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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhang, Dahai Wu, Congzhou Gai, Qin Bi, Yanqiu Zhang, Xiaowei Beijing Jiaotong Univ Sch Elect Engn Beijing Peoples R China Global Energy Interconnect Dev & Cooperat Org Beijing Peoples R China

ISBN: (纸本)9781665422482

The fault of power grid will cause serious personal safety problems and economic losses. It is very important to diagnose the power grid fault accurately and quickly. In order to improve the fault diagnosis accuracy for hybrid AC-DC power grid, this paper proposes a stacked sparse autoencoder-convolutional neural network method. The paper uses stacked sparse autoencoder (SSAE) to reduce the dimensionality of high dimensional data sets, and then uses convolutional neural network (CNN) to extracts data features to diagnose different line faults and different types of faults in the power grid. Finally, the effectiveness of the proposed method is validated by MATLAB simulation, and shows that the proposed method has a high accuracy to distinguish different faults.

关键词： deep learning fault diagnosis stacked sparse autoencoder convolutional neural network

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Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第9期58卷 2751-2766页

作者： Fang, Weiguang Guo, Yu Liao, Wenhe Ramani, Karthik Huang, Shaohua Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Nanjing Jiangsu Peoples R China Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Nanjing Univ Sci & Technol Sch Mech Engn Nanjing Jiangsu Peoples R China

Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of things (IOT) in the job shop, there is an increasing need for handling manufacturing BD for predictive manufacturing. In this study, we conceive the jobs remaining time (JRT) prediction during manufacturing execution based on deep learning (DL) with production BD. We developed a procedure for JRT prediction that includes three parts: raw data collection, candidate dataset design and predictive modelling. First, the historical production data are collected by the widely deployed IOT in the job shop. Then, the candidate dataset is formalised to capture various contributory factors for JRT prediction. Further, a DL model named stacked sparse autoencoder (S-SAE) is constructed to learn representative features from high dimensional manufacturing BD to make robust and accurate JRT prediction. Our work represents the first DL model for the JRT prediction at run time during production. The proposed methods are applied in a large-scale job shop that is equipped with 44 machine tools and produces 13 types of parts. Lastly, the experimental results show the S-SAE model has higher accuracy than previous linear regression, back-propagation network, multi-layer network and deep belief network in JRT prediction.

关键词： big data job shop jobs remaining time prediction stacked sparse autoencoder deep learning

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Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

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JOURNAL OF INTELLIGENT MANUFACTURING 2020年第4期31卷 799-814页

作者： Zhang, Yanxi You, Deyong Gao, Xiangdong Wang, Congyi Li, Yangjin Gao, Perry P. Guangdong Univ Technol Guangdong Prov Welding Engn Technol Res Ctr Guangzhou 510006 Peoples R China US China Youth Educ Solut Fdn New York NY 10019 USA

The laser welding quality is determined by its welding statuses, and online welding statuses are depicted by the real-time signals captured from the welding process. A multiple-sensor system is designed to obtain information as comprehensive as possible for welding statuses monitoring. The multiple-sensor system includes an auxiliary illumination visual sensor system, an ultraviolet and visible band visual sensor system, a spectrometer and two photodiodes. The signals captured by different sensors are analyzed via signal or digital image processing algorithms, and distinct features are extracted from these signals to depict the online welding statuses. A deep learning framework based on stacked sparse autoencoder (SSAE) is established to model the relationship between the multi-sensor features and their corresponding welding statuses, and Genetic algorithm (GA) is applied to optimize the parameters of the SSAE framework (SSAE-GA). The proposed framework achieves higher accuracy and stronger robustness in monitoring welding status by comparing with the backpropagation neural network, support vector machine and random forest. Three new experiments with different welding parameters are implemented to validate the effectiveness and generalization of our proposed method. This study provides a novel and accurate method for high-power disk laser welding status monitoring.

关键词： Features fusion Deep learning Genetic algorithm stacked sparse autoencoder Multiple-sensor signals

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A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

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IEEE ACCESS 2020年 8卷 202100-202110页

作者： Yang, Jun Ma, Zhengmin Wang, Jin Fu, Yunfa Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650093 Yunnan Peoples R China Yunnan Univ Fac Informat Kunming 650500 Yunnan Peoples R China

Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-to-noise ratio and spatial resolution make MI-EEG decoding a challenging task. Recently, some deep neural approaches have shown good improvements over state-of-the-art BCI methods. In this study, an end-to-end scheme that includes a multi-layer convolution neural network is constructed for an accurate spatial representation of multi-channel grouped MI-EEG signals, which is employed to extract the useful information present in a multi-channel MI signal. Then the invariant spatial representations are captured from across-subjects training for enhancing the generalization capability through a stacked sparse autoencoder framework, which is inspired by representative deep learning models. Furthermore, a quantitative experimental analysis is conducted on our private dataset and on a public BCI competition dataset. The results show the effectiveness and significance of the proposed methodology.

