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Abnormal State Detection using Memory-augmented autoencoder technique in Frequency-Time Domain

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第2期18卷 348-369页

作者： Zhong, Haoyi Zhao, Yongjiang Lim, Chang Gyoon Chonnam Natl Univ Dept Comp Engn Yeosu 59626 South Korea

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end -to -end, unsupervised Frequency-Time Domain Memory -Enhanced autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short -Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

关键词： Abnormal State Detection Memory Augmentation autoencoder Frequency- Time Domain

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Cross-Domain Translation Learning Method Utilizing autoencoder Pre-Training for Super-Resolution of Radar Sparse Sensor Arrays

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IEEE ACCESS 2023年 11卷 61773-61785页

作者： Kim, Jong-Hoon Hong, Ic-Pyo Kongju Natl Univ Smart Nat Space Res Ctr Cheonan 31080 South Korea Kongju Natl Univ Dept Smart Informat Technol Engn Cheonan 31080 South Korea

This study proposes a convolutional neural network based image translation framework to faithfully realize the super-resolution of radar sensor sparse measurement. We exploited the autoencoder model able to learn a latent representation of input domain data via the iteration of encoding and decoding processes. Based on the belief that the low resolution data retains an essential measurement to fully represent the sparse information of the high resolution counterpart, we assumed that input (source domain of low-resolution denoted by X) and output (target domain of high-resolution denoted by Y) share a common latent code. On the other hand, from the observation that the recovery quality of Y-to-Y translation is far more favorable than the outcome from X-to-Y translation, we performed the pre-training for finding the latent code (z(y)) of the Y domain data, and subsequently the acquired z(y) serving as a label for predicting the latent code (z(x)) of X domain data. As a result, in our proposed network, encoder and decoder parts were trained with separate minimization strategies relative to respective loss functions. For the sake of verifying the effect of the pre-training scheme, we introduced hyper-parameters, alpha and beta, to regulate the training rate for the encoder and decoder parts respectively. As metrics for quantitative assessment, detection rate (DR), false alarm rate (FAR) and ROC curve were utilized. Our network set with 0.9 of alpha and 0.1 of beta showed a higher ROC curve plot across the whole range of DR and FAR than a basic autoencoder. DRs of the proposed network and comparative model were respectively 92.23% and 91.00% at FAR of 2%.

关键词： Super-resolution radar sensor arrays sparse measurement autoencoder pre-training latent representation image-to-image translation

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GAAE: a novel genetic algorithm based on autoencoder with ensemble classifiers for imbalanced healthcare data

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JOURNAL OF SUPERCOMPUTING 2023年第1期79卷 541-572页

作者： Ram, Pintu Kumar Kuila, Pratyay Natl Inst Technol Dept Comp Sci & Engn Sikkim 737139 India

With the advancement of artificial intelligence (AI) and machine learning techniques, the diseases can be early diagnosed and detected with the help of known healthcare data of similar diseases. However, the analysis of healthcare data becomes challenging due to the imbalance behavior of such data. Such behavior has a skewed nature and the samples of imbalanced dataset contain the majority and minority instances. The quantity of majority instances is much higher than that of the minority instances. Thus, the classifier always gets biased toward the majority instances while classifying an unknown instance. This is not a perfect deal by any of the classifiers especially for disease diagnosis. Therefore, it is important to balance the dataset by considering both the majority and minority class samples. In this context, Genetic Algorithm based on autoencoder (GAAE) model is proposed to process the imbalance data. Initially, an autoencoder is trained by the help of genetic operators and both the majority and minority samples. The chromosome is efficiently designed to represent an autoencoder. The error function is designed as the fitness function with the help of ensemble of classifiers. The optimized autoencoder generates the synthetic data of minority class to balance the data. Once the imbalanced data is balanced using the proposed GAAE, then the feature selection is done based on the correlation coefficient. Then various classifiers, multi-layer perceptron (MLP), k-nearest neighbor (k-NN), C4.5 decision tree (DT) and random forest (RF) are employed for the data classification. An extensive simulation is performed on the proposed approach and comparison is done with the existing approaches. It is observed that consideration of both the majority and minority samples to generate the synthetic data helps the classifiers to perform better. In addition, statistical analysis has also been performed.

