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Stacked sparse autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

IEEE ACCESS 2017年 5卷 15066-15079页

作者： Qi, Yumei Shen, Changqing Wang, Dong Shi, Juanjuan Jiang, Xingxing Zhu, Zhongkui Soochow Univ Sch Urban Rail Transportat Suzhou 215131 Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

As a breakthrough in the field of machine fault diagnosis, deep learning has great potential to extract more abstract and discriminative features automatically without much prior knowledge compared with other methods, such as the signal processing and analysis-based methods and machine learning methods with shallow architectures. One of the most important aspects in measuring the extracted features is whether they can explore more information of the inputs and avoid redundancy to be representative. Thus, a stacked sparse autoencoder (SAE)-based machine fault diagnosis method is proposed in this paper. The penalty term of the SAE can help mine essential information and avoid redundancy. To help the constructed diagnosis network further mine more abstract and representative high-level features, the collected non-stationary and transient signals are preprocessed with ensemble empirical mode decomposition and autoregressive (AR) models to obtain AR parameters, which are extracted based on the intrinsic mode functions (IMFs) and regarded as the low-level features for the inputs of the proposed diagnosis network. Only the first four IMFs are considered, because fault information is mainly refiected in high-frequency IMFs. Experiments and comparisons are complemented to validate the superiority of the presented diagnosis network. Results fully demonstrate that the stacked SAE-based diagnosis method can extract more discriminative high-level features and has a better performance in rotating machinery fault diagnosis compared with the traditional machine learning methods with shallow architectures.

关键词： sparse autoencoder ensemble empirical mode decomposition autoregressive model fault diagnosis

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Credit Card Fraud Detection Using sparse autoencoder and Generative Adversarial Network 9

Credit Card Fraud Detection Using Sparse Autoencoder and Gen...

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9th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

作者： Chen, Jian Shen, Yao Ali, Riaz Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781538672662

Current credit card detection methods usually utilize the idea of classification, requiring a balanced training dataset which should contain both positive and negative samples. However, we often get highly skewed datasets with very few frauds. In this paper, we want to apply deep learning techniques to help handle this situation. We firstly use sparse autoencoder (SAE) to obtain representations of normal transactions and then train a generative adversarial network (GAN) with these representations. Finally, we combine the SAE and the discriminator of GAN and apply them to detect whether a transaction is genuine or fraud. The experimental results show that our solution outperforms the other state-of-the-art one-class methods.

关键词： credit fraud detection deep learning sparse autoencoder generative adversarial network

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Image Super-Resolution Algorithm Based on an Improved sparse autoencoder

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INFORMATION 2018年第1期9卷 11页

作者： Huang, Detian Huang, Weiqin Yuan, Zhenguo Lin, Yanming Zhang, Jian Zheng, Lixin Huaqiao Univ Coll Engn 269 Chenghuabei Rd Quanzhou 362021 Peoples R China Shantou Univ Dept Elect Engn 243 Daxue Rd Shantou 515063 Peoples R China

Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application's requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high-and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.

关键词： super-resolution sparse autoencoder dictionary learning ZCA whitening

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A sparse autoencoder Based Denosing the Spectrum Signal in LIBS

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

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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

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Pseudoinverse Learning Algorithom for Fast sparse autoencoder Training

Pseudoinverse Learning Algorithom for Fast Sparse Autoencode...

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IEEE Congress on Evolutionary Computation

作者： Bingxin Xu Ping Guo Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

sparse autoencoder is one approach to automatically learn features from unlabeled data and received significant attention during the development of deep neural networks. However, the learning algorithm of sparse autoencoder suffers from slow learning speed because of gradient descent based algorithms have many drawbacks. In this paper, a fast learning algorithm for sparse autoenceder is proposed which based on pseudoinverse learning algorithm (PIL). The proposed method calculates encoder weight matrix by truncating the pseudoinverse matrix of input data. The pseudoinverse truncation matrix is used as the weights of encoder, and then the input data is mapped to the hidden layer space through the biased ReLU activation function. The decoder weights are also can computed by the PIL. Unlike the gradient descent based algorithm, the proposed method does not require a time-consuming iterative optimization process and select many user-dependent parameters such as learning rate or momentum constant too. The experimental results indicate the superiority of proposed method which is very efficient and also can learned the sparsity of samples.

