： Deep learning sparse autoencoder Breast Cancer Histopathology

来源：评论

学校读者我要写书评

暂无评论

Attention-augmented X-vectors for the Evaluation of Mimicked Speech Using sparse autoencoder-LSTM framework 25

Attention-augmented X-vectors for the Evaluation of Mimicked...

引用

25th Interspeech Conference

作者： Bhasi, K. C. Rajan, Rajeev Noumida, A. Govt Engn Coll Barton Hill Thiruvananthapuram Kerala India Coll Engn Trivandrum Trivandrum Kerala India APJ Abdul Kalam Technol Univ Thiruvananthapuram Kerala India

This paper evaluates the quality of mimicked speech by computing the speaker embeddings. We propose an attention-augmented encoded speaker embedding for mimicking speaker evaluation. X-vector embeddings extracted from the spectral features are passed through a 1-D convolutional neural network (CNN) with an attention module. The resulting output is fed into a sparse autoencoder. Later, the encoded vector is fed to a long short-term memory (LSTM)-based scoring mechanism. The best mimicking artist is initially identified by a perception test. Later, the we investigate whether the LSTM-based self-attention model predicts the same artist. When the model identifies the mean opinion score(MOS)-identified artist with the highest probability (rank-1), we assume that one hit occurs. The performance evaluation is carried out with a mimicry dataset using top-X criteria. The experiment demonstrates efficacy in the proposed vector representation in competency evaluation of voice mimicking.

关键词： mimicked speech spectral features x-vector sparse autoencoder subjective test

来源：评论

学校读者我要写书评

暂无评论

Reconstruct Fingerprint Images Using Deep Learning and sparse autoencoder Algorithms

Reconstruct Fingerprint Images Using Deep Learning and Spars...

引用

Conference on Real-Time Image Processing and Deep Learning

作者： Saponara, Sergio Elhanashi, Abdussalam Gagliardi, Alessio Univ Pisa Dip Ingn Informaz Via G Caruso 16 I-56122 Pisa Italy

ISBN: (数字)9781510643109

ISBN: (纸本)9781510643109

Fingerprinting is one form of biometrics, a science that can be used for personal identification. It is one of the important techniques and security measures for human authentication across the globe due to its uniqueness and individualistic characteristics. Fingerprints are made up of an arrangement of ridges, called friction ridges. Each ridge consists of pores, that are attached to the glands under the skin. Several algorithms proposed different approaches to recreate fingerprint images. However, these works encountered problems with poor quality and presence of structured noise on these images. In this paper, we present a novel fingerprint system that provides more unique and robust algorithms which are capable to distinguish between individuals effectively. A sparse autoencoder (SAE) algorithm is used to reconstruct fingerprint images. It is an unsupervised deep learning model that replicates its input at the output. The architecture is designed and trained with datasets of fingerprint images that are pre-processed to be able to fit them in the model. Three datasets of fingerprint images have been utilized to validate the robustness of the model. This dataset has been split into 70% for training and 30% for testing the model. SAE is fine-tuned and optimized with L-2 and sparsity regularization, thus it increased the efficiency of learning representation for the architecture. The sparse autoencoder is a suitable deep learning model to improve the recreation of fingerprint images significantly. The proposed approach showed promising results, and it can enhance the quality of reproduced fingerprint images with a clear ridge structure and eliminating various overlapping patterns.

关键词： Reconstruct fingerprint sparse autoencoder unsupervised deep learning model personal identification

来源：评论

学校读者我要写书评

暂无评论

Fault Diagnosis Based on Batch-normalized Stacked sparse autoencoder 39

Fault Diagnosis Based on Batch-normalized Stacked Sparse Aut...

引用

39th Chinese Control Conference (CCC)

作者： Liu Xiaozhi Gao Yang Yang Yinghua Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

A fault diagnosis method based on batch-normalization stacked sparse autoencoder (SSAE) is presented in this paper. This paper use the autoencoder to extract features for fault diagnosis on account of its good performance in feature extraction. In order to improve the accuracy of the extracted features, this paper use a sparse representation which is a constraint during the encoding process. The multi-layer structure of autoencoder has an internal covariate shift problem and the generalization ability of the network is critical, batch normalization is employed before the activation function in each layer of the autoencoder network. And a stacked method is utilized to optimize network structure and reduce training difficulty. So the features of the original signal are extracted by the network based on the above method and the extracted features are placed in the classifier to identify different health states. For the purpose of fault diagnosis, this paper uses the proposed method to experiment with the bearing data set provided by Case Western Reserve University (CWRU). The experiment proves that the proposed method has a better fault diagnosis performance compared with other traditional methods.

关键词： Fault diagnosis sparse autoencoder Batch-normalization

来源：评论

学校读者我要写书评

暂无评论

Incident early warning based on sparse autoencoder and decision fusion for drilling process 47

Incident early warning based on sparse autoencoder and decis...

