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Low-rank reconstruction-based autoencoder for robust fault detection

CONTROL ENGINEERING PRACTICE 2022年第0期123卷

作者： Hu, Zhengwei Zhao, Haitao Peng, Jingchao East China Univ Sci & Technol Sch Informat Sci & Engn Automat Dept Shanghai 200237 Peoples R China

autoencoder (AE) has been widely used in multivariate statistical process monitoring (MSPM) and various AE-based methods have been applied in fault detection. Process data is often contaminated by outliers and measurement noise which may lead to the overfitting problem for AE-based methods. In this paper, a novel feature extraction method called low-rank reconstruction-based autoencoder (LRAE) is proposed for robust fault detection. LRAE decomposes the input into a combination of a low-rank data matrix and a noise matrix. By penalizing the rank of the data matrix, LRAE separates the low-rank clean data from the contaminated process data. Instead of directly reconstructing the loss between the input data and the output data, we design a low-rank reconstruction strategy, i.e. reconstruct the loss between the low-rank clean data and the output of the AE. The proposed LRAE can be trained end-to-end by jointly optimizing an AE and a low-rank approximation. LRAE is a nonlinear method which can tackle the complicated process data better than the linear methods such as principal component analysis (PCA). Moreover, the optimization of the low-rank approximation provides the robustness of LRAE to reconstruct the clean data in the output layer when the input process data is contaminated. After training, the features of the hidden layer can be computed for further fault detection. Extensive experiments demonstrate that LRAE outperforms traditional fault detection methods, including PCA, robust principal component analysis (RPCA), kernel principal component analysis (KPCA), AE, and denoising autoencoder (DAE). Especially, LRAE provides more robust results when the process data suffer from outliers and measurement noise.

关键词： Process monitoring Robust fault detection Low-rank optimization autoencoder

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Mobile applications identification using autoencoder based electromagnetic side channel analysis

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2023年 75卷

作者： Zhang, Jinghui Liang, Boxi Zhang, Hancheng Zhang, Wei Ling, Zhen Yang, Ming Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China China United Network Commun Grp Co Ltd Digital Dept Beijing Peoples R China

Various applications are deployed on mobile smart devices in almost every situations of our life, while in some of these situations sensitive applications are strictly prohibited, such as cameras in cinemas and browsers in examination halls. Real-time recognition of applications running on mobile smart devices is of great significance in these cases. However, most of the existing technologies have the limitation that they require system permissions to obtain the running application list which is banned by mainstream mobile operating systems. Noting that the launch of a certain application will emit a unique pattern of magnetic field, we introduce magnetic field side channel analysis to recognize running applications. However, magnetic field side channel analysis is challenging since it is hard to extract features from magnetic field data without domain experts. Besides, real-time applications identification demands accurate detection of applications launching. To overcome these challenges, we extract robust depth features using autoencoder and implement online application recognition by introducing finite-state machine to identify the application launch window from raw data. The proposed method is evaluated by recognizing 1000 different applications in real environment. The experiment results show that the proposed method is feasible and effective in real-time application identification.

关键词： Mobile smart devices Applications identification Magnetic field Side channel analysis autoencoder Finite-state machine

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Feature extraction of linear separability using robust autoencoder with distance metric

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期44卷 7589-7598页

作者： Wei, Pingping Zhang, Xin Yunnan Technol & Business Univ Sch Intelligent Sci & Engn Kunming Yunnan Peoples R China

This paper proposes a robust autoencoder withWasserstein distance metric to extract the linear separability features from the input data. To minimize the difference between the reconstructed feature space and the original feature space, using Wasserstein distance realizes a homeomorphic transformation of the original feature space, i.e., the so-called the reconstruction of feature space. The autoencoder is used for features extraction of linear separability in the reconstructed feature space. Experiment results on real datasets show that the proposed method reaches up 0.9777 and 0.7112 on the low-dimensional and high-dimensional datasets in extracted accuracies, respectively, and also outperforms competitors. Results also confirm that compared with feature metric-based methods and deep network architectures-based method, the linear separabilities of those features extracted by distance metric-based methods win over them. More importantly, the linear separabilities of those features obtained by evaluating distance similarity of the data are better than those obtained by evaluating feature importance of data. We also demonstrate that the data distribution in the feature space reconstructed by a homeomorphic transformation can be closer to the original data distribution.

关键词： autoencoder distance measure feature extraction linear separability

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DSVD-autoencoder: A scalable distributed privacy-preserving method for one-class classification

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期36卷 177-199页

作者： Fontenla-Romero, Oscar Perez-Sanchez, Beatriz Guijarro-Berdinas, Bertha Univ A Coruna CITIC Fac Informat Dept Comp Sci & Informat Technol Campus Elvina S-N La Coruna 15071 Spain

One-class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. autoencoder is the type of neural network that has been widely applied in these one-class problems. In the Big Data era, new challenges have arisen, mainly related with the data volume. Another main concern derives from Privacy issues when data is distributed and cannot be shared among locations. These two conditions make many of the classic and brilliant methods not applicable. In this paper, we present distributed singular value decomposition (DSVD-autoencoder), a method for autoencoders that allows learning in distributed scenarios without sharing raw data. Additionally, to guarantee privacy, it is noniterative and hyperparameter-free, two interesting characteristics when dealing with Big Data. In comparison with the state of the art, results demonstrate that DSVD-autoencoder provides a highly competitive solution to deal with very large data sets by reducing training from several hours to seconds while maintaining good accuracy.

