检索结果-内蒙古大学图书馆

denoising autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma

引用

Genomics, Proteomics & Bioinformatics 2020年第4期18卷 468-480页

作者： Jun Wang Xueying Xie Junchao Shi Wenjun He Qi Chen Liang Chen Wanjun Gu Tong Zhou Department of Thoracic Surgery Jiangsu Province People’s Hospital and the First Affiliated Hospital of Nanjing Medical UniversityNanjing 210029China State Key Laboratory of Bioelectronics School of Biological Sciences and Medical EngineeringSoutheast UniversityNanjing 210096China Department of Physiology and Cell Biology University of NevadaReno School of MedicineRenoNV 89557USA State Key Lab of Respiratory Disease Guangzhou Medical UniversityGuangzhou 510000China

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. denoising autoencoder(DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma(ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

关键词： denoising autoencoder Unsupervised learning Lung cancer Prognosis Molecular signature

来源：评论

学校读者我要写书评

暂无评论

Multiscale denoising autoencoder for improvement of target detection

引用

INTERNATIONAL JOURNAL OF REMOTE SENSING 2021年第8期42卷 3002-3016页

作者： Sun, Qiaoqiao Liu, Xuefeng Bourennane, Salah Liu, Bin Aix Marseille Univ Inst Fresnel Cent Marseille CNRS 52 Ave Escadrille Normandie Niemen F-13013 Marseille France Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao Peoples R China

Target detection is one of the most important applications of hyperspectral technology. However, due to spectral variations caused by noise or environment, the within-class variation is enlarged which degrades the performance of detectors, especially when the target size is small. Therefore, improving the detection performance of small targets and noisy targets is a key task. Considering the great feature extraction and representation ability of deep learning models, denoising autoencoder (DAE) is introduced to reconstruct spectrums and exploit the invariant information for target detection. To fully extract the features from the original spectrums, a multiscale denoising autoencoder (MSDAE) model is designed to incorporate complementary informationin in this paper. The final spectrum is fused by reconstructed spectrums from different scales representations, which provides more complex information and more robust features for subsequent spectral identification. Results on simulated hyperspectral images (HSIs) and real-world HSIs demonstrate that the proposed MSDAE model can effectively remove noise interference and lead to great improvements of the target detection. In addition, the proposed method shows significant potential in preserving small targets.

关键词： Target detection hyperspectral image denoising autoencoder spectrum

来源：评论

学校读者我要写书评

暂无评论

Feature learning using convolutional denoising autoencoder for activity recognition

引用

NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 10909-10922页

作者： Mohd Noor, Mohd Halim Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Wearable technology offers a prospective solution to the increasing demand for activity monitoring in pervasive healthcare. Feature extraction and selection are crucial steps in activity recognition since it determines the accuracy of activity classification. However, existing feature extraction and selection methods involve manual feature engineering, which is time-consuming, laborious and prone to error. Therefore, this paper proposes an unsupervised feature learning method that automatically extracts and selects the features without human intervention. Specifically, the proposed method jointly trains a convolutional denoising autoencoder with a convolutional neural network to learn the underlying features and produces a compact feature representation of the data. This allows not only more accurate and discriminative features to be extracted but also reduces the computational cost and improves generalization of the classification models. The proposed method was evaluated and compared with deep learning convolutional neural networks on a public dataset. Results have shown that the proposed method can learn a salient feature representation and subsequently recognize the activities with an accuracy of 0.934 and perform comparably well to the convolutional neural networks.

关键词： Activity recognition denoising autoencoder Deep learning Feature learning

来源：评论

学校读者我要写书评

暂无评论

Condition monitoring and performance forecasting of wind turbines based on denoising autoencoder and novel convolutional neural networks

引用

ENERGY REPORTS 2021年 7卷 6354-6365页

作者： Jia, Xiongjie Han, Yang Li, Yanjun Sang, Yichen Zhang, Guolei Harbin Engn Univ Coll Power & Energy Engn Harbin Peoples R China

With the proportion of wind power in the grid increasing, the monitoring and maintenance of wind turbines is becoming more and more important for the reliability of the grid. In this study, a data-driven modelling framework based on deep convolutional neural networks is constructed for wind turbines condition monitoring (CM) and performance forecasting (PF). For CM, a robust denoising autoencoder (DAE) model is introduced to output the reconstruction error (RE) of raw signals. The RE is processed to a state indicator by exponentially weighted moving average (EWMA) and monitored on a control chart. For PF, two multi-steps ahead forecasting models are constructed for the forecasting of generator bearing and transformer temperature. To prevent overfitting caused by abundant features, the marginal effect analysis based on random forests is implemented to measure the importance of features. Besides, novel residual attention module (RAM) and training strategies are used improve their representation power of DAE and PF models. Experiments on a real wind turbine dataset prove the effectiveness of the proposed models and methods. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Wind turbine Condition monitoring Performance forecasting denoising autoencoder Residual attention module