关键词： Imbalanced data GAAE autoencoder Ensemble classifier Feature selection

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Practical autoencoder based anomaly detection by using vector reconstruction error

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Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection autoencoder Reconstruction error Machine learning

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Operational performance evaluation of bridges using autoencoder neural network and clustering

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SMART STRUCTURES AND SYSTEMS 2024年第3期33卷 189-199页

作者： Jiang, Huachen Xie, Liyu Fang, Da Wan, Chunfeng Gao, Shuai Yang, Kang Ding, Youliang Xue, Songtao Shanghai Key Lab Engn Struct Safety SRIBS Shanghai 200032 Peoples R China Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 210096 Peoples R China Shanghai Inst Technol Sch Railway Transportat Shanghai 201418 Peoples R China Tohoku Inst Technol Dept Architecture Sendai Miyagi 9828577 Japan

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicleinduced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

关键词： autoencoder early warning Gaussian mixture model strain measurement structural health monitoring

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AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年 125卷 103964-103964页

作者： Sharma, Abhibhav Singh, Buddha Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

Protein-protein interactions (PPIs) play a crucial role in biological processes of living organisms. Correct prediction of PPI can prove to be extremely valuable in probing protein functions which can aid in the development of new and powerful therapies for disease prevention. Many experimental studies have been previously performed to investigate PPIs. However, in-vitro techniques to investigate PPIs are resource-extensive and time-consuming. Although various in-silico approaches to predict PPI have been developed in recent years, they could be fallible in terms of accuracy and false-positive rate. To overcome these shortcomings, we propose a novel approach, AE-LGBM to predict the PPIs more accurately. It employs LightGBM classifier and utilizes the autoencoder, which is an artificial neural network, to efficiently produce lower-dimensional, discriminative, and noise-free features. We incorporate conjoint triad (CT) and Composition-Transition-Distribution (CTD) features into the AE-LGBM framework. On performing ten-fold cross-validation, the prediction accuracies obtained by AE-LGBM for Human and Yeast datasets are 98.7% and 95.4% respectively. AE-LGBM was further evaluated on independent datasets and has achieved excellent accuracies of 100%, 100%, 99.9%, 99.3%, 99.2% on E. coli, M. musculus, C. elegans, H. pylori and H. sapiens respectively. AE-LGBM has also obtained the best accuracy when tested over three important PPI networks namely single-core network (CD9), the multiple-core network (The Ras/Raf/MEK/ERK pathway) and the cross-connection network (Wnt Network). The outstanding generalization ability of AE-LGBM makes it a versatile, efficient, and robust PPIs prediction model.

关键词： Protein-protein interaction Prediction model Machine learning Neural network autoencoder LightGBM Accuracy PPI Protein

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ABAE: Auxiliary Balanced autoencoder for class-imbalanced semi-supervised learning

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PATTERN RECOGNITION LETTERS 2024年 182卷 118-124页

作者： Tang, Qianying Wei, Xiang Su, Qi Zhang, Shunli Beijing Jiaotong Univ Sch Software Engn 3 Shangyuancun Beijing 100044 Peoples R China

Semi -supervised learning has achieved extraordinary success in prevalent image -classification benchmarks. However, a class -balanced distribution that differs notably from real -world data distribution is required. In general, models trained under class-imbalanced semi -supervised learning conditions are severely biased towards the majority classes. To address this issue, we propose a novel framework called ABAE by implanting an Auxiliary Balanced autoencoder branch into existing semi -supervised learning algorithms. Considering that adaptive feature augmentation for different classes can alleviate confirmation bias, we devise a class -aware reconstruction loss to train the autoencoder module. To further smooth the output, we adopt a graph -based label propagation scheme at the end of the autoencoder. Extensive experiments on CIFAR-10/100-LT, SVHN-LT and Small demonstrate the effectiveness of ABAE.

