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Modified autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning

IEEE ACCESS 2020年 8卷 101824-101833页

作者： Merrill, Nicholas Eskandarian, Azim Virginia Tech Dept Mech Engn Blacksburg VA 24060 USA

The autoencoder (AE) is a fundamental deep learning approach to anomaly detection. AEs are trained on the assumption that abnormal inputs will produce higher reconstruction errors than normal ones. In practice, however, this assumption is unreliable in the unsupervised case, where the training data may contain anomalous examples. Given sufficient capacity and training time, an AE can generalize to such an extent that it reliably reconstructs anomalies. Consequently, the ability to distinguish anomalies via reconstruction errors is diminished. We respond to this limitation by introducing three new methods to more reliably train AEs for unsupervised anomaly detection: cumulative error scoring (CES), percentile loss (PL), and early stopping via knee detection. We demonstrate significant improvements over conventional AE training on image, remote-sensing, and cybersecurity datasets.

关键词： Training Anomaly detection Image reconstruction Machine learning Training data Reliability Data models Hyperspectral HSI deep learning anomaly detection unsupervised autoencoder outlier detection condition monitoring

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Convolutional neural network with autoencoder-assisted multiclass labelling for seizure detection based on scalp electroencephalography

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年 125卷 104016-104016页

作者： Takahashi, Hirokazu Emami, Ali Shinozaki, Takashi Kunii, Naoto Matsuo, Takeshi Kawai, Kensuke Univ Tokyo Grad Sch Informat Sci & Technol Dept Mechanoinformat Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Univ Tokyo Res Ctr Adv Sci & Technol Meguro Ku 4-6-1 Komaba Tokyo 1538904 Japan Natl Inst Informat & Commun Technol CiNet Meguro Ku 1-4 Yamadaoka Suita Osaka 5650871 Japan Univ Tokyo Grad Sch Med Dept Neurosurg Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Tokyo Metropolitan Neurol Hosp 2-6-1 Musadhidai Fuchu Tokyo 1830042 Japan Jichi Med Univ Dept Neurosurg 3311-1 Yakushiji Shimotsuke Tochigi 3290498 Japan

Objective: In long-term video-monitoring, automatic seizure detection holds great promise as a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) designed to process images of EEG plots demonstrated high performance for seizure detection, but still has room for reducing the false-positive alarm rate. Methods: We combined a CNN that processed images of EEC plots with patient-specific autocncoders (AE) of EEC signals to reduce the false alarms during seizure detection. The AE automatically logged abnormalities, i.e., both seizures and artifacts. Based on seizure logs compiled by expert epileptologists and errors made by AE, we constructed a CNN with 3 output classes: seizure, non-seizure-but-abnormal, and non-seizure. The accumulative measure of number of consecutive seizure labels was used to issue a seizure alarm. Results: The second-by-second classification performance of AE-CNN was comparable to that of the original CNN. False-positive seizure labels in AF-CNN were more likely interleaved with "non-seizure-but-abnormal" labels than with true-positive seizure labels. Consequently, "non-seizure-but-abnormal" labels interrupted runs of false-positive seizure labels before triggering an alarm. The median false alarm rate with the AE-CNN was reduced to 0.034 h(-1), which was one-fifth of that of the original CNN (0.17 h(-1)). Conclusions: A label of "non-seizure-but-abnormal" offers practical benefits for seizure detection. The modification of a CNN with an AE is worth considering because Alis can automatically assign "non-seizure-but-abnormal" labels in an unsupervised manner with no additional demands on the time of the epileptologist.

关键词： autoencoder Convolutional neural network Electroencephalography Epilepsy Seizure Video-EEG monitoring

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A fusion autoencoder model and piecewise anomaly index for aero-engine fault diagnosis

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APPLIED INTELLIGENCE 2024年第20期54卷 10148-10160页

作者： Feng, Kun Xiao, Yuan Li, Zhouzheng Miao, Dongyan Beijing Univ Chem Technol State Key Lab High end Compressor & Syst Technol 15 Beisanhuan East Rd Beijing 100029 Peoples R China

Safety, efficiency, and reliability are essential requirements for aero-engines. Timely and accurate diagnosis of engine faults enables effective planning of maintenance operations and reduces downtime. Although traditional physics-based methods perform well under controlled test bench scenarios, their effectiveness in handling very noisy data and missing values is limited, constraining their utility in real-world settings. To address these gaps, we propose a fusion autoencoder that combines physics-informed and pattern-informed techniques, augmented with a Beta-Variational autoencoder learning backbone to enhance the robustness of the model. Additionally, a novel health index called the piecewise anomaly index is proposed that can detect and classify faults simultaneously. To evaluate the efficacy of the novel framework, we modified the New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset to simulate real-world scenarios and conducted experiments. The results show that the proposed method can detect faults earlier than common techniques, while also achieving accurate fault classification and degree determination with the new index.

