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An autoencoder-based model for learning regularizations in unmixing problems

DIGITAL SIGNAL PROCESSING 2023年第1期139卷

作者： Bobin, Jerome Gertosio, Remi Carloni Bobin, Christophe Thiam, Cheick Univ Paris Saclay IRFU CEA F-91191 Gif sur yvette France Univ Paris Saclay CEA List Lab Natl Henri Becquerel LNE LNHB F-91120 Palaiseau France

Signal unmixing is a class of complex, ill-posed inverse problems, which includes blind source separation or underdetermined signal separation to cite only two. Retrieving signals from their mixtures generally relies on adapted representations allowing to disentangle them. When dealing with real-world scientific data, the main challenge is to further build meaningful signal representations, which generally means capturing the underlying low-dimensional manifold structure of the signals to be recovered. Since the latter is generally unknown, this calls for a learning-based approach, which is a challenging task, especially when available training samples are scarce. The objective of the paper is to investigate a new learning model to build low-dimensional signal representations from few training samples. Based on an encoder-decoder architecture, the proposed approach aims to learn a non-linear interpolating scheme from examples. Extensive numerical experiments have been carried out to evaluate the performances of the proposed approach. We further illustrate how the learned representations can be conveniently deployed to tackle challenging semi-blind unmixing problems in the field of gamma -ray spectroscopy. (c) 2023 Elsevier Inc. All rights reserved.

关键词： Blind and semi -blind unmixing Learned constraint Machine learning autoencoder

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A Multivariate Anomaly Detector for Satellite Telemetry Data Using Temporal Attention-Based LSTM autoencoder

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Xu, Zhaoping Cheng, Zhijun Guo, Bo Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

Telemetry anomaly detection is a prominent health condition monitoring task that plays an increasingly crucial role in identifying unexpected events and improving satellite's overall reliability. Since a component or subsystem of a satellite contains multiple telemetry parameters, it is essential and practical to develop a multivariate telemetry anomaly detection framework. However, the complex nonlinear correlations among different telemetry parameters and the temporal dependency hidden in each telemetry parameter pose significant challenges. In this study, a temporal-attention-based long short-term memory autoencoder (TA-LSTM-AE) anomaly detector is proposed for detecting multivariate telemetry anomalies. Initially, a TA-LSTM-AE model is established to learn the latent representation of the correlations among the monitored telemetry parameters and the temporal dependency within each parameter. A temporal attention mechanism is applied to enhance the long-term temporal dependency modeling ability of the model. Subsequently, the Mahalanobis distance of the reconstruction error is defined as the anomaly score. An adaptive thresholding approach is specifically designed considering the variation in the learned latent representation by balancing the tradeoff between the detection reliability and accuracy. A critical parameters identification strategy is also presented for recognizing the telemetry parameters that contribute significantly to the triggered multivariate anomalies. Finally, the proposed detector is applied to detect anomalies in multivariate telemetry data collected from a real-world satellite. The experimental results verify the capability of the proposed detector in multivariate telemetry anomaly detection.

关键词： Adaptive thresholding anomaly detection autoencoder condition monitoring telemetry data

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VIGA: A variational graph autoencoder model to infer user interest representations for recommendation

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INFORMATION SCIENCES 2023年第1期640卷

作者： Gan, Mingxin Zhang, Hang Univ Sci & Technol Beijing Sch Econ & Management Beijing Peoples R China

Learning representations of both user interests and item characteristics is essentially important for recommendation tasks. Although graph neural network-based methods have significant advantages, there are still two inherent limitations: (i) users or items are often modeled as single embedding points in the vector space, and (ii) these methods usually focus more on the graph structure of user-item interactions, rather than the attribute-related information and useful attribute interactions. These limitations make it difficult to model complex user interests and learn high-quality representations, thus fail to obtain high recommendation accuracy. To overcome these limitations, we propose a variational inference-based graph autoencoder (VIGA) model to explore a multivariate distribution over latent representations for recommendation. In brief, to identify both of the complex users' interests and their interactive behaviors, VIGA first encodes users and items into latent variables, and subjects them to a multivariate Gaussian distribution via a graph autoencoder framework based on Bayesian variational inference. Then a feature fusion layer is established for the exchange and integration of both the topological structure and nodes attributes in the user-item interaction graph. Experiments on three real-world datasets show that the proposed VIGA method significantly outperforms state-of-the-art recommendation methods on recommendation accuracy.

