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8th International Symposium on Computational Intelligence and Design (ISCID)

作者： Yang, Ran Yin, Huarui Chen, Xiaohui Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Peoples R China

ISBN: (纸本)9781467395878

In modern society, automatic license plate recognition (ALPR) plays an important role in the field of Intelligent Transport Systems (ITS). In order to recognize the license plate efficiently, the location of the license plate must be detected first. In consequence, the detection of the license plate becomes a crucial stage in an ALPR system, affecting the performance of the whole system enormously. In this paper, we propose a novel method based on sparse auto-encoder (SAE) to detect the vehicle license plate. The proposed method consists of three main stages: (1) A block-based image segmentation technique used for dividing the image into several blocks. (2) Deep learning model (SAE) trained for candidate block selection. (3) Accurate extraction of the license plate. Unlike other existing license plate detection methods, the proposed algorithm use a deep learning model to learn the features of the license plate. Experiment results demonstrate that our method can detect various types of license plates with a high accuracy and a relatively short running time.

关键词： license plate detection deep learning sparse auto-encoder

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Imbalanced Fault Diagnosis Based on Particle Swarm Optimization and sparse auto-encoder 24

Imbalanced Fault Diagnosis Based on Particle Swarm Optimizat...

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24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)

作者： Peng, Peng Zhang, Wenjia Zhang, Yi Wang, Hongwei Zhang, Heming Tsinghua Univ Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781728165974

Imbalanced fault diagnosis becomes increasingly im-portant as the number of fault samples is relatively small in practical situations. sparse auto-encoder(SAE) has been well addressed in fault diagnosis while it is not suitable for imbalanced fault diagnosis. Cost sensitive learning can be uti-lized to extend the sparse auto-encoder to cost sensitive sparse auto-encoder(CS-SAE). However, the class weights assigned to different classes are usually unknown in practice. So we propose to use particle swarm optimization(PSO) to optimize the class weights for cost sensitive sparse auto-encoder(PSO-CSSAE). The experiments have shown that the proposed ap-proach consistently outperforms the state of the art on Tennesse Eastman(TE) dataset.

关键词： deep learning fault diagnosis imbalanced learning sparse auto-encoder particle swarm optimization

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Layerwise feature selection in Stacked sparse auto-encoder for tumor type prediction

Layerwise feature selection in Stacked Sparse Auto-Encoder f...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Singh, Vikas Baranwal, Nikhil Sevakula, Rahul K. Verma, Nishchal K. Cui, Yan Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India Indian Inst Technol Kanpur Fac Elect Engn Kanpur Uttar Pradesh India Univ Tennessee Hlth Sci Ctr Fac Microbiol Immunol & Biochem Knoxville TN 37996 USA

ISBN: (纸本)9781509016105

Transcriptome data has been proved to be very valuable for clinical applications, such as diagnosis and prognosis of various cancers. In this paper, we present layer-wise feature selection in conjunction with stacked sparse autoencoders (SSAE), a deep learning strategy for tumor classification with gene expression data. While SSAE learns high-level features from data, performing feature selection in every layer is a heuristic to obtain relevant features at every stage and also to assist in reducing the computation during fine-tuning procedure. The data in the new feature representation is finally used by classifier(s) to perform Tumor detection. The algorithm was tested on 36 datasets from the GEMLeR repository and w.r.t. AUC (Area under ROC curve) performance, it was found to outperform the GEMLeR benchmark results on 35 datasets (tied on the other dataset).

关键词： sparse auto-encoder cancer classification deep learning feature selection

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An effective approach for the diagnosis of melanoma using the sparse auto-encoder for features detection and the SVM for classification 5

An effective approach for the diagnosis of melanoma using th...