引用

47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Zhang, Zheng Lai, Xuzhi Wu, Min Du, Sheng China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

ISBN: (纸本)9781665435543

Complicated geological environments lead to a high risk of drilling incidents. Incident early warning for drilling process is in demand for industry field. An incident early warning method for loss and kick based on sparse autoencoder and decision fusion is proposed in this paper. sparse autoencoder is employed to detect the abnormality of the drilling parameter time series. Mann-Kendall trend test approach is performed to extract the trend of the time series that is detected as abnormal. The abnormality detection and trend extraction results of each drilling parameter are fused to get the final incident early warning result. Experiments are executed with the actual data collected from a practical drilling process. The experiment results indicate the effectiveness of the proposed method.

关键词： Decision fusion drilling process early warning sparse autoencoder time series

来源：评论

学校读者我要写书评

暂无评论

Power Quality Disturbances Classification Using sparse autoencoder (SAE) Based on Deep Neural Network 11

Power Quality Disturbances Classification Using Sparse Autoe...

引用

11th IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE)

作者： Manan, Nurul Asiah Shahbudin, Shahrani Kassim, Murizah Mohamad, Roslina Rahman, Farah Yasmin Abdul Univ Teknol MARA Fac Elect Engn Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9781665403382

Power quality is main concern for the electrical energy consumptions and electrical equipment. Hence, the power quality disturbances needed to monitor, improve and control. However, most of the research are focusing to the accuracy of the classification analysis. In this paper, an approach to classify the power quality disturbances is presented using the deep neural network algorithm. A raw data containing various types of the power quality disturbances, like swell, interruption, harmonics, and normal signal is evaluated. This several types of power quality disturbance will be extracted using the sparse autoencoder (SAE). The various values of weight decay parameter, A and sparsity parameter, p are applied to determine which features give optimal values. Optimal features learned from the SAE are then used to train a neural network classifier for identifying power quality disturbances.

关键词： deep learning power quality disturbance autoencoder sparse autoencoder neural network

来源：评论

学校读者我要写书评

暂无评论

A sparse autoencoder Based Denosing the Spectrum Signal in LIBS 30

A Sparse Autoencoder Based Denosing the Spectrum Signal in L...

引用

30th Chinese Control and Decision Conference (CCDC)

作者： Ye, Shibing Niu, Zhixing Yang, Peng Sun, Junqing Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300384 Peoples R China

ISBN: (纸本)9781538612446

Based on laser induced breakdown spectroscopy (LIBS) technique, the content of the main elements in the liquid steel of carbon steel alloy can he detected in real time during melting process. In order to detect the liquid alloy steel and forecast the content of the main elements in the alloy steel more accurately, we use the method of sparse autoencoder to reduce noise in the spectral data which were collected in the detecting process. The experimental results show that the sparse autoencoder method can effectively improve the accuracy and precision of the spectral data.

关键词： LIBS sparse autoencoder spectral data denosing

来源：评论

学校读者我要写书评

暂无评论

Robust Transfer Learning in Multi-Robot Systems by Using sparse autoencoder 19

Robust Transfer Learning in Multi-Robot Systems by Using Spa...

引用

19th IEEE International Conference on Soft Computing and Measurements (SCM)

作者： Utkin, Lev V. Popov, Sergey G. Zhuk, Y. A. Peter Great St Petersburg Polytech Univ Telemat Dept St Petersburg Russia ITMO Univ Dept Comp Educ Technol St Petersburg Russia

ISBN: (纸本)9781467389198

Robust algorithms for transfer learning in multi-robot systems based on elements of the deep learning are proposed in the paper. The algorithms are based on using the sparse autoencoder. The main ideas underlying the algorithms are to extend the set of set-valued observations by training examples having uncertain weights and to apply the robust minimax strategy in order to find an optimal autoencoder for dealing with set-valued observations. An interesting scheme for transfer learning is considered for which source learning set is reconstructed by means of the sparse autoencoder trained on the target learning set.

关键词： multi-robot system deep learning transfer learning sparse autoencoder extreme points minimax strategy

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Method Combined sparse autoencoder with SVM 7

A Deep Learning Method Combined Sparse Autoencoder with SVM

引用

6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Ju, Yao Guo, Jun Liu, Shuchun East China Normal Univ Comp Ctr Dept Shanghai Peoples R China

ISBN: (纸本)9781467392006

In this paper, a novel unsupervised method for learning sparse features combined with support vector machines for classification is proposed. The classical SVM method has restrictions on the large-scale applications. This model uses sparse autoencoder, a deep learning algorithm, to improve the performance. Firstly, we use multiple layers of sparse autoencoder to learn the features of the data. Secondly, we use SVM to classify. Many experimental results show that compared with SVM, our proposed method can improve the classification rate. In particular, it can effectively deal with large-scale data sets.

关键词： sparse autoencoder unsupervised learning deep learning SVM

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26页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：