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Open-circuit fault diagnosis of power rectifier using sparse autoencoder based deep neural network

NEUROCOMPUTING 2018年 311卷 1-10页

作者： Xu, Lin Cao, Maoyong Song, Baoye Zhang, Jiansheng Liu, Yurong Alsaadi, Fuad E. Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Yangzhou Univ Dept Math Yangzhou 225002 Jiangsu Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia

This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN). Firstly, some preliminaries on SAE-based DNN are briefly introduced to automatically learn the representative fault features from the raw fault signals. Then, a novel strategy is developed to design the structure of the SAE-based DNN, by which the depth and hidden neurons of the SAE-based DNN could be regularly determined to extract the features of input signals. Furthermore, the fault model and system framework are presented to diagnose the open-circuit fault of the three-phase full-bridge rectifier. Finally, the effectiveness of the developed novel strategy is verified by the results of simulation experiments, and the superiority of the novel SAE-based DNN is evaluated by comparing with other frequently used approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Fault diagnosis Power rectifier sparse autoencoder Deep neural network Feature extraction

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Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate

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JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 4412-4435页

作者： Huang, Jianying Yang, Seunghyeok Li, Jinhui Oh, Jeill Kang, Hoon Chung Ang Univ Sch Elect & Elect Engn 84 Heukseok Ro Seoul 06974 South Korea Chung Ang Univ Dept Civil & Environm Engn 84 Heukseok Ro Seoul 06974 South Korea

Sanitary sewer overflows caused by excessive rainfall derived infiltration and inflow is the major challenge currently faced by municipal administrations, and therefore, the ability to correctly predict the wastewater state of the sanitary sewage system in advance is especially significant. In this paper, we present the design of the sparse autoencoder-based Bidirectional long short-term memory (SAE-BLSTM) network model, a model built on sparse autoencoder (SAE) and Bidirectional long short-term memory (BLSTM) networks to predict the wastewater flow rate in a sanitary sewer system. This network model consists of a data preprocessing segment, the SAE network segment, and the BLSTM network segment. The SAE is capable of performing data dimensionality reduction on high-dimensional original input feature data from which it can extract sparse potential features from the aforementioned high-dimensional original input feature data. The potential features extracted by the SAE hidden layer are concatenated with the smooth historical wastewater flow rate features to create an augmented previous feature vector that more accurately predicts the wastewater flow rate. These augmented previous features are applied to the BLSTM network to predict the future wastewater flow rate. Thus, this network model combines two kinds of abilities, SAE's low-dimensional nonlinear representation for original input feature data and BLSTM's time series prediction for wastewater flow rate. Then, we conducted extensive experiments on the SAE-BLSTM network model utilizing the real-world hydrological time series datasets and employing advanced SVM, FCN, GRU, LSTM, and BLSTM models as comparison algorithms. The experimental results show that our proposed SAE-BLSTM model consistently outperforms the advanced comparison models. Specifically, we selected a 3 months period training dataset in our dataset to train and test the SAE-BLSTM network model. The SAE-BLSTM network model yielded the lowest RMSE, MAE

关键词： Time series prediction sparse autoencoder Bidirectional long short-term memory Data dimensionality reduction Sanitary sewer overflows (SSOs) Rainfall derived infiltration and inflow (RDII)

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Room Response Equalization of Non-Minimum Phase Systems Using Kautz Filter and sparse autoencoder: A Hybrid Approach

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IETE JOURNAL OF RESEARCH 2023年第12期69卷 9251-9265页

作者： Chaudhuri, Sayanti Dey, Debangshu Munshi, Sugata Jadavpur Univ Dept Elect Engn Kolkata WB India

Room Response Equalization (RRE) systems play a vital role in enhancing the hearing experience in different real-time application areas such as cinema theatres, home theatres, hearing aid implementation, car hi-fi systems, etc. Thenceforth, RRE using digital signal processing techniques has been a matter of great interest to researchers for a long time. RRE is intended to provide a listener with a profound audio experience alike the original audio signal. Conventional filtering approaches have been used by several researchers to perform room equalization. However, this article presents a novel technique by cascading a Kautz filter and a sparse autoencoder in performing room equalization. Kautz filters belong to a category of fixed pole IIR filters that use least-square (LS) minimization to generate orthonormal tap-output impulse responses. This LS approximated signal is further trained by a sparse autoencoder, which provides filter coefficients, iteratively optimized, to minimize a cost function. The cost function is considered here in terms of the reconstruction error function. The performance of this hybrid network of equalization is evaluated in terms of mean square error and spectral deviation measures. Computational results show that this hybrid approach yields better results both qualitatively and quantitatively in comparison with the other filtering techniques.

