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

207 篇 期刊文献
131 篇 会议
2 篇 学位论文

馆藏范围

340 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

315 篇 工学
- 176 篇 计算机科学与技术...
- 123 篇 电气工程
- 50 篇 信息与通信工程
- 43 篇 控制科学与工程
- 28 篇 软件工程
- 25 篇 仪器科学与技术
- 21 篇 电子科学与技术（可...
- 18 篇 石油与天然气工程
- 11 篇 动力工程及工程热...
- 10 篇 机械工程
- 8 篇 材料科学与工程（可...
- 8 篇 生物医学工程（可授...
- 6 篇 测绘科学与技术
- 5 篇 土木工程
- 4 篇 建筑学
- 4 篇 化学工程与技术
- 4 篇 生物工程
- 3 篇 力学（可授工学、理...
- 3 篇 船舶与海洋工程
- 3 篇 环境科学与工程（可...
69 篇 理学
- 32 篇 物理学
- 25 篇 生物学
- 9 篇 化学
- 6 篇 数学
- 6 篇 地球物理学
44 篇 医学
- 25 篇 临床医学
- 15 篇 基础医学(可授医学...
- 6 篇 特种医学
23 篇 管理学
- 19 篇 管理科学与工程(可...
5 篇 农学
1 篇 教育学
1 篇 文学
1 篇 艺术学

主题

340 篇 denoising autoen...
64 篇 deep learning
21 篇 convolutional ne...
16 篇 noise reduction
13 篇 neural network
12 篇 machine learning
11 篇 speech recogniti...
10 篇 autoencoder
8 篇 deep neural netw...
8 篇 robust speech re...
7 篇 feature enhancem...
7 篇 transfer learnin...
7 篇 anomaly detectio...
7 篇 missing data
7 篇 neural networks
7 篇 classification
7 篇 unsupervised lea...
6 篇 collaborative fi...
6 篇 fault diagnosis
6 篇 remaining useful...

机构

3 篇 nagaoka univ tec...
3 篇 wuhan univ sch c...
3 篇 acad sinica res ...
2 篇 tsinghua univ de...
2 篇 nanyang technol ...
2 篇 natl taiwan univ...
2 篇 univ calif irvin...
2 篇 univ chinese aca...
2 篇 donald bren scho...
2 篇 nanyang technol ...
2 篇 taiyuan univ tec...
2 篇 harbin inst tech...
2 篇 beijing univ pos...
2 篇 univ sains malay...
2 篇 nanjing univ sci...
2 篇 tsinghua univ de...
2 篇 tech univ libere...
2 篇 natl inst techno...
2 篇 univ calif irvin...
2 篇 city univ hong k...

作者

5 篇 wang longbiao
5 篇 kai atsuhiko
4 篇 tsao yu
4 篇 ueda yuma
3 篇 he fazhi
3 篇 pan yiteng
3 篇 schanze thomas
3 篇 malek jiri
3 篇 li jian
3 篇 samann fars
3 篇 liu bin
2 篇 zhang tao
2 篇 petr cerva
2 篇 kang moses
2 篇 baek jongbok
2 篇 wang huawei
2 篇 wang yuhao
2 篇 padil khairul ha...
2 篇 liu yang
2 篇 teeparthi kiran

语言

328 篇 英文
11 篇 其他
1 篇 中文

检索条件"主题词=denoising autoencoder"

共 340 条记录，以下是271-280 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：

EFFECTIVE JOINT TRAINING OF denoising FEATURE SPACE TRANSFORMS AND NEURAL NETWORK BASED ACOUSTIC MODELS

EFFECTIVE JOINT TRAINING OF DENOISING FEATURE SPACE TRANSFOR...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fukuda, Takashi Ichikawa, Osamu Kurata, Gakuto Tachibana, Ryuki Thomas, Samuel Ramabhadran, Bhuvana IBM Watson Multimodal Chuo Ku Tokyo 1038510 Japan IBM Watson Multimodal Yorktown Hts NY 10598 USA

ISBN: (纸本)9781509041176

Neural Network (NN) based acoustic frontends, such as denoising autoencoders, are actively being investigated to improve the robustness of NN based acoustic models to various noise conditions. In recent work the joint training of such frontends with backend NNs has been shown to significantly improve speech recognition performance. In this paper, we propose an effective algorithm to jointly train such a denoising feature space transform and a NN based acoustic model with various kinds of data. Our proposed method first pretrains a Convolutional Neural Network (CNN) based denoising frontend and then jointly trains this frontend with a NN backend acoustic model. In the unsupervised pretraining stage, the frontend is designed to estimate clean log Mel-filterbank features from noisy log-power spectral input features. A subsequent multi-stage training of the proposed frontend, with the dropout technique applied only at the joint layer between the frontend and backend NNs, leads to significant improvements in the overall performance. On the Aurora-4 task, our proposed system achieves an average WER of 9.98%. This is a 9.0% relative improvement over one of the best reported speaker independent baseline system's performance. A final semi-supervised adaptation of the frontend NN, similar to feature space adaptation, reduces the average WER to 7.39%, a further relative WER improvement of 25%.