关键词： Recommender system Graph embedding autoencoder Variational inference Representation learning User interest

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RAIDS: Robust autoencoder-based intrusion detection system model against adversarial attacks

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COMPUTERS & SECURITY 2023年 135卷

作者： Sarikaya, Alper Kilic, Banu Gunel Demirci, Mehmet Middle East Tech Univ Grad Sch Informat Ankara Turkiye Gazi Univ Dept Comp Engn Ankara Turkiye

Machine learning-based intrusion detection systems (IDS) are essential security functions in conventional and software-defined networks alike. Their success and the security of the networks they protect depend on the accuracy of their classification results. Adversarial attacks against machine learning, which seriously threaten any IDS, are still not countered effectively. In this study, we first develop a method that employs generative adversarial networks to produce adversarial attack data. Then, we propose RAIDS, a robust IDS model, designed to be resilient against adversarial attacks. In RAIDS, an autoencoder's reconstruction error is used as a prediction value for a classifier. Also, to prevent the attacker from guessing about the feature set, multiple feature sets are created and used to train baseline machine learning classifiers. A LightGBM classifier is then trained with the results produced by two autoencoders and an ensemble of baseline machine learning classifiers. The results show that the proposed robust model can increase overall accuracy by at least 13.2% and F1-score by more than 110% against adversarial attacks without the need for adversarial training.

关键词： Intrusion detection Adversarial attack autoencoder InSDN CICIDS 2017 Adversarial robustness

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ONION: Online Semantic autoencoder Hashing for Cross-Modal Retrieval

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2023年第2期14卷 27-27页

作者： Zhang, Donglin Wu, Xiao-Jun Chen, Guoqing Jiangnan Univ Sch Artificial Intelligence & Comp Sci 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Sci 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China

Cross-modal hashing (CMH) has recently received increasing attention with the merit of speed and storage in performing large-scale cross-media similarity search. However, most existing cross-media approaches utilize the batch-based mode to update hash functions, without the ability to efficiently handle the online streaming multimedia data. Online hashing can effectively address the preceding issue by using the online learning scheme to incrementally update the hash functions. Nevertheless, the existing online CMH approaches still suffer from several challenges, such as (1) how to efficiently and effectively utilize the supervision information, (2) how to learn more powerful hash functions, and (3) how to solve the binary constraints. To mitigate these limitations, we present a novel online hashing approach named ONION (ONline semantIc autoencoder hashiNg). Specifically, it leverages the semantic autoencoder scheme to establish the correlations between binary codes and labels, delivering the power to obtain more discriminative hash codes. Besides, the proposed ONION directly utilizes the label inner product to build the connection between existing data and newly coming data. Therefore, the optimization is less sensitive to the newly arriving data. Equipping a discrete optimization scheme designed to solve the binary constraints, the quantization errors can be dramatically reduced. Furthermore, the hash functions are learned by the proposed autoencoder strategy, making the hash functions more powerful. Extensive experiments on three large-scale databases demonstrate that the performance of our ONION is superior to several recent competitive online and offline cross-media algorithms.