关键词： Kautz filter Non-minimum phase systems Nyquist criterion Room response equalization sparse autoencoder Spectral deviation measure

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Deep Learning Approach Combining sparse autoencoder With SVM for Network Intrusion Detection

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IEEE ACCESS 2018年 6卷 52843-52856页

作者： Al-Qatf, Majjed Yu Lasheng Al-Habib, Mohammed Al-Sabahi, Kamal Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

Network intrusion detection systems (NIDSs) provide a better solution to network security than other traditional network defense technologies, such as firewall systems. The success of NIDS is highly dependent on the performance of the algorithms and improvement methods used to increase the classification accuracy and decrease the training and testing times of the algorithms. We propose an effective deep learning approach, self-taught learning (STL)-IDS, based on the STL framework. The proposed approach is used for feature learning and dimensionality reduction. It reduces training and testing time considerably and effectively improves the prediction accuracy of support vector machines (SVM) with regard to attacks. The proposed model is built using the sparse autoencoder mechanism, which is an effective learning algorithm for reconstructing a new feature representation in an unsupervised manner. After the pre-training stage, the new features are fed into the SVM algorithm to improve its detection capability for intrusion and classification accuracy. Moreover, the efficiency of the approach in binary and multiclass classification is studied and compared with that of shallow classification methods, such as J48, naive Bayesian, random forest, and SVM. Results show that our approach has accelerated SVM training and testing times and performed better than most of the previous approaches in terms of performance metrics in binary and multiclass classification. The proposed STL-IDS approach improves network intrusion detection and provides a new research method for intrusion detection.

关键词： Network security network intrusion detection system deep learning sparse autoencoder SVM self-taught learning NSL-KDD

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Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier

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MATHEMATICAL BIOSCIENCES 2019年 311卷 103-108页

作者： Yang Qing Jia Cangzhi Li Taoying Dalian Maritime Univ Inst Environm Syst Biol Coll Environm & Engn 1 Linghai Rd Dalian 116026 Peoples R China Dalian Maritime Univ Sch Sci 1 Linghai Rd Dalian 116026 Peoples R China Dalian Maritime Univ Dept Maritime Econ & Management 1 Linghai Rd Dalian 116026 Peoples R China

Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore, it is vital to construct a high prediction performance model for identifying aptamer-target interacting pairs. In this study, a novel ensemble method is presented to predict aptamer-protein interacting pairs by integrating sequence characteristics derived from aptamers and the target proteins. The features extracted for aptamers were the compositions of amino acids and pseudo K-tuple nucleotides. In addition, a sparse autoencoder was used to characterize features for the target protein sequences. To remove redundant features, gradient boosting decision tree (GBDT) and incremental feature selection (IFS) methods were used to obtain the optimum combination of sequence characters. Based on 616 selected features, an ensemble of three sub-support vector machine (SVM) classifiers was used to construct our prediction model. Evaluated on an independent dataset, our predictor obtained an accuracy of 75.7%, Matthew's Correlation Coefficient of 0.478, and Youden's Index of 0.538, which were superior to the values reached using other existing predictors. The results show that our model can be used to distinguishing novel aptamer-protein interacting pairs and revealing the interrelation between aptamers and proteins.

关键词： Amino acid composition Machine learning sparse autoencoder Ensemble learning Feature selection

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Early Warning of Loss and Kick for Drilling Process Based on sparse autoencoder With Multivariate Time Series

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第11期19卷 11019-11029页

作者： Zhang, Zheng Lai, Xuzhi Du, Sheng Yu, Wanke Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

Complicated geological environments lead to a high risk of drilling incidents. Early warning of loss and kick for the drilling process is essential to ensure process safety. On account of the nonlinear and temporal correlation of drilling parameters, an early warning method for loss and kick based on sparse autoencoder with multivariate time series is proposed. The sparse autoencoder is utilized for multivariate time series abnormality detection of the drilling process. Abnormal drilling parameter isolation is performed through contribution analysis. Reconstruction analysis and time series segmentation approaches are integrated for abnormal time series trend evaluation. The characteristic of drilling parameters under normal operation learned by the sparse autoencoder and the property of the original time series are taken into account. The final early warning result can be obtained through expert rules based on the trend evaluation result. Case studies are presented based on the data from an actual drilling project. The experiment result shows the effectiveness of the proposed method.

关键词： Drilling Time series analysis Market research Principal component analysis Time measurement Hydraulic systems Geologic measurements Drilling process early warning loss and kick multivariate time series sparse autoencoder

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Unifying attentive sparse autoencoder with neural collaborative filtering for recommendation

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INTELLIGENT DATA ANALYSIS 2022年第4期26卷 841-857页

作者： Zhang, Yihao Zhao, Chu Yuan, Meng Chen, Mian Liu, Xiaoyang Chongqing Univ Technol Sch Artificial Intelligence Chongqing Peoples R China Chongqing Univ Technol Sch Comp & Engn Chongqing Peoples R China

The autoencoder network has been proven to be one of the powerful techniques for recommender systems. Currently, the ways of utilizing autoencoder in recommender systems can be divided into two categories: modeling user-item interaction rely solely on autoencoder and integrating autoencoder with other models. Most existing methods based on autoencoder assume that all features of model's input are equally the same contributing to the final prediction, which can be regarded as attention weight vectors;however, this hypothesis is not reliable, especially when exploring users' interaction frequency with different items. Moreover, combining autoencoder with traditional methods, the usual strategy is to leverage a linear kernel of the inner product of user and item vectors to predict user preferences, which will lead to insufficient expression power and hurt the performance of recommendation when facing data sparsity and cold start problems. To tackle the above two problems, we propose a novel hybrid deep learning model for top-n recommendation, called attentive stacked sparse autoencoder (A-SAERec), which can capture attention weights vector of a user for items, and then combined with the neural matrix factorization to improve the performance of recommender model. Extensive experiments on four real-world datasets show that our A-SAERec algorithm has significant improvements over state-of-the-art algorithms.