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5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Zghal, Nadia Smaoui Kallel, Imene Khanfir Natl Sch Engineers Sfax Lab Control & Energy Management Sfax Tunisia

ISBN: (纸本)9781728175133

Malignant melanoma is considered one of the terrible disorders causing death. The goal of the modern dermatology is the early screening of skin cancer, aiming at reducing the mortality rate with less extensive treatment. In this context, this work focuses on the problem of an automatic melanoma diagnosis. The proposed approach uses unsupervised robustness of deep learning to extract significant characteristics from pixels of the images. A preprocessing step is used to remove unwanted artifacts and to improve the contrast of the images. Then, features are extracted by a deep sparse autoencoder. Finally, the classifier Support Vector Machine (SVM) is used to distinguish respectively between three populations which are Melanoma, suspicious cases, and non-melanoma. For evaluation, we test the proposed approach using images from the PH2 dataset. The results show remarkable performance in terms of specificity, sensitivity, and accuracy.

关键词： sparse auto-encoder PH2 database Melanoma SVM Classification

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Deep Representations Based on sparse auto-encoder Networks for Face Spoofing Detection 11th

Deep Representations Based on Sparse Auto-Encoder Networks f...

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11th Chinese Conference on Biometric Recognition (CCBR)

作者： Yang, Dakun Lai, Jianhuang Mei, Ling Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783319466545;9783319466538

automatic face recognition plays significant role in biometrics systems, and face spoofing has raised concerns at the same time, since a photo or video of an authorized uesr's face could be used for deceiving the system. In this paper, we propose a new hierarchical visual feature based on deep learning to discriminate spoof face. First, the LBP descriptor is used to extract low level face features of face images, and then these low level features are encoded to high level features via a deep learning architecture which is consists of sparse auto-encoder (SAE). Finally, SVM classifier is applied to detect face spoofing. We perform a experimental evaluation on two face liveness detection databases, CASIA database and NUAA database. The results indicate the robustness of the proposed approach for face spoofing detection.

关键词： LBP Deep learning sparse auto-encoder SVM Face spoofing

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Water Quality Prediction Model Combining sparse auto-encoder and LSTM Network

Water Quality Prediction Model Combining Sparse Auto-encoder...

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6th International-Federation-of-automatic-Control (IFAC) Conference on Bio-Robotics (BIOROBOTICS)

作者： Li, Zhenbo Peng, Fang Niu, Bingshan Li, Guangyao Wu, Jing Miao, Zheng China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Agr Informat Acquisit Technol Beijing 100083 Peoples R China Beijing Engn & Technol Res Ctr Internet Things Ag Beijing 100083 Peoples R China

In order to improve the prediction accuracy of dissolved oxygen in aquaculture, a hybrid model based on sparse auto-encoder (SAE) and long-short-term memory network (LSTM) is proposed in this paper. The hidden layer data pre-trained by SAE contains deep latent features of water quality, and then input it into the LSTM to enhance the prediction accuracy. Experimental results show that SAE-LSTM surpasses LSTM through reducing MSE respectively by 23.3%, 53.6%, and 39.2% in the prediction steps of 3, 6, and 12 hours, and surpasses SAE-BPNN by 87.7%, 91.9%, and 90.0%, proving that our hybrid model is more accurate. (C) 2018, IFAC (International Federation of automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： water quality prediction dissolved oxygen sparse auto-encoder long-short-term memory network

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Multi-joint industrial robot fault identification using deep sparse auto-encoder network with attitude data

Multi-joint industrial robot fault identification using deep...

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Prognostics and System Health Management Conference (PHM-Besancon)

作者： Hong, Ying Sun, Zhenzhong Zou, Xiaohong Long, Jianyu Dongguan Univ Technol Sch Mech Engn Dongguan Peoples R China Everwin Precis Technol Co Ltd Guangdong Everwin Precis Technol Co Ltd Dongguan Peoples R China

ISBN: (纸本)9781728156750

Intelligent fault identification of the mechanical transmission system for multi-joint industrial robots is important to guarantee safe operations. An attitude data-based intelligent fault identification approach is introduced for multi-joint robots in this study. Considering that the attitude change of the last joint can be used to reflect the attitude change of the other connecting rods or joints, an economical data acquisition strategy is proposed by only installing one attitude sensor on the last joint. An intelligent fault identification model is subsequently established by training a deep sparse auto-encoder network (DSAE) on the attitude dataset. To test the performance of the proposed approach, a test-rig was built to collect attitude dataset under different fault conditions. Experimental results show that the proposed fault identification approach has effective performance for multi-joint industrial robots.