关键词： Cross-modal retrieval online hashing autoencoder discrete optimization

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An autoencoder compression approach for accelerating large-scale inverse problems

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INVERSE PROBLEMS 2023年第11期39卷 115009-115009页

作者： Wittmer, Jonathan Badger, Jacob Sundar, Hari Bui-Thanh, Tan Univ Texas Austin Oden Inst Computat Sci & Engn Austin TX 78712 USA Univ Utah Dept Comp Sci Salt Lake City UT 84112 USA Univ Texas Austin Dept Aerosp Engn & Engn Mech Oden Inst Computat Sci & Engn Austin TX 78712 USA

Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the PDE solution introduce an enormous number of parameters and require large-scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time-dependent PDEs, the adjoint method often employed to compute gradients and higher order derivatives efficiently requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high-dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior showing how the DIAS method can be affordably extended to large-scale problems without the need for checkpointing and large memory.

关键词： machine learning autoencoder inverse problems high performance computing

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Reconstructing echoes completely submerged in background noise by a stacked denoising autoencoder method for low-power EMAT testing

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第12期34卷 125910-125910页

作者： Zhou, Jinjie Yu, Dianrui Li, Xiang Zheng, Yang Liu, Yao North Univ China Sch Mech Engn Taiyuan 030051 Shanxi Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Sichuan Peoples R China China Special Equipment Inspect & Res Inst Beijing 100029 Peoples R China

Low-power electromagnetic-acoustic transducer (EMAT) is crucially important for safety-critical equipment in industry, especially for potential explosives and inflammable petrochemical equipment and facilities. When the excitation power is very low, the corresponding echoes are overwhelmed in noise and related measurement would be inaccurate. To solve this problem, this paper presents a new echo reconstruction method based on a deep stacked denoising autoencoder (DSDAE) for nondestructive evaluation. First, the uses of reference signals and new data structure are to improve the training efficiency. A hybrid method based on variational mode decomposition and wavelet transform is used to obtain clean reference signals as inputs of the deep network. Then, the modified network structure and loss function aim to improve the ability of feature extraction and reconstruct clean echoes from low-power EMAT signals. To validate the effectiveness of the proposed method, the experiments of self-excitation and receiving A-scan inspections of stepped specimens with different thicknesses are conducted at some excitation voltages, as low as 25 V. The results indicate that the proposed DSDAE shows better and more stable denoising performance than some popular processing methods for different specimens and excitation voltages. It greatly improves the signal-to-noise ratio of the reconstructed signal to 20 dB. When applying to thickness measurement of specimens, its relative error is lower than 0.3%, which provides a practical and accurate tool for low-power EMAT testing.

关键词： autoencoder noise reduction electromagnetic-acoustic transducer echo excitation voltage

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autoencoder with Adaptive Loss Function for Supervised Anomaly Detection

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Procedia Computer Science 2022年 207卷 563-572页

作者： Yasuhiro Kanishima Takashi Sudo Hiroyuki Yanagihashi Corporate Research & Development Center Toshiba Corporation 1 Komukai-Toshiba-cho Saiwai-ku Kawasaki 212-8582 Japan

When anomaly detection is applied to a product inspection process, it is often the case that the system initially has no anomaly data, but acquires anomaly data during operation. In such cases, an unsupervised learning-based anomaly detection model (e.g. autoencoders) using only normal data may be applied in the initial phase of operation, and a supervised learning-based anomaly detection model with additional anomaly data may be applied once anomaly data have been acquired during operation. Although supervised learning-based anomaly detection models are an extension of autoencoders to supervised learning, when they are used to update an unsupervised learning-based anomaly detection model to a supervised learning-based anomaly detection model, the problem of inconsistency in the anomaly scores before and after the model update arises. In this paper, we propose a new method called autoencoder with adaptive loss function (AEAL) to improve the detection accuracy of known anomalies while ensuring consistent anomaly scores before and after model updates. AEAL is an autoencoder-based method for learning anomaly detection models by dynamically adjusting the balance between minimizing reconstruction errors for anomaly data and maximizing reconstruction errors for anomaly data. Experimental results on multiple public image datasets show that AEAL has the effect of improving the detection accuracy of known anomalies while ensuring that anomaly scores are consistent with the anomaly detection model prior to the update.