关键词： autoencoder big data distributed learning neural network one-class classification privacy-preserving singular value decomposition

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Towards an Interpretable autoencoder: A Decision-Tree-Based autoencoder and its Application in Anomaly Detection

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2023年第2期20卷 1048-1059页

作者： Aguilar, Diana Laura Medina-Perez, Miguel Angel Loyola-Gonzalez, Octavio Choo, Kim-Kwang Raymond Bucheli-Susarrey, Edoardo Tecnol Monterrey Atizapan 52926 Estado De Mexic Mexico Altair Management Consultants Madrid 28006 Spain Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA

The importance of understanding and explaining the associated classification results in the utilization of artificial intelligence (AI) in many different practical applications (e.g., cyber security and forensics) has contributed to the trend of moving away from black-box / opaque AI towards explainable AI (XAI). In this article, we propose the first interpretable autoencoder based on decision trees, which is designed to handle categorical data without the need to transform the data representation. Furthermore, our proposed interpretable autoencoder provides a natural explanation for experts in the application area. The experimental findings show that our proposed interpretable autoencoder is among the top-ranked anomaly detection algorithms, along with one-class Support Vector Machine (SVM) and Gaussian Mixture. More specifically, our proposal is on average 2% below the best Area Under the Curve (AUC) result and 3% over the other Average Precision scores, in comparison to One-class SVM, Isolation Forest, Local Outlier Factor, Elliptic Envelope, Gaussian Mixture Model, and eForest.

关键词： Interpretable artificial intelligence autoencoder decision tree anomaly detection explainable artificial intelligence (XAI)

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Deep autoencoder-Based Z-Interference Channels With Perfect and Imperfect CSI

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第2期72卷 861-873页

作者： Zhang, Xinliang Vaezi, Mojtaba Villanova Univ Dept Elect & Comp Engn Villanova PA 19085 USA

A deep autoencoder (DAE)-based structure for end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimizes the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes. This brings further gain compared to standard uniform constellations such as quadrature amplitude modulation. The proposed structure is then extended to work with imperfect channel state information (CSI). The CSI imperfection due to both the estimation and quantization errors are examined. The performance of the DAE-ZIC is compared with two baseline methods, i.e., standard and rotated constellations. The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI. Simulation results show that the improvement is achieved in all interference regimes (weak, moderate, and strong) and consistently increases with the signal-to-noise ratio (SNR). For instance, more than an order of magnitude BER reduction is obtained with respect to the most competitive conventional method at weak interference when SNR > 15 dB$ and two bits per symbol are transmitted. The improvements reach about two orders of magnitude when quantization error exists, indicating that the DAE-ZIC is more robust to the interference compared to the conventional methods.

关键词： Interference channel Z-interference imperfect CSI autoencoder constellation design

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Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第24期28卷 31084-31096页

作者： Tercan, Emre Atasever, Umit Haluk Gen Directorate Highways Dept Survey Project & Environm 13th Reg TR-07090 Antalya Turkey Erciyes Univ Fac Engn Dept Geomat Engn TR-38039 Kayseri Turkey

The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface areas tend to decrease. It is necessary for obtain baseline datasets such as surface areas and boundaries of water bodies with high accuracy, effectively, economically, and practically by using satellite images in terms of management and planning of lakes. Extracting surface areas of water bodies using image classification algorithms and high-resolution RGB satellite images and evaluating the effectiveness of different image classification algorithms have become an important research domain. In this experimental study, eight different machine learning-based classification approaches, namely, k-nearest neighborhood (kNN), subspaced kNN, support vector machines (SVMs), random forest (RF), bagged tree (BT), Naive Bayes (NB), and linear discriminant (LD), have been utilized to extract the surface areas of lakes. Lastly, autoencoder (AE) classification algorithm was applied, and the effectiveness of all those algorithms was compared. Experimental studies were carried out on three different lakes (Hazar Lake, Salda Lake, Manyas Lake) using high-resolution Turkish RASAT RGB satellite images. The results indicated that AE algorithm obtained the highest accuracy values in both quantitative and qualitative analyses. Another important aspect of this study is that Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI) metrics that can evaluate close to human perception are used for comparison. With this application, it has been shown that overall accuracy calculated from test data may be inadequate in some cases by using SSIM, UIQI, mean squared error (MSE), peak signal to noise ratio (PSNR), and Cohen's KAPPA metrics. In the last application, the robustness of AE was examined with boxplots.

