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检索条件"主题词=Sparse AutoEncoder"

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A Rotating Machinery Fault Diagnosis Method for High-speed Trains Based on Improved Deep Learning Network 7

A Rotating Machinery Fault Diagnosis Method for High-speed T...

引用

7th International Conference on Control Automation and Information Sciences (ICCAIS)

作者： Yang, Jing Xie, Guo Yang, Yanxi Li, Xin Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Shaanxi Peoples R China Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China

ISBN: (纸本)9781538660201

Rotating machinery is an important part of highspeed train system. Any failure of such components will have a serious impact on the service itself and even jeopardize the safe and reliable operation of the train. However, the limited diagnostic accuracy has become the bottleneck of fault diagnosis. In this paper, an adaptive fault diagnosis method based on deep sparse autoencoder (SAE) network is proposed. Firstly, the original time domain signals collected by sensor are preprocessed and used as input to the diagnostic network. Secondly, the SAE fault diagnosis network model is built. Thirdly, the loss function of SAE is modified to improve the diagnostic accuracy and efficiency of network. Finally, experiments verify the feasibility and advantages of the proposed method.

关键词： sparse autoencoder ITS Non-stationary signal Fault diagnosis

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Post-Processing of Unsupervised Dictionary Learning in Handwritten Digit Recognition 14

Post-Processing of Unsupervised Dictionary Learning in Handw...

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International Symposium on Communications and Information Technologies

作者： Phaisangittisagul, Ekachai Chongprachawat, Rapeepol Kasetsart Univ Fac Engn Dept Elect Engn Bangkok 10900 Thailand Kasetsart Univ Grad Sch Bangkok 10900 Thailand

ISBN: (纸本)9781479944163

To achieve high performance in object recognition, a high-level feature representation is play an essential role to transform a raw input data (low-level) into a new representation. Unsupervised feature learning is one of the most successful methods that is widely used in machine learning literatures for creating a high-level feature to improve the supervised learning problems. The main concept of unsupervised feature learning is, in some sense, to encode input knowledge for gaining not only latent features but compact high-fidelity representation. In particular, a sparse coding has proven to be an effective tool as a prior lead to state-of-the-art performance in many benchmark datasets. In sparse coding, an input data can be represented as a sparse linear combination of a set of training overcomplete dictionary. However, an open problem in sparse coding is how to create and correctly choose the dictionary for representing a given input. Moreover, some bases of the learned dictionary is highly dependent and non-orthogonal among itself. In this study, we propose a post-processing scheme to transform the overcomplete dictionary obtained from unsupervised feature learning using Principle Component Analysis (PCA). The goal of our post-processing is to reduce the number of bases in the unsupervised dictionary learning for real-time applications. In experimental results, while the performance without post-processing the unsupervised dictionary learning is better than that of using PCA, it takes more computation and requires more resources during the test time.

关键词： dictionary high-level feature MNIST dataset sparse autoencoder unlabeled data unsupervised feature learning

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Learning the Structure of Auto-Encoding Recommenders 20

Learning the Structure of Auto-Encoding Recommenders

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29th World Wide Web Conference (WWW)

作者： Khawar, Farhan Poon, Leonard Zhang, Nevin L. Hong Kong Univ Sci & Technol Hong Kong Peoples R China Educ Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781450370233

autoencoder recommenders have recently shown state-of-the-art performance in the recommendation task due to their ability to model non-linear item relationships effectively. However, existing autoencoder recommenders use fully-connected neural network layers and do not employ structure learning. This can lead to inefficient training, especially when the data is sparse as commonly found in collaborative filtering. The aforementioned results in lower generalization ability and reduced performance. In this paper, we introduce structure learning for autoencoder recommenders by taking advantage of the inherent item groups present in the collaborative filtering domain. Due to the nature of items in general, we know that certain items are more related to each other than to other items. Based on this, we propose a method that first learns groups of related items and then uses this information to determine the connectivity structure of an auto-encoding neural network. This results in a network that is sparsely connected. This sparse structure can be viewed as a prior that guides the network training. Empirically we demonstrate that the proposed structure learning enables the autoencoder to converge to a local optimum with a much smaller spectral norm and generalization error bound than the fully-connected network. The resultant sparse network considerably outperforms the state-of-the-art methods like MULT-VAE/MULT-DAE on multiple benchmarked datasets even when the same number of parameters and flops are used. It also has a better cold-start performance.