关键词： multi-joint industrial robot fault identification fault diagnosis sparse auto-encoder deep learning

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Learning Facial Expression Codes with sparse auto-encoder

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2nd International Conference on Mechatronics and Control Engineering (ICMCE 2013)

作者： Hu, Dekun Duan, Guiduo Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat P Chengdu 610106 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China

ISBN: (纸本)9783037858943

A sparse auto-encoder model was trained to extract the code of different facial expression, which comprises four encoder layers and three decode layers, the representation locating in the fourth layer (code layer) is the features expected. With large amounts of patches randomly selected from training faces, the model was trained firstly via backpropagation which minimizes an unsupervised sparse reconstruction error, and then a softmax classifier was learned for supervised classification. The input vector for the classification is the feature of facial image induced by the learned sparse auto-encoder and two key operations (convolving and pooling). Using a small number of hidden units per layer and a relatively small number of training set, the proposed model achieves excellent performance in the experiments.

关键词： Feature learning facial expression sparse auto-encoder

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Transfer learning with deep sparse auto-encoder for speech emotion recognition

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Journal of Southeast University(English Edition) 2019年第2期35卷 160-167页

作者： Liang Zhenlin Liang Ruiyu Tang Manting Xie Yue Zhao Li Wang Shijia School of Information Science and Engineering Southeast UniversityNanjing 210096China School of Communication Engineering Nanjing Institute of TechnologyNanjing 211167China School of Computer Engineering Jinling Institute of TechnologyNanjing 211169China

In order to improve the efficiency of speech emotion recognition across corpora,a speech emotion transfer learning method based on the deep sparse auto-encoder is *** algorithm first reconstructs a small amount of data in the target domain by training the deep sparse auto-encoder,so that the encoder can learn the low-dimensional structural representation of the target domain ***,the source domain data and the target domain data are coded by the trained deep sparse auto-encoder to obtain the reconstruction data of the low-dimensional structural representation close to the target ***,a part of the reconstructed tagged target domain data is mixed with the reconstructed source domain data to jointly train the *** part of the target domain data is used to guide the source domain *** on the CASIA,SoutheastLab corpus show that the model recognition rate after a small amount of data transferred reached 89.2%and 72.4%on the *** to the training results of the complete original corpus,it only decreased by 2%in the CASIA corpus,and only 3.4%in the SoutheastLab *** show that the algorithm can achieve the effect of labeling all data in the extreme case that the data set has only a small amount of data tagged.

关键词： sparse auto-encoder transfer learning speech emotion recognition

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Deep Feature Learning for Tibetan Speech Recognition using sparse auto-encoder

Deep Feature Learning for Tibetan Speech Recognition using S...

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2015 International Conference on Electrical, automation and Mechanical Engineering(EAME 2015)

作者： H.Wang Y.Zhao X.F.Liu X.N.Xu L.Wang N.Zhou Y.M.Xu School of Information Engineering Minzu University of China

ISBN: (纸本)9781510806528

HMM models based on MFCC features are widely used by researchers in Tibetan speech *** the shallow models of HMM are effective,they cannot reflect the speech perceptual mechanism in human being's *** this paper,we propose to apply sparse auto-encoder to learn deep features based on MFCC features for speech *** deep features not only simulate sparse touches signal of the auditory nerve,and are significant to improve speech recognition accuracy with HMM *** results show that the deep features learned by sparse auto-encoder perform better on Tibetan speech recognition than MFCC features and the deep features learned by MLP.

关键词： deep feature learning sparse auto-encoder tibetan speech recognition MFCC features

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