关键词： Class-imbalanced learning Semi-supervised learning autoencoder Label propagation

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Graph dynamic autoencoder for fault detection

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CHEMICAL ENGINEERING SCIENCE 2022年 254卷

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

Dynamic information is a non-negligible part of time-correlated process data, and its full utilization can improve the performance of fault detection. Traditional dynamic methods concatenate the current process data with a certain number of previous process data into an extended vector before performing feature extraction. However, this simple way of using dynamic information inevitably increases the input dimensionality and it is inappropriate to treat previous process data as equally important. To address these problems, this paper proposes a novel nonlinear dynamic method, called graph dynamic autoencoder (GDAE), for fault detection. GDAE utilizes a graph structure to model the dynamic information between different data points. GDAE firstly embeds the current data point and previous data points as the features of the central node and its neighbors, respectively, then convolves the feature of the central node with the features of its neighbors to derive the updated feature for the central node, and finally, an encoder-decoder structure is adopted to extract the key low-dimensional feature. Due to the utilization of the graph structure, the extended high-dimensional vectors utilized by traditional dynamic fault detection methods are avoided in GDAE. Furthermore, with the dynamically constructed graph, GDAE is able to adaptively assign different weights to its neighbors by updating the adjacency matrix of the graph. Experimental results obtained from a numerical simulation and the Tennessee Eastman process illustrate the superiority of GDAE in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of GDAE can be found in https://***/luliu-fighting/Graph-Dynamic-autoencoder. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Process monitoring Dynamic fault detection Graph convolution autoencoder

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Graph autoencoder Anomaly Detection for E-Commerce Application by Contextual Integrating Contrast With Reconstruction and Complementarity

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1623-1630页

作者： Wu, Jinjie Qiu, Zhipeng Zeng, Zhixia Xiao, Ruliang Rida, Imad Zhang, Shi Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Peoples R China Univ Technol Compiegne Ctr Rech Royallieu Lab Biomecan & Bioingn UMR7338 F-60200 Compiegne France

Considering the personalized characteristics and behavior patterns of users, quickly and accurately identifying abnormal users is crucial in the realm of E-commerce applications. However, existing methods fall short in integrating local and global structural information. Meanwhile, contextual information can serve effectively as a supervisory signal for anomaly detection. This paper proposes a Graph autoencoder Anomaly Detection model, GAAD, that leverages context-integrated contrast and reconstruction complementarity. This model utilizes local information of target nodes and adopts a contrastive learning module based on graph neural networks to capture anomalies in the structure space;simultaneously, a context subgraph of the global information of the target node is constructed and anomalies in the attribute space are captured through the autoencoder-based reconstruction module. The GAAD model integrates two complementary modules, contrast and reconstruction, to enhance the accuracy of anomaly node detection. The experimental results showcase the superior performance of the proposed model over existing baseline methods across all seven benchmark datasets, thereby highlighting its efficiency in anomaly detection within E-commerce applications. Our code is available at https://***/jinjiewu2000/GAAD.

关键词： Anomaly detection Graph neural networks Context modeling Self-supervised learning Feature extraction Electronic commerce Decoding autoencoder graph neural network contextual integrating anomaly detection

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Minimum interpretation by autoencoder-based serial and enhanced mutual information production

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APPLIED INTELLIGENCE 2020年第8期50卷 2423-2448页

作者： Kamimura, Ryotaro Tokai Univ IT Educ Ctr 4-1-1 Kitakaname Hiratsuka Kanagawa 2591292 Japan

The present paper aims to propose an information-theoretic method for interpreting the inference mechanism of neural networks. The new method aims to interpret the inference mechanism minimally by disentangling complex information into simpler and easily interpretable information. This disentanglement of complex information can be realized by maximizing mutual information between input patterns and the corresponding neurons. However, because the use of mutual information has faced difficulty in computation, we use the well-known autoencoder to increase mutual information by re-interpreting the sparsity constraint, which is considered a device to increase mutual information. The computational procedures to increase mutual information are decomposed into the serial operation of equal use of neurons and specific responses to input patterns. The specific responses are realized by enhancing the results by the equal use of neurons. The method was applied to three data sets: the glass, office equipment, and pulsar data sets. With all three data sets, we could observe that, when the number of neurons was forced to increase, mutual information could be increased. Then, collective weights, or average collectively treated weights, showed that the method could extract the simple and linear relations between inputs and targets, making it possible to interpret the inference mechanism minimally.

关键词： autoencoder Minimum interpretation Enhanced information Generalization Interpretation Mutual information

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