关键词： Speech recognition neural network CNN joint training denoising autoencoder

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning Self-Supervised Multilingual Sequence-To-Sequence Models for Extremely Low-Resource NMT 7

Fine-Tuning Self-Supervised Multilingual Sequence-To-Sequenc...

引用

Moratuwa Engineering Research Conference (MERCon) / 7th International Multidisciplinary Engineering Research Conference

作者： Thillainathan, Sarubi Ranathunga, Surangika Jayasena, Sanath Univ Moratuwa Dept Comp Sci & Engn Katubedda Sri Lanka

ISBN: (纸本)9781665437530

Neural Machine Translation (NMT) tends to perform poorly in low-resource language settings due to the scarcity of parallel data. Instead of relying on inadequate parallel corpora, we can take advantage of monolingual data available in abundance. Training a denoising self-supervised multilingual sequence-to-sequence model by noising the available large scale monolingual corpora is one way to utilize monolingual data. For a pair of languages for which monolingual data is available in such a pre-trained multilingual denoising model, the model can be fine-tuned with a smaller amount of parallel data from this language pair. This paper presents fine-tuning self-supervised multilingual sequence-to-sequence pre-trained models for extremely low-resource domain-specific NMT settings. We choose one such pre-trained model: mBART. We are the first to implement and demonstrate the viability of non-English centric complete fine-tuning on multilingual sequence-to-sequence pretrained models. We select Sinhala, Tamil and English languages to demonstrate fine-tuning on extremely low-resource settings in the domain of official government documents. Experiments show that our fine-tuned mBART model significantly outperforms state-of-the-art Transformer based NMT models in all pairs in all six bilingual directions, where we report a 4.41 BLEU score increase on Tamil -> Sinhala and a 2.85 BLUE increase on Sinhala -> Tamil translation.

关键词： neural machine translation pre-trained models fine-tuning denoising autoencoder low-resource languages

来源：评论

学校读者我要写书评

暂无评论

Online Anomaly Detection with Streaming Data based on Fine-grained Feature Forecasting 33

Online Anomaly Detection with Streaming Data based on Fine-g...

引用

33rd Chinese Control and Decision Conference (CCDC)

作者： Liu, Keying Mao, Wentao Shi, Huadong Wu, Chao Chen, Jiaxian Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China

ISBN: (纸本)9781665440899

In the industrial applications like fault diagnosis and health management, monitoring data generally reaches sequentially in a streaming form. To recognize fault occurrence in real time without system halt, it is necessary to improve the accuracy and stability of anomaly detection with streaming data. To solve this problem, a new online anomaly detection method with streaming data is proposed based on fine-grained feature forecasting. First, to conduct fine-grained decomposition of features, a denoising autoencoder network is run to extract multiple-dimensional deep features of online data in the initial period of normal state. Second, a forecasting model with tensor Tucker decomposition and ARIMA is conducted to predict the fluctuation trend of all feature sequences. Finally, the deviation degree between the prediction values and sequentially-arrived data is calculated, and an alarm threshold is built according to the 95% confidence interval of the maximum deviation. Then the anomalous state data can be detected in real time. This paper adopts the problem of bearing early fault online detection as an example, and run comparative experiments on the IEEE PHM Challenge 2012 bearing dataset. The results show that the proposed method has good detection accuracy and is with no false alarm, while the model training does not rely on any offline data. Then the proposed method is applicable to the problem of online anomaly detection.

关键词： Streaming data denoising autoencoder Anomaly detection Alarm threshold ARIMA model

来源：评论

学校读者我要写书评

暂无评论

Understanding Chromatin Remodeling Through Physics-Based Machine Learning Approaches

Understanding Chromatin Remodeling Through Physics-Based Mac...