来源：评论

学校读者我要写书评

暂无评论

Detection of Freezing of Gait Using Unsupervised Convolutional denoising autoencoder

引用

IEEE ACCESS 2021年 9卷 115700-115709页

作者： Noor, Mohd Halim Mohd Nazir, Amril Ab Wahab, Mohd Nadhir Ling, Jodene Ooi Yen Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zayed Univ Coll Technol Innovat Dept Informat Syst Abu Dhabi U Arab Emirates

At the advanced stage of Parkinson's disease, patients may suffer from 'freezing of gait' episodes: a debilitating condition wherein a patient's "feet feel as though they are glued to the floor." The objective, continuous monitoring of the gait of Parkinson's disease patients with wearable devices has led to the development of many freezing of gait detection models involving the automatic cueing of a rhythmic auditory stimulus to shorten or prevent episodes. The use of thresholding and manually extracted features or feature engineering returned promising results. However, these approaches are subjective, time-consuming, and prone to error. Furthermore, their performance varied when faced with the different walking styles of Parkinson's disease patients. Inspired by state-of-art deep learning techniques, this research aims to improve the detection model by proposing a feature learning deep denoising autoencoder to learn the salient characteristics of Parkinsonian gait data that is applicable to different walking styles for the elimination of manually handcrafted features. Even with the elimination of manually handcrafted features, a reduction in half of the data window sizes to 2s, and a significant dimensionality reduction of learned features, the detection model still managed to achieve 90.94% sensitivity and 67.04% specificity, which is comparable to the original Daphnet dataset research.

关键词： Feature extraction Legged locomotion Support vector machines Diseases Sensitivity Data models Sensor phenomena and characterization Parkinson's disease freezing of gait denoising autoencoder unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network

引用

ENERGY 2021年 227卷 120451-120451页

作者： Chen, Junxiong Feng, Xiong Jiang, Lin Zhu, Qiao Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Sichuan Aerosp Syst Engn Res Inst Chengdu 610100 Peoples R China

To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data processing method with the conven-tional gated recurrent unit recurrent neural network (GRU-RNN) method. First, a denoising autoencoder neural network (DAE-NN) is introduced to extract the useful data features by reducing the noise and increasing the dimensions of the battery measurement data. Then, the feature-extracted data is utilized to train the GRU-RNN, which is widely used in SOC estimation. Notice that a good input data processing method plays a key role in the SOC estimation performance and the generalization ability. Therefore, it is not trivial to combine the data processing method (DAE-NN), and the SOC estimation method (GRU-RNN), which is named DAE-GRU. Compared with the traditional GRU-RNN, the new DAE-GRU method shows a better nonlinear mapping relation between the measurement data and the SOC because of the involvement of the DAE-NN. Finally, three different driving cycles are given in the experiment to cross -train and verify the proposed DAE-GRU, GRU-RNN and RNN. Compared with the GRU-RNN and the RNN, it is demonstrated that the proposed DAE-GRU has better accuracy and robustness in the SOC estimation. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： State of charge estimation Lithium-ion battery denoising autoencoder Gated recurrent unit Recurrent neural network Electric vehicle

来源：评论

学校读者我要写书评

暂无评论

Stacked Graph Fusion denoising autoencoder for Hyperspectral Anomaly Detection

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Zhang, Yongshan Li, Yijiang Wang, Xinxin Jiang, Xinwei Zhou, Yicong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a few anomalous pixels from a majority of background pixels. Most existing methods cannot simultaneously explore both structural and spatial information from global and local perspectives. In this letter, we propose a stacked graph fusion denoising autoencoder (SGFDAE) for hyperspectral anomaly detection. Specifically, the global and local graphs are constructed from an HSI to explore potential structural and spatial information. With the designed graph fusion strategy, an advanced graph denoising autoencoder with deep architecture is developed in a hierarchical manner. To achieve better reconstruction and detection, a greedy layerwise unsupervised pretraining strategy is presented for network training. Experiments show that SGFDAE achieves 97.17%, 98.43%, and 98.90% detection accuracies by averaging the results of the datasets from three different scenes and outperforms the state-of-the-art methods.