关键词： Brain– computer interface discriminative and representative deep learning feature fusion convolution neural network stacked sparse autoencoder

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Deep neural network for semi-automatic classification of term and preterm uterine recordings

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年 105卷 101861-101861页

作者： Chen, Lili Xu, Huoyao Chongqing Jiaotong Univ Sch Mechatron & Vehicle Engn Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Sch Chongqing Key Lab Urban Rail Transit Vehicle Chongqing 400074 Peoples R China

Pregnancy is a complex process, and the prediction of premature birth is uncertain. Many researchers are exploring non-invasive approaches to enhance its predictability. Currently, the ElectroHysteroGram (EHG) and Tocography (TOCO) signal are a real-time and non-invasive technology which can be employed to predict preterm birth. For this purpose, sparse autoencoder (SAE) based deep neural network (SAE-based DNN) is developed. The deep neural network has three layers including a stacked sparse autoencoder (SSAE) network with two hidden layers and one final softmax layer. To this end, the bursts of all 26 recordings of the publicly available TPEHGT DS database corresponding to uterine contraction intervals and non-contraction intervals (dummy intervals) were manually segmented. 20 features were extracted by two feature extraction algorithms including sample entropy and wavelet entropy. Afterwards, the SSAE network is adopted to learn high-level features from raw features by unsupervised learning. The softmax layer is added at the top of the SSAE network for classification. In order to verify the effectiveness of the proposed method, this study used 10-fold cross-validation and four indicators to evaluate classification performance. Experimental research results display that the performance of deep neural network can achieve Sensitivity of 98.2%, Specificity of 97.74%, and Accuracy of 97.9% in the publicly TPEHGT DS database. The performance of deep neural network outperforms the comparison models including deep belief networks (DBN) and hierarchical extreme learning machine (H-ELM). Finally, experimental research results reveal that the proposed method could be valid applied to semi-automatic identification of term and preterm uterine recordings.

关键词： EHG and TOCO signals sample entropy wavelet entropy stacked sparse autoencoder softmax

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Deep Neural Network for Automatic Classification of Pathological Voice Signals

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JOURNAL OF VOICE 2022年第2期36卷 288-288页

作者： Chen, Lili Chen, Junjiang Chongqing Jiaotong Univ Sch Mechatron & Vehicle Engn Chongqing 400074 Peoples R China Chongqing Survey Inst Chongqing Peoples R China

Objectives. Computer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN. Methods. In this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment. Results. Experimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest). Conclusions. The proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice

关键词： Deep neural network Automatic classification Pathological voice stacked sparse autoencoder Mel frequency cepstral coefficients

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A Deep Learning Architecture for P300 Detection with Brain-Computer Interface Application

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IRBM 2020年第1期41卷 31-38页

作者： Kundu, S. Ari, S. Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela 769008 Odisha India

In this paper, a brain-cdmputer interface (BCI) system for character recognition is proposed based on the P300 signal. A P300 speller is used to spell the word or character without any muscle movement. P300 detection is the first step to detect the character from the electroencephalogram (EEG) signal. The character is recognized from the detected P300 signal. In this paper, sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) based feature extraction methods are proposed for P300 detection. This work also proposes a fusion of deep-features with the temporal features for P300 detection. A SSAE technique extracts high-level information about input data. The combination of SSAE features with the temporal features provides abstract and temporal information about the signal. An ensemble of weighted artificial neural network (EWANN) is proposed for P300 detection to minimize the variation among different classifiers. To provide more importance to the good classifier for final classification, a higher weightage is assigned to the better performing classifier. These weights are calculated from the cross-validation test. The model is tested on two different publicly available datasets, and the proposed method provides better or comparable character recognition performance than the state-of-the-art methods. (C) 2019 AGBM. Published by Elsevier Masson SAS. All rights reserved.

