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38th Chinese Control Conference (CCC)

作者： Xu, Zhong Mo, Wenxiong Wang, Yong Luo, Simin Liu, Tian Guangzhou Power Supply Bur Co Ltd Elect Power Test & Res Inst Guangzhou 510000 Peoples R China

ISBN: (纸本)9789881563972

Dissolved gas analysis (DGA) is an effective way to diagnose the internal faults of transformer. This paper proposes a deep belief sparse autoencoder (DBSAE), which can be performed on DGA data to detect the transformer fault. The proposed method is constructed by two layers of sparse autoencoder and one layer of back propagation network. DBSAE adopts the unsupervised training to collect the features of DGA raw data, and the back propagation network with the supervised training could realize the transformer faults diagnosis. The experiments are performed on a real DGA dataset. The experimental results verify the effectiveness of the proposed method, which can extract features from the original data, and achieve a superior correct differentiation rate which reached 95.4% in average accuracy on transformer fault diagnosis.

关键词： Transformer fault diagnosis dissolved gas analysis sparse autoencoder deep belief network

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Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a sparse autoencoder 18

Anomaly Detection in Network Traffic Using Dynamic Graph Min...

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18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 13th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Jia, Guanbo Miller, Paul Hong, Xin Kalutarage, Harsha Ban, Tao Queens Univ Belfast Ctr Secur Informat Technol Belfast Antrim North Ireland Robert Gordon Univ Sch Comp Sci & Digital Media Aberdeen Scotland Natl Inst Informat & Commun Technol Informat Secur Res Ctr Koganei Tokyo Japan

ISBN: (纸本)9781728127767

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected.

关键词： anomaly detection network traffic network security bipartite graph sparse autoencoder dynamic graph

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Extracting Activity Patterns from Altering Biological Networks: a sparse autoencoder Approach 32

Extracting Activity Patterns from Altering Biological Networ...

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32nd IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS)

作者： Szlobodnyik, Gergely Pazmany Peter Catholic Univ Dept Informat Technol & Bion Budapest Hungary

ISBN: (纸本)9781728122861

In this paper we address the problem of extracting activity patterns from biological networks that cannot be characterized in the form of a static graph, such as gene regulatory networks wherein the co-expression patterns of different genes may vary over time. A simple autoencoder architecture is applied in order to detect activity patterns appearing in several observational samples of the data. We apply Kullback-Leibler divergence based sparsity constraint on the hidden layer neurons, such a way the activity of a hidden layer unit can be associated to a particular activity pattern which can be extracted using the trained decoder function.

关键词： activity patterns network inference altering graph neural networks sparse autoencoder

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Fault Diagnosis Based on Batch-normalized Stacked sparse autoencoder

Fault Diagnosis Based on Batch-normalized Stacked Sparse Aut...

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第三十九届中国控制会议

作者： Liu Xiaozhi Gao Yang Yang Yinghua College of Information Science and Engineering Northeastern University

A fault diagnosis method based on batch-normalization stacked sparse autoencoder(SSAE) is presented in this paper. This paper use the autoencoder to extract features for fault diagnosis on account of its good performance in feature extraction. In order to improve the accuracy of the extracted features, this paper use a sparse representation which is a constraint during the encoding process. The multi-layer structure of autoencoder has an internal covariate shift problem and the generalization ability of the network is critical, batch normalization is employed before the activation function in each layer of the autoencoder network. And a stacked method is utilized to optimize network structure and reduce training difficulty. So the features of the original signal are extracted by the network based on the above method and the extracted features are placed in the classifier to identify different health states. For the purpose of fault diagnosis,this paper uses the proposed method to experiment with the bearing data set provided by Case Western Reserve University(CWRU). The experiment proves that the proposed method has a better fault diagnosis performance compared with other traditional methods.

关键词： Fault diagnosis sparse autoencoder Batch-normalization

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Improved sparse autoencoder based artificial neural network approach for prediction of heart disease

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Informatics in Medicine Unlocked 2020年 18卷

作者： Mienye, Ibomoiye Domor Sun, Yanxia Wang, Zenghui Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg 2006 South Africa Department of Electrical and Mining Engineering University of South Africa Florida 1709 South Africa

In this paper a two stage method is proposed to effectively predict heart disease. The first stage involves training an improved sparse autoencoder (SAE), an unsupervised neural network, to learn the best representation of the training data. The second stage involves using an artificial neural network (ANN) to predict the health status based on the learned records. The SAE was optimized so as to train an efficient model. The experimental result shows that the proposed method improves the performance of the ANN classifier, and is more robust as compared to other methods and similar scholarly works. © 2020 The Authors

关键词： ANN Deep learning Heart disease sparse autoencoder Unsupervised learning

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A Framework for Automatically Extracting Overvoltage Features Based on sparse autoencoder

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IEEE TRANSACTIONS ON SMART GRID 2018年第2期9卷 594-604页

