检索结果-南通市图书馆

Chinese Journal of Mechanical Engineering 2021年第3期34卷 146-157页

作者： Zhe Yang Dejan Gjorgjevikj Jianyu Long Yanyang Zi Shaohui Zhang Chuan Li School of Mechanical Engineering Dongguan University of TechnologyDongguan 523808China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China Faculty of Computer Science and Engineering Ss.Cyril and Methodius UniversitySkopjeMacedonia

Supervised fault diagnosis typically assumes that all the types of machinery failures are known.However,in practice unknown types of defect,i.e.,novelties,may occur,whose detection is a challenging task.In this paper,a novel fault diagnostic method is developed for both diagnostics and detection of novelties.To this end,a sparse autoencoder-based multi-head Deep Neural Network(DNN)is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data.The detection of novelties is based on the reconstruction error.Moreover,the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function,instead of performing the pre-training and fine-tuning phases required for classical DNNs.The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer.The results show that its performance is satisfactory both in detection of novelties and fault diagnosis,outperforming other state-of-the-art methods.This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect,but also detect unknown types of defects.

关键词： Deep learning Fault diagnostics Novelty detection Multi-head deep neural network sparse autoencoder

Fault Diagnosis Based on Batch-normalized Stacked sparse autoencoder

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

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

第三十九届中国控制会议

作者： Liu Xiaozhi Gao Yang Yang Yinghua College of Information Science and Engineering Northeastern University

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

Face recognition based on deep aggregated sparse autoencoder network

在线全文

cnki会议

学校读者我要写书评

暂无评论

Face recognition based on deep aggregated sparse autoencoder...

第37届中国控制会议

作者： Guofeng Zou Dingyi Lin Gui-xia Fu Jin Shen Mingliang Gao College of Electrical and Electronic Engineering Shandong University of Technology

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

A sparse autoencoder Based Denosing the Spectrum Signal in LIBS

在线全文

cnki会议

学校读者我要写书评

暂无评论

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

第30届中国控制与决策会议

作者： Shibing Ye Zhixing Niu Peng Yang Junqing Sun Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology

Based on laser induced breakdown spectroscopy（LIBS） technique, the content of the main elements in the liquid steel of carbon steel alloy can be 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

Representation learning via an integrated autoencoder for unsupervised domain adaptation

在线全文

cnki会议

学校读者我要写书评

暂无评论

Frontiers of Computer Science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target domain.The key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain discrepancy.Recently,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain adaptation.However,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain features.To address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called IAUDA.To capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature representations.After higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain representations.In addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local area.Experimental results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and sparse Auto Encoder Method

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第7期76卷 793-811页

作者： Deepthi K.Oommen J.Arunnehru Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalaniChennai600026TamilnaduIndia

Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.

关键词： Alzheimer’s disease mild cognitive impairment Weiner filter contrast limited adaptive histogram equalization transfer learning sparse autoencoder deep neural network

Down image recognition based on deep convolutional neural network

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Information Processing in Agriculture 2018年第2期5卷 246-252页

作者： Wenzhu Yang Qing Liu Sile Wang Zhenchao Cui Xiangyang Chen Liping Chen Ningyu Zhang School of Cyber Security and Computer Hebei UniversityBaoding 071002PR China

Since of the scale and the various shapes of down in the image,it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy,even for the Traditional Convolutional Neural Network(TCNN).To deal with the above problems,a Deep Convolutional Neural Network(DCNN)for down image classification is constructed,and a new weight initialization method is proposed.Firstly,the salient regions of a down image were cut from the image using the visual saliency model.Then,these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters,which accord with the statistical characteristics of dataset.At last,a DCNN with Inception module and its variants was constructed.To improve the recognition accuracy,the depth of the network is deepened.The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN,when recognizing the down in the images.The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN.

关键词： Deep convolutional neural network Weight initialization sparse autoencoder Visual saliency model Image recognition

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

基于三维人脸建模的多视角人脸识别方法研究

基于三维人脸建模的多视角人脸识别方法研究

作者：邢健飞杭州电子科技大学

学位级别：硕士

作为典型的模式识别任务，人脸识别有着巨大的实际应用价值与市场前景。理想环境下的人脸识别已经取得不俗成绩，然而，当所处环境变化（如姿态变换、夸张表情、阴阳脸、分辨率较低）时，识别难度增加，效果也急剧变差。与此同时，现有... 详细信息