关键词： supervised anomaly detection autoencoder adaptive loss function

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An innovative approach to predict atmospheric rivers: Exploring convolutional autoencoder

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ATMOSPHERIC RESEARCH 2023年第1期289卷

作者： Singh, Shivam Goyal, Manish Kumar Indian Inst Technol Indore Dept Civil Engn Indore 453552 India

Atmospheric rivers (ARs) are filamentary regions of high moisture content in mid-latitude regions through which most of the poleward moisture is being transported. These ARs carry a huge amount of water in the form of vapor and thus landfalling of these ARs may bring either a beneficial supply of water or may create hazardous flood situations and thus cause damage to life and property. These regions have been statistically characterized as intense integrated water vapor transport (IVT) regions in the troposphere based on various thresholds of magnitude, direction, and geometry. To enhance the knowledge of data-driven methods for modelling nonlinear atmospheric dynamics associated with ARs, a first ever study with data-driven methodology incorporating a Deep Learning architecture, autoencoder has been proposed. While training the proposed model, the Adam optimizer was used to reduce the mean squared error loss and was optimized using the Rectified Linear Unit (ReLU) and Sigmoid activation functions. The prediction results of the availability of ARs at next frames by the autoencoder were assessed by popularly used performance evaluation metrics structural similarity index metrics (SSMI), mean squared error (MSE), root mean squared error (RMSE), and peak signal to noise ratio (PSNR). We have got comparatively higher scores (average) of SSIM (0.739) and PSNR (64.422) and lower scores (average) of RMSE (0.155) and MSE (0.0247) for AR prediction from our model which signifies the accuracy of the proposed autoencoder in capturing AR dynamics. The findings of the study could be useful in giving important insights to incorporate Deep Learning models for forecasting ARs at significant lead time and consequently reducing the risk and increasing the resilience of AR flood prone regions.

关键词： Atmospheric rivers Deep learning autoencoder Floods Integrated water vapor transport Machine learning

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Unsupervised anomaly detection in pressurized water reactor digital twins using autoencoder neural networks

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NUCLEAR ENGINEERING AND DESIGN 2023年第1期413卷

作者： Cancemi, S. A. Lo Frano, R. Santus, C. Inoue, T. Univ Pisa Dept Civil & Ind Engn Pisa Italy JAMSTEC Inst Marine Earth Explorat & Engn Yokosuka Japan

Deep learning (DL), that is becoming quite popular for prediction and analysis of complex patterns in large amounts of data is used to investigate the safety behaviour of the nuclear plant items. This is achieved by using multiple layers of artificial neural networks to process and transform input data, allowing for the creation of highly accurate predictive models. Particularly to the aim the unsupervised machine learning approach and the digital twin concept in form of pressurized water reactor 2-loop simulator are used. This innovative methodology is based on neural network algorithm that makes capable to predict failures of plant structure, system, and components earlier than the activation of safety and emergency systems. Moreover, to match the objective of the study several scenarios of loss of cooling accident (LOCA) of different break size were simulated. To make the acquisition platform realistic, Gaussian noise was added to the input signals. The neural network has been fed by synthetic dataset provide by PCTRAN simulator and the efficiency in event identification was studied. Further, due to the very limited studies on the unsupervised anomaly detection by means of autoencoder neural networks applied for plant monitoring and surveillance, the methodology has been validated with experimental data from resonant test rig designed for fatigue testing of tubular components. The obtained results demonstrate the reliability and the efficiency of the methodology in detecting anomalous events prior the activation of safety system. Particularly, if the difference between the expected readings and the collected data goes beyond the predetermined threshold, then the anomalous event is identified, e.g., the model detected anomalies up to 38 min before the reactor scram intervention.

关键词： Neural Network autoencoder Predictive Maintenance Safety NPP Unsupervised Anomaly Detection LOCA

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