引用

作者： Alvarado, Walter The University of Chicago

学位级别：Ph.D., Doctor of Philosophy

The packing of nucleosomes regulates gene expression through genome condensation and expansion, but the specific structures and their thermodynamic stabilities remain unresolved. In this work, we employ the use of a meso-scale model of chromatin, referred to as "1-Cylinder-per-Nucleosome," or 1CPN, in combination with nonlinear manifold learning to identify and characterize the structure and free energy of metastable states of short chromatin segments. Our results reveal the intrinsic formation of two previously characterized tetranucleosomal conformations, the "α-tetrahedron" and the "β-rhombus," which have been suggested to play a role in inducing chromatin compaction or elongation, respectively. Building upon these findings, we leverage convolutional neural networks and molecular dynamics simulations, to design a deep convolutional denoising autoencoder (DAE) capable of providing nucleosome-level resolution of scanning transmission electron microscopy images of chromatin (ChromSTEM). Our DAE is trained on synthetic images generated from simulations of the chromatin fiber using the 1CPN model of chromatin, thereby learning structural features driven by the physics of chromatin folding. We find that our DAE outperforms other well-known denoising algorithms without degradation of structural features and allows for the resolution of individual nucleosomes and organized domains within chromatin-dense regions. Lastly, we investigate how post-translational modifications (PTMs), modeled as modifications to the nucleosome interaction potential, affect the construction of these motifs and, consequently, the chromatin fiber as a whole. Our study provides important insight into chromatin folding and highlights the value of interdisciplinary approaches in this field.

关键词： Chromatin denoising autoencoder Machine learning Molecular dynamics Nonlinear manifold learning Tetranucleosome

来源：评论

学校读者我要写书评

暂无评论

DAE-Transformer-based Remaining Useful Life Prediction for Lithium-Ion Batteries in Energy Storages 3

DAE-Transformer-based Remaining Useful Life Prediction for L...

引用

3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Huang, Bowen Zeng, Zihao Zhou, Yamin Liu, Jiang Zheng, Qian Wang, Luting Feng, Zihao Sun, Shukai Pan, Zheng Energy Saving Management Branch of State Grid Hunan Electric Power Co. Ltd Changsha China Hunan University College of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350393224

To improve the operation stability and reliability of energy storage stations (ESSs), it's significance to ensure high-precision battery remaining useful life (RUL) prediction. Recently, the raw capacity of batteries in ESSs are affected by noise and long-term dependence on time series, which negatively impact the accuracy of the RUL prediction model. To address this issue, this paper proposes a RUL prediction model based on denoising autoencoder (DAE) and Transformer for batteries in ESSs. Firstly, the DAE is used to reduce the noise of the original battery capacity to reduce the influence of data noise on the accuracy of the RUL prediction model. Then, the Transformer network is used to learn to normalize the time-series long-term dependencies of the capacity to generate high-precision RUL predictions. The proposed model is verified by experiments based on the battery degradation dataset in the NASA Center for Prediction Excellence data repository. © 2023 IEEE.

关键词： component denoising autoencoder Lithium-ion battery Remaining useful life Transformer

来源：评论

学校读者我要写书评

暂无评论

MIDC-Net: Medical Image denoising and Disease Classification Network for Chest X-rays 3

MIDC-Net: Medical Image Denoising and Disease Classification...

引用

3rd International Conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Li, Jiatu University of California San Diego Halicioǧlu Data Science Institute Department of Mathematics San DiegoCA United States

ISBN: (纸本)9798350316674

Accurate medical imaging is vital for precise disease diagnosis and effective treatment. However, X-ray images may be subject to varying degrees of noise due to factors such as patient health conditions requiring reduced X-ray radiation. In this paper, the Medical Image denoising and Disease Classification Network (MIDC-Net) is proposed to solve the issue of noisy chest X-rays that negatively affects thorax disease classification by performing image denoising and disease classification simultaneously. The MIDC-Net integrates a Convolutional Neural Network (CNN) for classification, and a denoising autoencoder with convolutional layers and residual connections for image denoising. It comprises three components, namely an encoder, a decoder, and a classifier, where the input of the encoder is noisy images with varying levels of Gaussian noise, and the output of the decoder and the classifier are reconstructed images and predicted diseases. The loss of the MIDC-Net is a linear combination of Mean Square Error (MSE) loss used for denoising and Binary Cross-Entropy (BCE) loss used for multi-label classification. Experimental outcomes on the NIH chest X-ray dataset illustrated that the reconstructed images resembled the original X-rays, and the network achieved a low MSE loss, but the quality of denoised images exhibited an inverse relationship with noise levels. Regarding multi-label thorax classification results, the test accuracies all exceed 95% regardless of noise levels of the input images, demonstrating efficient and accurate prediction in the scenario of noisy inputs. © 2023 IEEE.