关键词： Attention mechanism sparse autoencoder collaborative filtering recommender systems

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Automated Detection and Localization of Myocardial Infarction With staked sparse autoencoder and TreeBagger

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IEEE ACCESS 2019年 7卷 70634-70642页

作者： Zhang, Jieshuo Lin, Feng Xiong, Peng Du, Haiman Zhang, Hong Liu, Ming Hou, Zengguang Liu, Xiuling Hebei Univ Coll Elect & Informat Engn Key Lab Digital Med Engn Hebei Prov Baoding 071000 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Hebei Univ Affiliated Hosp Baoding 071000 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Novel techniques in deep learning networks are proposed for the staked sparse autoencoder (SAE) and the bagged decision tree (TreeBagger), achieving significant improvement in detection and localization of myocardial infarction (MI) from single-lead electrocardiograph (ECG) signals. With our layer-wise training strategies, the SAE-based diagnostic feature extraction network can automatically and steadily extract the deep distinguishing diagnostic features of the single-lead ECG signals and avoid the vanishing gradient problem. This feature extraction network is formed by stacking shallow SAEs. In addition, to automatically learn the stable distinctive feature expression of the label-less input ECG signals, this feature extraction network adopts unsupervised learning. Moreover, TreeBagger classifier can optimize the results of multiple decision trees to more accurately detect and localize MI. The experiment and verification datasets include healthy controls, various types of MI with anterior, anterior lateral, anterior septal, anterior septal lateral, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral, from PTB diagnostic ECG database. The evaluation results show that the new techniques can effectively and accurately detect and localize the MI pathologies. For MI detection, the accuracy, the sensitivity, and the specificity rates achieve as high as 99.90%, 99.98%, and 99.52%, respectively. For MI localization, we obtain consistent results with the accuracy of 98.88%, sensitivity 99.95%, and specificity 99.87%. The comparative studies are conducted with the state-of-the-art techniques, and significant improvements by our methods are presented in the context. Success in the development of the accurate and comprehensive tool greatly helps the cardiologists in detection and localization of the single-lead ECG signals of MI.

关键词： Electrocardiograph myocardial infarction sparse autoencoder bagged decision tree deep learning networks

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Clutter Removal in Through-the-Wall Radar Imaging Using sparse autoencoder With Low-Rank Projection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第2期59卷 1118-1129页

作者： Tivive, Fok Hing Chi Bouzerdoum, Abdesselam Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia Hamad Bin Khalifa Univ Div Informat & Comput Technol Coll Sci & Engn Doha Qatar

Through-the-wall radar imaging is a sensing technology that can be used by first responders to see through obscure barriers during search-and-rescue missions or deployed by law enforcement and military personnel to maintain situational awareness during tactical operations. However, the strong reflections from the front wall and other obstacles render the detection of stationary targets very difficult. In this article, a learning-based approach is proposed to mitigate the effect of the wall and background clutter. A sparse autoencoder with a low-rank projection is developed to mitigate the wall clutter and recover the target signal. The weights of the proposed autoencoder are determined by solving an augmented Lagrange multiplier optimization problem, and the regularization parameters are estimated using the Bayesian optimization technique. Experiments using real data from a stepped-frequency radar were conducted to illustrate its effectiveness for wall clutter removal. The results show that the proposed method achieves superior performance compared with the existing approaches.

关键词： Clutter Radar antennas Radar imaging Antenna arrays Radar clutter Training Clutter removal low-rank (LR) projection sparse autoencoder through-the-wall radar imaging (TWRI)

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Fault diagnosis of air-conditioning refrigeration system based on sparse autoencoder

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2019年第4期14卷 487-492页

作者： Wang, Zhiyi Zhong, Jiachen Li, Jingfan Xia, Cui Zhejiang Sci Tech Univ Sch Civil Engn & Architecture Hangzhou 310018 Peoples R China

To overcome the drawbacks of using supervised learning to extract fault features for classification and low nonlinearity of the features inmost of current fault diagnosis of air-conditioning refrigeration system, sparse autoencoder (SAE) is presented to extract fault features that are used as the input to the classifier and to achieve fault diagnosis for air-conditioning refrigeration system. The SAE structure is tuned by adjusting the number of hidden layers and nodes to build the optimal model, which is compared with the fault diagnosis model based on support vector machine. Results indicate that the indexes of the model combined with SAE, such as accuracy, precision and recall, are all improved, especially for the faults with high complexity. Besides, SAE shows high generalization ability with small-scale sample data and high efficiency with large-scale data. Obviously, the use of SAE can effectively optimize the diagnosis performance of the classifier.

关键词： sparse autoencoder feature extraction air-conditioning refrigeration system fault diagnosis

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