关键词： Structure Learning Collaborative Filtering sparse autoencoder Wide autoencoder Shallow Networks

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DISTRIBUTION PRESERVING NETWORK EMBEDDING 44

DISTRIBUTION PRESERVING NETWORK EMBEDDING

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Qin, Anyong Shang, Zhaowei Zhang, Taiping Tang, Yuan Yan Chongqing Univ Chongqing Peoples R China Univ Macau Macau Peoples R China

ISBN: (纸本)9781479981311

The deep autoencoder network which is based on constraining non-negative weights, can learn a low dimensional part-based representation. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intraclass sample. Then one hopes to learn a new low dimensional feature which can preserve the intrinsic structure embedded in the high dimensional data space perfectly. In this paper, by preserving data distribution, a deep part-based representation can be learned, and the novel algorithm is called Distribution Preserving Network Embedding (DPNE). In DPNE, we first need to estimate the distribution of the original data, and then we seek a part-based representation which respects the distribution. The experimental results on real-world data sets show that the proposed algorithm has good performance in terms of cluster accuracy and adjusted mutual information (AMI).

关键词： Distribution preserving manifold structure part-based representation sparse autoencoder clustering

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High-speed Railway Clearance Surveillance System Based on Convolutional Neural Networks 8

High-speed Railway Clearance Surveillance System Based on Co...

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8th International Conference on Digital Image Processing (ICDIP)

作者： Wang, Yang Yu, Zujun Zhu, Liqiang Guo, Baoqing Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China

ISBN: (纸本)9781510605039;9781510605046

In this paper, the convolutional neural networks with the pre-trained kernels are applied to the video surveillance system, which has been built along the Shanghai-Hangzhou high-speed railway to monitor the railway clearance scene and will output the alarm images with the dangerous intruding objects in. The video surveillance system will firstly generate the images which are suspected of containing the dangerous objects intruding the clearance. The convolutional neural networks with the pre-trained kernels are applied to process these suspicious images to eliminating the false alarm images, only contain the trains and the empty clearance scene, from other suspicious images before the final output. Experimental result shows that, the process of each test image only takes 0.16 second and has a high accuracy.

关键词： High-speed railway train recognition sparse autoencoder pre-trained kernels convolutional neural networks

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Occupancy Measurement by Object Tracking at Building Entrances 36

Occupancy Measurement by Object Tracking at Building Entranc...

引用

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 *** paper proposes an occupancy measurement algorithm based on visual object tracking at entrances of *** 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 *** parameters are online uptated using the latest sample set after analysis of each *** modes of occlusion are summarized and compensation mechanism is designed once the occlusion is detected by a Finite State Machine(FSM) *** experimental result shows the good performance,especially in terms of robustness and *** 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

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Convolutional autoencoder-based Color Image Classification using Chroma Subsampling in YCbCr Space 8

Convolutional Autoencoder-based Color Image Classification u...

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Proceedings 2015 8 International Congress on Image and Signal Processing (CISP)

作者： Li, Zuhe Fan, Yangyu Wang, Fengqin Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou Peoples R China

ISBN: (纸本)9781467390989

We propose a convolutional autoencoder neural network for image classification in YCbCr color space to reduce computational complexity. We first learned local image features from image patches in YCbCr space with a sparse autoencoder and then convolved them with large images to obtain global features. Chrominance components were subsampled before convolution as it is permitted to reduce bandwidth for chrominance components in YCbCr space. We then adopted an algorithm to resize the convolved features in chrominance components by shifting the elements after convolution. Global features were finally fed into a softmax classifier to test the classification accuracy. Experimental results reveal that the convolutional neural network in YCbCr space is able to obtain a reduction of at least 21.6% in time consumption compared to the RGB representation with a slight loss in accuracy.

关键词： Image classification Computer vision Unsupervised feature learning Convolutional neural network sparse autoencoder

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A Deep Architecture for Face Recognition Based on Multiple Feature Extraction Techniques 5

A Deep Architecture for Face Recognition Based on Multiple F...