关键词： Machine learning autoencoder Remote sensing Image classification Water body extraction RASAT Environmental monitoring Lake management

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The Hybrid Trip Destination Prediction Model of Vehicles Based on autoencoder and High-Order Interaction Features

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 8443-8451页

作者： He, Yuchu Jia, Zhijuan Hu, Mingsheng Zhang, Geng Dong, Hanjie Zhengzhou Normal Univ Sch Informat Sci & Technol Zhengzhou 450044 Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng 475001 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Peoples R China

Through the intelligent vehicles trip data collection, processing and analysis, so as to predict the vehicles trip destination, this technology can improve the user's driving experience and the city traffic conditions. The vehicles dispatching system can also judge the real-time road conditions according to the prediction results and plan a more reasonable and efficient driving route, which is of great significance to urban traffic planning and urban construction planning. However, the amount of information in the vehicles data is less, which cannot meet the training needs of some artificial intelligence models. In addition, due to communication technology and other issues, there is a certain degree of deviation between the GPS data of the vehicles and the real data. The previous vehicles destination prediction model did not well eliminate the negative impact of this deviation. In addition, the previous model linearly adds the characteristics of each vehicles's trip data, which cannot reflect the complex rules of the vehicles's trip destination. Therefore, to address the above-mentioned drawbacks, we propose a novel vehicle trip destination prediction method named Hybrid Trip Destination Prediction Model of Vehicle Based on autoencoder and High-Order Interaction Features (HAHIF). The HAHIF model extracts robust hidden features using the autoencoder model and considers the second-order association between them using a factorization machine to improve its superiority and effectiveness. Compared with mainstream benchmarks, the HAHIF model has an MSE value of 0.096, an RMSE value of 0.427, and a MAE value of 0.203 on the public dataset, both of which are the first place, which verifies that the HAHIF model has good predictive ability.

关键词： Trip destination prediction vehicles trajectory mining neural networks autoencoder

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NEUROCOMPUTING 2022年第0期490卷 482-494页

作者： Li, Nanjun Chang, Faliang Liu, Chunsheng Shandong Univ Sch Control Sci & Engn 17923 Jingshi Rd Jinan 250061 Peoples R China

Automatic detection of human-related anomalous events in surveillance videos is challenging, owing to unclear definition of anomalies and insufficiency of training data. Generally, the irregular human motion patterns can be regarded as human-related abnormal events. Therefore, we propose a novel method to operate directly on sequences of human skeleton graphs for discovering the normal patterns of human motion. The sequence of skeleton graphs is decomposed into two sub-components: global movement and local posture sequences. The global component is utilized to compute local component. The local component sequences are then input to our network for capturing normal spatial-temporal motion patterns of human skeleton. Our network is established on a Spatial-temporal Graph Convolutional autoencoder (ST-GCAE) and embedded with Long Short-Term Memory (LSTM) network in hidden layers for exploring the temporal cues, which is thus called Spatial-temporal Graph Convolutional autoencoder with Embedded Long Short-Term Memory Network (STGCAE-LSTM). Different from traditional autoencoder, STGCAE-LSTM owns a single-encoder-dual-decoder architecture, which is capable of reconstructing the input and predicting the unseen future simultaneously. Then, samples that deviate from normal patterns are detected as anomalies with fusion of reconstruction and prediction errors. Experimental results on four challenging datasets demonstrate advantages of our method over other state-of-the-art algorithms. (c) 2021 Elsevier B.V. All rights reserved.

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BLER performance evaluation of an enhanced channel autoencoder

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COMPUTER COMMUNICATIONS 2021年 176卷 173-181页

作者： Njoku, Judith Nkechinyere Morocho-Cayamcela, Manuel Eugenio Lim, Wansu Kumoh Natl Inst Technol Dept Aeronaut Mech & Elect Convergence Engn Gumi South Korea Yachay Tech Univ Sch Math & Computat Sci San Miguel De Urcuqui Ecuador

The concept of using autoencoders (AEs) to represent wireless communication systems as an end-to-end reconstruction task that optimizes the transmitter and receiver components simultaneously in a single process has attracted the attention of wireless practitioners worldwide. This is attributable to the flexibility, and convenience of representing complex channel models. However, owing to the characteristics of deep neural networks (DNNs), as the AE learns the representation of the channel, overfitting limits its performance. In this paper, we propose RegAE, a regularized DNN architecture that overcomes the overfitting limitation in AEs and reduces their training complexity, which are characteristics of models with higher dimensions. We demonstrate that RegAE improves the block error rate (BLER) as compared with equivalent models from the literature. Thereby, it achieves a performance (1) better than that of a 4/7 rate Hamming code with a 16 phase-shift keying (16PSK) modulation under an additive white Gaussian noise (AWGN) channel, (2) comparable to that of a 4/7 rate maximum likelihood decoding (MLD) with a E-b/N-0 range from 1 dB to 5 dB, and (3) equivalent to that of an uncoded binary phase-shift keying (BPSK) modulation over a E-b/N-0 range from 0 dB to 10 dB.

关键词： autoencoder Deep learning Neural network Wireless communications Modulation End-to-end learning

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