关键词： Aero-engine Fault diagnosis autoencoder Health index

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Denoising Graph autoencoder for Missing Human Joints Reconstruction

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IEEE ACCESS 2024年 12卷 57381-57389页

作者： Lee, Wonseok Park, Seonghee Kim, Taejoon Chungbuk Natl Univ Dept Informat & Commun Engn Chungju 28644 South Korea Elect & Telecommun Res Inst Digital Convergence Res Lab Daejeon 34129 South Korea Chungbuk Natl Univ Res Inst Comp & Informat Commun Chungju 28644 South Korea

Skeleton-based human action recognition (HAR) is being utilized in various fields like action classification and abnormal behavior detection. The accurate coordinates of the human joints are a crucial factor for the high performance in skeleton-based HAR. However, the missing joints caused by occlusion and invisibility result in performance degradation. Hence, in this paper, a missing joint reconstruction model is proposed to improve the performance of skeleton-based HAR. The proposed model, based on a denoising graph autoencoder (DGAE), regards missing joints as noise corrupted information and aims to reconstruct them to be close to their original coordinates. When the encoder of the proposed model compresses the noised input into a latent vector, a masking Laplacian matrix is introduced to reduce the effect of the missing joints' features. The masking Laplacian matrix adjusts the effect of features between a missing joint and its adjacent joints by altering the weights of an adjacent matrix. In the decoder, a Laplacian matrix, which represents the connections among the joints, is utilized to reconstruct an output from the latent vector. The experiment result shows that the proposed model reconstructs the coordinates of missing joints with a marginal error. In addition, the performance of skeleton-based HAR is enhanced by reconstructing the missing joints.

关键词： autoencoder graph convolution joint reconstruction pose estimation

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Graph structured autoencoder

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NEURAL NETWORKS 2018年 106卷 271-280页

作者： Majumdar, Angshul Indraprastha Inst Informat Technol A 606Acad Bldg Delhi India

In this work, we introduce the graph regularized autoencoder. We propose three variants. The first one is the unsupervised version. The second one is tailored for clustering, by incorporating subspace clustering terms into the autoencoder formulation. The third is a supervised label consistent autoencoder suitable for single label and multi-label classification problems. Each of these has been compared with the state-of-the-art on benchmark datasets. The problems addressed here are image denoising, clustering and classification. Our proposed methods excel of the existing techniques in all of the problems. (c) 2018 Elsevier Ltd. All rights reserved.

关键词： autoencoder Graph Classification Clustering Denoising

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Fermentation process quality prediction using teacher student stacked sparse recurrent autoencoder

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2022年第10期100卷 2907-2917页

作者： Gao, Xuejin Meng, Lingjun Gao, Huihui Han, Huayun Qi, Yongsheng Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Inner Mongolia Univ Technol Sch Elect Power Hohhot Peoples R China

For predicting the value of the quality variable in fermentation processes, traditional data-driven methods do not use information in large amounts of unlabelled data. To solve this data-rich but information-poor (DRIP) problem, a teacher student stacked sparse recurrent autoencoder (TS-SSRAE) model is proposed. Compared with traditional data-driven methods, the proposed method has three main advantages. First, an autoencoder is an unsupervised method which can effectively extract rich information in unlabelled data. The proposed stacked recurrent autoencoder (SRAE) with long short-term memory (LSTM) recurrent neural unit is superior to traditional autoencoders when extracting the dynamic correlation information in the fermentation process. Second, sparse constraints can make it much easier for hidden neurons to obtain useful information in a single moment. Finally, the LSTM recurrent neural unit is complex and the inputs of a SRAE must be a sequence, which increases the complexity of the model to a certain extent. So, the knowledge distillation is employed to simplify the model and reduce the computing time. In order to demonstrate its effectiveness, the proposed method is applied to the penicillin fermentation process for a simulation experiment and Escherichia coli production of interleukin-2. The results show that the proposed method based on TS-SSRAE can have better performance than conventional methods.

关键词： autoencoder fermentation process knowledge distillation LSTM sparse

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Joint loss learning-enabled semi-supervised autoencoder for bearing fault diagnosis under limited labeled vibration signals

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JOURNAL OF VIBRATION AND CONTROL 2024年第19-20期30卷 4537-4550页

作者： Liang, Mingxuan Zhou, Kai China Jiliang Univ Coll Mech & Elect Engn Hangzhou Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Michigan Technol Univ Dept Mech Engn & Engn Mech Houghton MI USA Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom 11 Yuk Choi Rd Hong Kong Peoples R China

Rolling bearing is a critical component of machinery that has been widely applied in manufacturing, transportation, aerospace, and power and energy industries. The timely and accurate bearing fault detection thus is of vital importance. Computational data-driven deep learning has recently become a prevailing approach for bearing fault detection. Despite the progress of the deep learning approach, the deep learning performance is hinged upon the size of labeled data, the acquisition of which is expensive in actual implementation. Unlabeled data, on the other hand, are inexpensive. In this research, we develop a new semi-supervised learning method built upon the autoencoder to fully utilize a large amount of unlabeled data together with limited labeled data to enhance fault detection performance. Compared with the state-of-the-art semi-supervised learning methods, this proposed method can be more conveniently implemented with fewer hyperparameters to be tuned. In this method, a joint loss is established to account for the effects of labeled and unlabeled data, which is subsequently used to direct the backpropagation training. Systematic case studies using the Case Western Reserve University (CWRU) rolling bearing dataset are carried out, in which the effectiveness of this new method is verified by comparing it with other well-established baseline methods. Specifically, nearly all emulation runs using the proposed methodology can lead to around 2%-5% accuracy increase, indicating its robustness in performance enhancement.