关键词： sparse autoencoder pseudoinverse learning algorithm fast learning data representation

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Face recognition based on deep aggregated sparse autoencoder network

Face recognition based on deep aggregated sparse autoencoder...

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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 *** this problem,we propose a face recognition approach fused sub-region LBP feature and deep aggregated sparse autoencoder ***,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 ***,the deep features for different sub-region LBP features are extracted with different sparse ***,the output features of different sparse autoencoders are aggregated by full connection,and the face feature vectors are obtained for *** 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

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sparse autoencoder based deep neural network for voxelwise detection of cerebral microbleed 22

Sparse Autoencoder based deep neural network for voxelwise d...

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22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhang, Yu-Dong Hou, Xiao-Xia Lv, Yi-Ding Chen, Hong Zhang, Yin Wang, Shui-Hua Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Hunan Prov Key Lab Network Invest Technol Changsha 410138 Hunan Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Neurol Nanjing 210029 Jiangsu Peoples R China Nanjing Med Univ Dept Psychiat Nanjing 210029 Jiangsu Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Hubei Peoples R China CUNY City Coll New York Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781509044573

In order to detect cerebral microbleed more efficiently, we developed a novel computer-aided detection method based on susceptibility-weighted imaging. We enrolled five CADASIL patients and five healthy controls. We used a 20x20 neighboring window to generate samples on each slice of the volumetric brain images. The sparse autoencoder (SAE) was used to unsupervised feature learning. Then, a deep neural network was established using the learned features. The results over 10x10-fold cross validation showed our method yielded a sensitivity of 93.20 +/- 1.37%, a specificity of 93.25 +/- 1.38%, and an accuracy of 93.22 +/- 1.37%. Our result is better than Roy's method, which was proposed in 2015.

关键词： sparse autoencoder deep neural network cerebral microbleed cross validation susceptibility weighted imaging

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Self-Taught Learning Based on sparse autoencoder for E-Nose in Wound Infection Detection

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SENSORS 2017年第10期17卷 2279页

作者： He, Peilin Jia, Pengfei Qiao, Siqi Duan, Shukai Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive;thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol);however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector theta. Then, using the basis vector theta, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy.

关键词： electronic nose self-taught learning sparse autoencoder wound infection

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

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

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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

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Asymmetry Analysis with sparse autoencoder in Mammography 16

Asymmetry Analysis with Sparse Autoencoder in Mammography

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8th International Conference on Internet Multimedia Computing and Service (ICIMCS)

作者： Yang, Dongxiao Wang, Ying Jiao, Zhicheng Xidian Univ Sch Elect Engn VIPS Lab Xian 710071 Peoples R China

ISBN: (纸本)9781450348508

With the development of medical image processing methods, computer-aided detection systems sprang up, which could assist the diagnosis of radiologists, reducing fatigue, and greatly improve the accuracy and efficiency of early detection. However, most researches concentrated on mass detection methods by unilateral mammography, ignoring the relationship between left and right mammograms that the bilateral breast tissues were symmetric by physiology. However, radiologists often preferred to take images of both sides into consideration to judge if there were lesions. In this paper, we proposed an asymmetric analysis framework which employed bilateral mammograms together. Generally speaking, an improved bilateral region matching method that control points extracted automatically based on shape context was used to align the corresponding regions of bilateral mammograms. Meanwhile, sparse autoencoder was introduced into asymmetric analysis to learn a network between normal and its corresponding normal regions. For giving one side of bilateral regions, it would output a reconstruction vector of another form through the trained network. A similarity was computed between the output and the corresponding region of input, therefore a reasonable result was given.

关键词： Bilateral asymmetric analysis region matching multi-scale sliding window sparse autoencoder breast mass

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