作为典型的模式识别任务，人脸识别有着巨大的实际应用价值与市场前景。理想环境下的人脸识别已经取得不俗成绩，然而，当所处环境变化（如姿态变换、夸张表情、阴阳脸、分辨率较低）时，识别难度增加，效果也急剧变差。与此同时，现有方法大多数无法实时（在线）完成人脸识别任务，这也限制了人脸识别技术的应用。本文在浙江省大学生科技成果推广项目（2013R407063）的赞助下，以人脸识别的背景及目的意义为出发点，考虑到人脸的三维结构，分析研究了基于三维人脸建模的多视角人脸识别方法，本文主要完成了以下几方面的研究工作：（1）人脸检测阶段：为了确保系统的稳定性与实时性，选择了基于AdaBoost的人脸检测算法并将所得的分类器打包成XML文件的形式，为以后的实时人脸识别系统的搭建打下了基础。（2）三维人脸建模方面：在比较几种基于图像的三维人脸建模方法后，选择了基于单张正面的照片的三维人脸建模方法，与常规方法相比，该方法的可行性与实用性高，方法可以无缝链接到人脸识别系统中。本文提出了一套基于Candide-3的三维人脸建模方法，具体分为两步：首先，使用改进的ASM（主动形状模型）算法对定位正面人脸的特征点进行定位并根据临近点插值法确定非特征点坐标;然后，在纹理贴图阶段，本文依据DMS样条函数，在Candide-3标准模型的基础上生成了含更多顶点的人脸模型，使得建立的模型真实感和连续性更好。最后，给出了根据所建三维人脸模型生成的多视角人脸图像。（3）在人脸图像特征提取阶段：本文提出了基于深度神经网络的人脸识别方法，本文的深度神经网络的训练分为两步：无监督阶段与有监督阶段。其中，在无监督阶段，使用sparse autoencoder在大规模人脸库LFW的基础上进行图像恢复以训练深度神经网络。与此同时，为探讨网络层数对于识别效果的影响，本文训练了多种多层数的神经网络，实验结果表明，识别率并非与网络层数正相关;有监督阶段，采用邻近元分析的方法，利用混合人脸库人脸的多样性（多姿态、多表情、多种光照），有目的地提高网络对于人脸的理解能力，减小相同人脸间距离，增大不同人脸间距离。实验结果表明，较传统方法，本文提出的特征提取算法可提取人脸图像的深度特征，适用于人脸姿态、表情、光照变化较大的人脸识别任务中，对于人脸识别方法的探究是一次有益的尝试。（4）在实际的人脸识别过程中：提出了一种“搜索半径”的概念。首先将每张图像用二进制数据表示。在图像检索过程中，先计算出图像间的二进制数据间的汉明距离，然后按照汉明距离排序，在汉明距离较小的半径内，寻找与待识别图像欧式距离最小的图像，根据阈值，确定该图像是否是待识别个体。实验结果表明，本套识别方法所用识别时间极小，适合于对实时性要求较高的人脸识别任务。（5）最后，本文将所有的算法整合起来，利用OpenCV、OpenGL、Armadillo等库搭建了人脸实时识别系统，并演示了系统的具体操作。实验结果表明，本套人脸系统识别效果好，识别时间短，是人脸识别较为理想的方案。

关键词：人脸识别三维人脸建模 AdaBoost Candide-3 sparse autoencoder 邻近元分析

Occupancy Measurement by Object Tracking at Building Entrances

同方学位论文库评论

在线全文

同方学位论文库

学校读者我要写书评

暂无评论

Occupancy Measurement by Object Tracking at Building Entranc...

第36届中国控制会议

作者： Jianhong Zou Qianchuan Zhao Rui Cong Center for Intelligent and Networked Systems Department of AutomationTNLISTTsinghua University IEEE

ISBN: (纸本)9781538629185

Building occupancy measurement is becoming an increasingly important topic in the energy saving area.This paper proposes an occupancy measurement algorithm based on visual object tracking at entrances of buildings.The algorithm figures out variation of occupancy by comparing the size flags of the same pedestrian in adjacent frames.A CNN based deep learning tracker is designed to match multiple pedestrians in sequential frames.Its parameters are online uptated using the latest sample set after analysis of each frame.Five modes of occlusion are summarized and compensation mechanism is designed once the occlusion is detected by a Finite State Machine(FSM) model.The experimental result shows the good performance,especially in terms of robustness and accuracy.The algorithm is very valuable in measuring occupancy of buildings once it is integrated with the existing video surveillance software.

关键词： Occupancy measurement object tracking deep learning anti-occlusion tracking sparse autoencoder