关键词： Rolling bearing deep learning bearing fault diagnosis semi-supervised learning autoencoder joint loss

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A semisupervised autoencoder-based method for anomaly detection in cutting tools

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JOURNAL OF MANUFACTURING PROCESSES 2023年 93卷 315-327页

作者： Sun, Shixu Liu, Yingchao Hu, Xiaofeng Zhang, Wenjuan Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China Shanghai Inst Precis Measurement & Test Shanghai 201109 Peoples R China Univ Warwick Warwick Business Sch Coventry CV4 7AL England

Detecting anomalies such as breakage and excessive wear of cutting tools in the machining process is crucial to prevent damage and improve productivity. Data-driven anomaly detection (AD) methods suffer from limited availability of anomaly samples, which is ineluctable in practice owing to strict reliability restrictions. Therefore, we propose a semisupervised AD approach in which only failure-free samples are required to establish an AD model. The key strategy is to learn the characteristics of failure-free samples using an improved autoencoder (AE) and discern observations by deviations from the characteristics. We rebuild the loss function of AE to impel the model to learn the common characteristics in latent space. We propose a factor that reflects the anomaly degree as the decision-making function to implement AD. The proposed approach is verified on an experimental cutting tool breakage dataset and a public cutting tool wear dataset. The experimental results demonstrate the validity of the proposed approach. The comparisons with conventional methods substantiate that the proposed approach outperforms existing AD methods.

关键词： Anomaly detection Imbalanced data learning autoencoder Cutting tool Semisupervised learning

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HyperRefiner:a refined hyperspectral pansharpening network based on the autoencoder and self-attention

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International Journal of Digital Earth 2023年第1期16卷 3268-3294页

作者： Bo Zhou Xianfeng Zhang Xiao Chen Miao Ren Ziyuan Feng Institute of Remote Sensing and Geographic Information System Peking UniversityBeiigPeople's Republic of China

Deep learning has been developed to generate promising super resolution hyperspectral imagery by fusing hyperspectral imagery with the panchromatic ***,it is still challenging to maintain edge spectral information in the necessary upsampling processes of these approaches,and diffcult to guarantee effective feature *** study proposes a pansharpening network denoted as HyperRefiner that consists of,(1)a well performing upsampling network SRNet,in which the dual attention block and refined attention block are cascaded to accomplish the extraction and fusion of features;(2)a spectral autoencoder that is embedded to perform dimensionality reduction under constrained feature extraction;and(3)the optimization module which performs self-attention at the pixel and feature levels.A comparisonwithseveral state-of-the-art models reveals that HyperRefiner can improve the quality of the fused ***,compared to the single-head HyperTransformer and with the Chikusei dataset,our network improved the Peak Signal-to-Noise Ratio,Erreur Relative Globale Adimensionnelle de Synthese and Spectral Angle Mapper by 0.86%,3.62%,and 2.09%,and reduce the total memory,floating point operations,model parameters and computation time by 41%,75%,86%and 46%,*** experimental results show that HyperRefiner outperforms several networks and demonstrates its usefulness in hyperspectral image *** code is publicly available athttps://***/zsspo/Fusion_HyperRefiner.

关键词： Image fusion hyperspectral self-attention autoencoder edge spectrum

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Scalable Discrete Matrix Factorization and Semantic autoencoder for Cross-Media Retrieval

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 5947-5960页

作者： Zhang, Donglin Wu, Xiao-Jun Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China

Hashing methods have sparked great attention on multimedia tasks due to their effectiveness and efficiency. However, most existing methods generate binary codes by relaxing the binary constraints, which may cause large quantization error. In addition, most supervised cross-modal approaches preserve the similarity relationship by constructing an n x n large-size similarity matrix, which requires huge computation, making these methods unscalable. To address the above challenges, this article presents a novel algorithm, called scalable discrete matrix factorization and semantic autoencoder method (SDMSA). SDMSA is a two-stage method. In the first stage, the matrix factorization scheme is utilized to learn the latent semantic information, the label matrix is incorporated into the loss function instead of the similarity matrix. Thereafter, the binary codes can be generated by the latent representations. During optimization, we can avoid manipulating a large nxn similarity matrix, and the hash codes can be generated directly. In the second stage, a novel hash function learning scheme based on the autoencoder is proposed. The encoder-decoder paradigm aims to learn projections, the feature vectors are projected to code vectors by encoder, and the code vectors are projected back to the original feature vectors by the decoder. The encoder-decoder scheme ensures the embedding can well preserve both the semantic and feature information. Specifically, two algorithms SDMSA-lin and SDMSA-ker are developed under the SDMSA framework. Owing to the merit of SDMSA, we can get more semantically meaningful binary hash codes. Extensive experiments on several databases show that SDMSA-lin and SDMSA-ker achieve promising performance.

关键词： autoencoder cross-modal retrieval hashing

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