关键词： Brain-computer interface Deep learning P300 sparse autoencoder stacked sparse autoencoder

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Rock mass type prediction for tunnel boring machine using a novel semi-supervised method

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MEASUREMENT 2021年 179卷 109545-109545页

作者： Yu, Honggan Tao, Jianfeng Qin, Chengjin Xiao, Dengyu Sun, Hao Liu, Chengliang Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Tunnel boring machine is extremely sensitive to geological changes, and the accurate prediction of geological conditions ahead of the tunnel face is helpful for safe and efficient tunneling. Since soft methods can use on-site data to predict geological conditions, they are getting more and more attention. However, there is an imbalance between the machine data and geological data, and current soft methods can only utilize limited machine data with geological labels, limiting the performance of the model. To make full use of the massive unlabeled data and limited labeled data, a novel semi-supervised method is proposed to establish the rock mass type prediction model. In the first step, twenty machine parameters are selected as inputs, and the data preprocessing is performed. Thereafter, a geological feature extractor is established based on the stacked sparse autoencoder and unlabeled machine data. Finally, a feature classifier is obtained based on the deep neural network and labeled geological features to realize the prediction of rock mass type. The on-site data collected from Mumbai metro tunnel was utilized to verify the effectiveness of the proposed method. The results indicate that the unsupervised stacked sparse autoencoder is capable of extracting geological features, and the proposed stacked sparse autoencoder and deep neural network-based semi-supervised method outperforms commonly adopted supervised methods. Its classification performance (F-measure) is 13.84%, 10.29%, 8.71%, 5.23% and 5.13% higher than the support vector machine-based, decision tree-based, K-nearest neighbor-based, random forest-based and deep neural network-based methods, respectively. Therefore, the proposed semi-supervised method can predict the rock mass types ahead of the tunnel face more accurately than the current supervised soft methods.

关键词： Rock mass type prediction Tunnel boring machine Semi-supervised learning stacked sparse autoencoder Data preprocessing

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Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning

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APPLIED ENERGY 2019年 233卷 930-942页

作者： Zhang, Zehan Li, Shuanghong Xiao, Yawen Yang, Yupu Shanghai Jiao Tong Univ Dept Automat Key Lab Minist Educ Syst Control & Informat Proc Shanghai 200240 Peoples R China Orient Secur Co Ltd Shares South Zhong Shan Rd 318 Shanghai 200010 Peoples R China

Fault diagnosis technology is a vital tool for ensuring the stability and durability of solid oxide fuel cell systems. Simultaneous faults are common problems in modern industrial systems. Many fault diagnosis methods have been successfully designed for solid oxide fuel cell systems, but they only address independent faults, and only a few researchers have studied simultaneous fault diagnosis. The design of a simultaneous fault diagnosis method for solid oxide fuel cell systems remains a huge challenge. This study introduces a deep learning technology into the simultaneous fault diagnosis for the solid oxide fuel cell system and proposes a novel simultaneous fault diagnosis method on the basis of a deep learning network called stacked sparse autoencoder. The proposed method can automatically capture the essential features from the original system variables, thereby consuming minimal time on heavily hand-crafted features. Moreover, massive unlabeled samples are fully utilized through the proposed method. Experimental results show that the proposed method can diagnose simultaneous faults with high accuracy requiring only a few independent fault samples and a minimal number of simultaneous fault samples. Comparisons between traditional machine learning methods and experimental results on training sets of different sizes verify the superiority of the proposed method. Deep learning provides an effective and promising approach for simultaneous fault diagnosis in the field of fuel cells.

关键词： Deep learning stacked sparse autoencoder Automated feature learning Simultaneous fault diagnosis SOFC system

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Automated Nuclei Detection in Serous Effusion Cytology with stacked sparse autoencoders 26

Automated Nuclei Detection in Serous Effusion Cytology with ...

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Baykal, Elif Dogan, Hulya Ercin, Mustafa Emre Ersoz, Safak Ekinci, Murat Karadeniz Tech Univ Bilgisayar Muhendisligi Bolumu Trabzon Turkey Karadeniz Tech Univ Tibbi Patol Bolumu Trabzon Turkey

ISBN: (纸本)9781538615010

Serous effusion is frequently encountered specimen type in (cyto)pathological assessment. However, this assessment is time-consuming and leads to variability among pathologists. The cell nuclei is seen as the corner stone for diagnostic purposes in automatic analysis of cytopathological images. In this paper, a stacked sparse autoencoder (SSAE) is proposed for nuclei detection in serous effusion cytology. SSAE is an unsupervised deep learning method which learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. High-level features, obtained via the autoencoder, are then subsequently fed to a softmax classifier which categorizes each patch as nuclei or non-nuclei. With a detection rate of 98.3% based on images of serous effusion cytology, proposed method shows good performance.

关键词： Serous effusion cytopathological assessment nuclei detection stacked sparse autoencoder unsupervised learning

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