作者： Chen, Kunjin Hu, Jun He, Jinliang Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

With the development of smart grid, it is of increasing significance to identify and cope with various types of overvoltages, faults and power quality disturbances effectively and automatically. In this paper, a framework for overvoltage identification and classification based on sparse autoencoder is proposed. By using single-layer and stacked sparse autoencoders, dimensionality reduction and automatic feature extraction of ferroresonance overvoltage waveforms in power distribution systems are achieved as an example, which does not require feature engineering to produce a series of features. Classification of different ferroresonance modes is then implemented with a softmax classifier, and favorable classification results are obtained after parameters of feature extraction and classifier models are determined. Application of the proposed framework in smart grids is discussed. The proposed framework provides a brand new idea for establishing a smart identification and classification system for overvoltages, which can be generalized to classification of faults and power quality disturbances.

关键词： sparse autoencoder softmax regression feature extraction dimensionality reduction ferroresonance overvoltage

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Supervised Learning via Unsupervised sparse autoencoder

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IEEE ACCESS 2018年 6卷 73802-73814页

作者： Liu, Jianran Li, Chan Yang, Wenyuan Minnan Normal Univ Fujian Key Lab Granular Comp & Applicat Zhangzhou 363000 Peoples R China Xiamen Univ Tan KahKee Coll Xiamen 363105 Peoples R China

Dimensionality reduction is commonly used to preprocess high-dimensional data, which is an essential step in machine learning and data mining. An outstanding low-dimensional feature can improve the efficiency of subsequent learning tasks. However, existing methods of dimensionality reduction mostly involve datasets with sufficient labels and fail to achieve effective feature vectors for datasets with insufficient labels. In this paper, an unsupervised multiple layered sparse autoencoder model is studied. Its advantage is that it reduces the reconstruction error as its optimization goal, with the resulting low-dimensional feature being reconstructed to the original dataset as much as possible. Therefore, the reduction of high-dimensional datasets to low-dimensional datasets is effective. First, the relationship among the reconstructed data, the number of iterations, and the number of hidden variables is explored. Second, the dimensionality reduction ability of the sparse autoencoder is proven. Several classical feature representation methods are compared with the sparse autoencoder on publicly available datasets, and the corresponding low-dimensional representations are placed into different supervised classifiers and the classification performances reported. Finally, by adjusting the parameters that might influence the classification performance, the parametric sensitivity of the sparse autoencoder is shown. The extensively low-dimensional feature classification experimental results demonstrated that the sparse autoencoder is more efficient and reliable than the other selected classical dimensional reduction algorithms.

关键词： Machine learning dimensionality reduction sparse autoencoder supervised learning feature representation

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Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第9期77卷 10521-10538页

作者： Zhang, Yu-Dong Zhang, Yin Hou, Xiao-Xia Chen, Hong 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 Dept Neurol Affiliated Hosp 1 Nanjing 210029 Jiangsu Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Hubei Peoples R China CUNY City Coll Dept Elect Engn New York NY 10031 USA

In order to detect the cerebral microbleed (CMB) voxels within brain, we used susceptibility weighted imaging to scan the subjects. Then, we used undersampling to solve the accuracy paradox caused from the imbalanced data between CMB voxels and non-CMB voxels. we developed a seven-layer deep neural network (DNN), which includes one input layer, four sparse autoencoder layers, one softmax layer, and one output layer. Our simulation showed this method achieved a sensitivity of 95.13%, a specificity of 93.33%, and an accuracy of 94.23%. The result is better than three state-of-the-art approaches.

关键词： Cerebral microbleed Deep neural network sparse autoencoder Voxelwise detection Accuracy paradox

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Post-fault prediction of transient instabilities using stacked sparse autoencoder

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ELECTRIC POWER SYSTEMS RESEARCH 2018年 164卷 243-252页

作者： Mahdi, Mohammed Genc, V. M. Istemihan Istanbul Tech Univ Dept Elect Engn TR-34469 Istanbul Turkey

Post-fault prediction of transient stability of power systems has a great impact on the performance of wide area monitoring, protection and control systems. Situational awareness capabilities of a power system are improved by fast detection of instabilities after severe fault occurrences. This allows sufficient time to take necessary corrective control actions. In this paper, a novel method based on stacked sparse autoencoder is proposed to predict the post-fault transient stability status of the power system directly after clearing the fault. A dataset is generated off-line to train a stacked sparse autoencoder, and then the trained stacked sparse autoencoder is used in an online application of predicting any transient instability. The stacked sparse autoencoder is fed by the inputs, which are specific points extracted from the fault-on voltage magnitude measurements collected from the phasor measurement units. The effectiveness of the proposed method is demonstrated and compared with the conventional approaches that adopt multilayer perceptrons or post-fault measurements as it is applied to the 127-bus WSCC test system and to the Turkish power system.

关键词： Deep learning Phasor measurement units Prediction sparse autoencoder Transient stability Wide area measurements

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

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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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