引用

IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

作者： Albelwi, Saleh Mahmood, Ausif Univ Bridgeport Dept Comp Sci Bridgeport CT 06604 USA

ISBN: (纸本)9781509055593

Some of the best current face recognition approaches use feature extraction techniques based on either Principle Component Analysis (PCA), Local Binary Patterns (LBP), autoencoder (non-linear PCA), etc. While each of these feature techniques works fairly well, we propose to combine multiple feature extractors with deep learning in a system so that the overall face recognition accuracy can be improved. The output from multiple feature extractions is classified using a deep learning approach. Deep learning algorithms possess high capability to learn more complex functions in order to handle difficult computer vison tasks. Our proposed method integrates the output of three different feature extractors, specifically PCA, LBP+PCA, and dimensionality reduction of LBP features using a Neural Network (NN). The features from the above three techniques are concatenated to form a joint feature vector. This feature vector is fed into a deep Sacked sparse autoencoder (SSA) as a classifier to generate the recognition results. Our proposed approach is evaluated by ORL and AR face databases. The experimental results indicate that our system outperforms existing ones based on individual feature techniques as well as reported systems employing multiple feature types.

关键词： Face recognition Multiple Classifiers (MC) deep learning Stacked sparse autoencoder (SSA) sparse autoencoder

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Deep and Self-taught Learning for Protein Accessible Surface Area Prediction 15

Deep and Self-taught Learning for Protein Accessible Surface...

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15th International Conference on Frontiers of Information Technology (FIT)

作者： ul Hassan, Fahad Minhas, Fayyaz ul Amir Afsar DI Khan Inst Nucl Med & Radiotherapy Dera Ismail Khan Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Biomed Informat Res Lab Islamabad Pakistan

ISBN: (纸本)9781538635674

ASA captures the degree of burial or surface accessibility of a protein residue. It is a very important indicator of the behavior of amino acids within a protein as well. It can be used to find protein interactions, interfaces, folding states, etc. Calculation of the ASA requires the presence of the structure of the protein. However, structure determination for proteins is expensive and requires significant technical effort. As a consequence, the prediction of ASA is a very important and fundamental problem in Bioinformatics and Proteomics. In this work, we have investigated self-taught machine learning methods along with deep neural network to predict the residue level accessible surface area (ASA) of a protein. We have found that deep learning neural networks can predict the ASA of the residues in a protein accurately. Furthermore, the proposed deep learning based method does not require the use of computationally demanding features such as the position specific scoring matrix (PSSM) which have been used in previous works. A simple Blosum62 matrix based position dependent representation of amino acids in a sequence window gives comparable performance. This is particularly attractive for proteome wide prediction of ASA. We have used various self-taught learning schemes for obtaining an optimal feature representation from unlabeled data. These include a sparse and regularized autoencoder neural network and a dictionary based learning scheme. We have used unlabeled data from the protein universe in an attempt to improve the feature representation. We have also evaluated the performance of a stochastic gradient based predictor of accessible surface area for different feature representations.

关键词： Accessible Surface Area Deep Learning Self-Taught Learning sparse autoencoder Dictionary Learning Stochastic Subgradient Optimization based Regressor

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A Stacking Ensemble Learning Model for Mental State Recognition towards Implementation of Brain Computer Interface 6

A Stacking Ensemble Learning Model for Mental State Recognit...

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6th National-Foundation-for Science-and-Technology-Development (NAFOSTED) Conference on Information and Computer Science (NICS)

作者： Hoang-Anh The Nguyen Thanh Ha Le The Duy Bui Vietnam Acad Sci & Technol Inst Informat Technol Hanoi Vietnam Vietnam Natl Univ Univ Engn & Technol Hanoi Vietnam

ISBN: (数字)9781728151632

ISBN: (纸本)9781728151632

This paper presents a novel stacking ensemble learning model that aims at improving mental state classification for brain computer interface implementation. The proposed model combines machine learning based methods that use support vector machine, artificial neural network and deep learning with a model selection rule to classify EEG signals into accurate mental states. The proposed ensemble learning model is validated on an EEG dataset in which EEG signals are recorded from four subjects. Three mental tasks are turning mind into Zen condition (Neutral), imagining how to turn a light on (Light) and memorizing scientific paper content (Paper). Experimental results show that this ensemble learning model is robust and effective while comparing with other existing machine learning models and methods for the same purpose.

关键词： Ensemble learning EEG signals Brain computer interface Deep learning sparse autoencoder

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