关键词： Detectors Noise reduction Anomaly detection Training Image edge detection Hyperspectral imaging Geoscience and remote sensing denoising autoencoder graph neural network hyperspectral imagery

来源：评论

学校读者我要写书评

暂无评论

Nonlinear process monitoring based on load weighted denoising autoencoder

引用

MEASUREMENT 2021年 171卷 108782-108782页

作者： Zhu, Jiazhen Shi, Hongbo Song, Bing Tao, Yang Tan, Shuai Zhang, Tianqing East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Shanghai Shixiang Technol Corp Ind Big Data Res Ctr Shanghai 200237 Peoples R China

Traditional monitoring methods are trained with normal data and map the process variables into latent variables directly. However, for these methods, the process variables would become intertwined in the latent variables, which results in that the fluctuations of process variables would be submerged in noise or neutralized in latent variables space. In order to address the submergence and neutralization problems, a novel algorithm load weighted denoising autoencoder (LWDAE) is proposed. According to the direction and magnitude of online data, the loading matrix of LWDAE is weighted to highlight the useful information of both training data and online data in latent variables space. In addition, to reduce the effect of noise on weighting matrix, LWDAE modifies the loss function by adding two new regularizations and revises the calculation logic of weighting matrix to consider the successive samples. Case studies of continuous stirred tank reactor demonstrate the effectiveness of LWDAE.

关键词： denoising autoencoder Nonlinear process Fault detection Online weighting strategy

来源：评论

学校读者我要写书评

暂无评论

An end-to-end denoising autoencoder-based deep neural network approach for fault diagnosis of analog circuit

引用

ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2021年第3期107卷 605-616页

作者： Yang, Yueyi Wang, Lide Chen, Huang Wang, Chong Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China

Fault diagnosis of analog circuit is critical to improve safety and reliability in electrical systems and reduce losses. Traditional fault diagnosis methods of analog circuit usually rely on the hand design feature extractor and can not generalize well to other diagnosis domains. To address these issues, an end-to-end denoising autoencoder (EEDAE)-based fault diagnosis approach is proposed. The proposed approach includes denoising autoencoder (DAE) and a softmax classifier. The DAE is designed to automatically extract fault features from the raw time series signals without any signal processing techniques and diagnostic expertise, and then the softmax classifier is used to classify the fault mode of analog circuits. Specifically, we design a novel loss function by jointly minimizing reconstruction loss and classification loss to improve training efficiency. The proposed approach just has one training stage, in which the encoder, decoder, and classifier are trained simultaneously. The experimental results demonstrate that compared with traditional methods, the proposed method has higher accuracy and lower requirements on data.

关键词： Analog circuits Fault diagnosis denoising autoencoder End-to-end

来源：评论

学校读者我要写书评

暂无评论

Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network

引用

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2023年 16卷 916511页

作者： Yan, Ting Yan, Zhenpeng Liu, Lili Zhang, Xiaoyu Chen, Guohui Xu, Feng Li, Ying Zhang, Lijuan Peng, Meilan Wang, Lu Li, Dandan Zhao, Dong Shanxi Med Univ Dept Pathol Key Lab Cellular Physiol Minist Educ Taiyuan Shanxi Peoples R China Taiyuan Univ Technol Coll Informat & Comp Taiyuan Peoples R China Shanxi Prov Peoples Hosp Taiyuan Peoples R China Capital Med Univ Beijing Chaoyang Hosp Dept Stomatol Beijing Peoples R China

ObjectivesThis study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM). MethodsIn this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of the Cox proportional hazard regression model was used to extract features for survival prediction. In addition, the Kaplan-Meier curve, the Schoenfeld residual analysis, the time-dependent receiver operating characteristic curve, the nomogram, and the calibration curve were performed to assess the survival prediction ability. ResultsThe concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard regression model, reached 0.78 in the training set, 0.75 in the validation set, and 0.74 in the test set. Patients were divided into high- and low-risk groups based on the median prognostic index (PI). Kaplan-Meier curve was used for survival analysis (p = < 2e-16 in the training set, p = 3e-04 in the validation set, and p = 0.007 in the test set), which showed that the survival probability of different groups was significantly different, and the PI of the network played an influential role in the prediction of survival probability. In the residual verification of the PI, the fitting curve of the scatter plot was roughly parallel to the x-axis, and the p-value of the test was 0.11, proving that the PI and survival time were independent of each other and the survival prediction ability of the PI was less affected than survival time. The areas under the curve of the training set were 0.843, 0.871, 0.903, and 0.941;those of the validation set were 0.687, 0.895, 1.000, and 0.967;and those of the test set were 0.757, 0.852, 0.683, and 0.898. ConclusionThe survival prediction model, which combines the DAE and the Cox proportional hazard regression model, can effectively predict the prognosis of patients with GBM.

关键词： glioblastoma multiforme deep learning survival prediction denoising autoencoder time-dependent ROC curve Cox proportional hazard regression

来源：评论

学校读者我要写书评

暂无评论

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

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：

请选择收藏分类：

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

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：