关键词： convolutional neural network denoising autoencoder Image denoising multi-label classification

来源：评论

学校读者我要写书评

暂无评论

autoencoder: Issues, Challenges and Future Prospect 3rd

Autoencoder: Issues, Challenges and Future Prospect

引用

3rd International Conference on Recent Innovations and Technological Development in Mechanical Engineering, ICRITDME 2020

作者： Maheshwari, Anega Mitra, Priyanka Sharma, Bhavna Department of Computer Science Jaipur Engineering College and Research Centre Rajasthan Jaipur India

ISBN: (纸本)9789811692352

As of more recently, deep learning-based models have demonstrated considerable potential, as they have outperformed all traditional practices. When data becomes high dimensional, extraction of features and compression of data become progressively significant. In this paper, we describe the autoencoder deep learning algorithm. autoencoder is primarily a neural network-based feature extraction methodology that accomplishes outstanding victory in producing highlights of high-dimensional data. autoencoder assumes a principal job in unsupervised learning which targets to rework inputs into outputs with minimal reconstruction error. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： autoencoder Convolutional autoencoder denoising autoencoder Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

LPI radar signal recognition with U2-Net-based denoising 14

LPI radar signal recognition with U2-Net-based denoising

引用

14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Lee, Siho Nam, Haewoon Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9798350313277

Low Probability of Intercept (LPI) radar signals play a vital role in electronic warfare by maintaining informational superiority. Classifying these LPI radar waveforms is a key capability but remains a challenging task due to strong noise interference. Traditional signal processing techniques often show limitations in effectively removing complex noise signals. While deep learning-based modulation classification has exhibited superior performance, its effectiveness is compromised in the presence of significant noise. In this study, we propose a deep learning-based denoising method using the U2-Net for LPI radar signals, followed by modulation classification using a Convolutional Neural Network (CNN). We further compare the performance of U2-Net with other denoising models such as U-Net and denoising autoencoder. Experimental results demonstrate that the U2-Net outperforms other methods, achieving over 90% classification accuracy for signals with a signal-to-noise ratio above -14dB. © 2023 IEEE.

关键词： denoising autoencoder Low Probability of Intercept (LPI) radar time frequency analysis U-Net U2-Net

来源：评论

学校读者我要写书评

暂无评论

NrGe-DTL: a computational framework for cancer drug response prediction based on deep transfer learning from combined denoised genomic profiles and chemical structure embedding of drugs

NrGe-DTL: a computational framework for cancer drug response...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yuchen Lian, Linghang Yang, Xuhua Zhejiang University of Technology Coll. of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350337488

In recent years, precision medicine has been consistently studied and employed in cancer treatment. One of the main challenges in precision medicine is accurately predicting a cancer patient's response to a specific drug(s) using computational models. Due to the heterogeneity of cancer, the distribution between the drug response profiles based on cell lines and real samples is often different, which leads to out-of-data distribution problems that affect putting the existing predictive models into practical application. To overcome this limitation, a novel framework to predict cancer drug response based on transfer learning from combined genomic profiles and chemical structure embedding of drugs is proposed in this paper. This framework features a denoising auto-encoder (DAE) module designed to remove noise from genomics profiles and a graph convolutional network (GCN) employed to extract the embedding of pharmaceutical chemical structures. Through the experiments, our model outperforms recently published methods on Genomics of Drug Sensitivity in Cancer (GDSC) datasets, and gives promising results on personalized drug response prediction on TCGA pan-cancer datasets, especially for the response of two commonly used anti-cancer drugs―Gemcitabine and Cisplatin. In summary, our study designed a computational framework with the potential capability of predicting personalized cancer drug response, which has been preliminarily validated for its effectiveness. © 2023 IEEE.

关键词： chemical structure information denoising autoencoder drug response precision medicine transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Reliability Assessment Method for Composite Power Systems 3

A Data-Driven Reliability Assessment Method for Composite Po...

引用

3rd International Conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Liu, Zeyu Zhang, Bingchen Li, Qiang Zhao, Feng Liu, Di Hou, Kai Tianjin University Key Laboratory of Smart Grid of Ministry of Education Tianjin300072 China State Grid Information and Telecommunication Group Co. Ltd Beijing102211 China

ISBN: (纸本)9798350318180

A data driven approach for reliability assessment of composite power systems have been proposed in our paper. A Multi-Layer Extreme Learning Machine (MELM) is trained to graph the relations among system states versus minimum load curtailment. This approach is a computationally efficient alternative to the traditional optimal power flow (OPF) optimizations. Moreover, the pretraining process using denoising autoencoder (DAE) is utilized to improve the feature extraction and predictive performance. By conducting case studies on the RTS-79 system, the proposed methodology was evaluated. Results show that the proposed method is efficient and effective in enhancing the assessment of reliability of composite power systems. © 2023 IEEE.

关键词： composite systems data-driven denoising autoencoder multilayer exterme learning machine reliability assessment

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34页 << < 24 25 26 27 28 29 30 31 32 33 > >>

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：