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denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder

MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第3期4卷 035024页

作者： Bacon, Philippe Trovato, Agata Bejger, Michal Univ Paris cite CNRS Astroparticule & Cosmol F-75013 Paris France Univ Trieste Dipartimento Fis I-34127 Trieste Italy Sez Trieste INFN I-34127 Trieste Italy INFN Sez Ferrara Via Saragat 1 I-44122 Ferrara Italy Polish Acad Sci Nicolaus Copernicus Astron Ctr Bartycka 18 PL-00716 Warsaw Poland

The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals to the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector's noise and a dataset of detector noise artifacts ('glitches'), and its fidelity is tested on real GW events from O1 and O2 LIGO-Virgo observing runs.

关键词： denoising autoencoder convolutional neural network data analysis gravitational waves

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Medical image denoising system based on stacked convolutional autoencoder for enhancing 2-dimensional gel electrophoresis noise reduction

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期69卷 102842-102842页

作者： Ahmed, Aya Saleh El-Behaidy, Wessam H. Youssif, Aliaa A. A. Helwan Univ Fac Comp & Artificial Intelligence Cairo Egypt Arab Acad Sci Technol & Maritime Transport AASTMT Al Giza Desert Giza Governorat Egypt

Image denoising is the technique of removing noise or distortions from an image. During medical image acquisition, random noise is added, which results in a lower contrast in those images. For that, image denoising is a crucial task for medical imaging analysis. In this study, a denoising system using three heterogeneous medical datasets is proposed based on stacked convolutional autoencoder (SCAE) technique. To validate its efficiency, different evaluation metrics are used, such as mean squared error (MSE), Peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), structural similarity index measure (SSIM) and cross correlation (CC). The proposed denoising system gives good results among the medical and microscopic datasets that are used for training. The best average results obtained are 0.0039 for MSE, 24.07 for PSNR, 0.1220 for CNR, 0.85 for SSIM, and 0.6358 for CC. Then, the proposed SCAE denoising system was applied to the LECB 2-D PAGE database for denoising real 2-DGE images. The results of denoising 2-DGE images are evaluated by MSE, spot efficiency, false discovery rate (FDR), and signal-to-noise ratio (SNR). The best average results for 2-DGE images are 0.014 for MSE, 75 spot efficiency, 36.3 for FDR and 18.41 for SNR. The proposed system has enhanced the denoising of 2DGE images by 0.9% to 17.6% when compared to other techniques.

关键词： 2-D gel electrophoresis Noise reduction Stacked convolutional auto-encoder denoising autoencoder SCAE

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Near-surface defect detection in ultrasonic testing using domain-knowledge-informed self-supervised learning

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ULTRASONICS 2025年 147卷 107528页

作者： Jeon, Minsu Choi, Minseok Choi, Wonjae Ha, Jong Moon Oh, Hyunseok Gwangju Inst Sci & Technol GIST Sch Mech Engn Gwangju 61005 South Korea Ajou Univ Dept Mech Engn Suwon 16499 South Korea Korea Res Inst Stand & Sci KRISS Nondestruct Metrol Grp Daejeon 34113 South Korea

Recently, significant research efforts have been made to enhance ultrasonic testing (UT) by employing artificial intelligence (AI). However, collecting an extensive amount of labeled data across various testing environments to train the AI model poses significant challenges. Moreover, conventional UT typically focuses on detecting deep- depth defects, which limits the effectiveness of such methods in detecting near-surface defects. To this end, this paper proposes a novel near-surface defect detection method for ultrasonic testing that can be employed without collecting labeled data. We propose a self-supervised anomaly detection model that incorporates domain knowledge. First, synthetic faulty samples are generated by fusing the measured UT signals with the back-wall UT reflection signals, to simulate real faulty features. Unlike the CutPaste method used for computer vision applications, this synthesis method adds the back-wall echo signal to random locations by incorporating the physical principles of the superposition of ultrasonic signals. Next, a de-anomaly network is devised to isolate subtle defect features within the measured UT signals. The presence of defects was determined using the three- sigma rule of the mean absolute value of the residual output. The defect depth is determined by a time-of-flight calculation from the residual output. The effectiveness of the proposed method was evaluated through the UT of aluminum blocks with near-surface defects of varying depths under different surface conditions. Both qualitative and quantitative comparison studies demonstrated that the proposed method outperformed existing methods in detecting the presence and depth of near-surface defects.

关键词： Ultrasonic testing Diagnostics Data synthesis denoising autoencoder Self-supervised learning

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End to end multifunctional autoencoder-based classification and denoising network

End to end multifunctional autoencoder-based classification ...

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作者： Mingze Zhou College of Computer Science and Technology Zhejiang University

Brain tumours are one of the common diseases in human beings. Currently, brain Nuclear Magnetic Resonance(MRI) is the main means of detecting brain diseases. There are many works related to the classification and noise reduction of brain MRI images, but there are few works that combine these two parts simultaneously, i.e., noise reduction processing and classification of brain images at the same time. In this work, these two parts of work will be combined together, extracting the features of brain MRI images through an encoder composed of convolutional neural networks, using the features to classify the images, and then using the features to reduce the noise of the images to produce a low-noise image. According to the model,this work also proposes a new loss function. The new loss function is composed by adjusting the weights of the loss functions related to the classification and denoising tasks. This paper tries three different ways of weight assignment, and the experimental results show that the dynamic parameter assignment approach achieves the best results for image classification, but none of the three approaches achieves acceptable results for noise reduction.

关键词： brain tumour MRI images convolutional neural networks denoising autoencoder classification

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Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using denoising autoencoders

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第4期9卷 913-926页

作者： Sahay, Rajeev Zhang, Minjun Love, David J. J. Brinton, Christopher G. G. Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA Saab Inc Auton & Undersea Syst Div W Lafayette IN 47907 USA GoForward Inc San Francisco CA 94104 USA

Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model's input, during inference (i.e., the adversarial perturbation is injected into inputs during deployment after the model has been trained) that are specifically crafted to force the trained regression model to output an infeasible power allocation solution. In this work, we develop an autoencoder-based mitigation technique, which allows deep learning-based power allocation models to operate in the presence of adversaries without requiring retraining. Specifically, we develop a denoising autoencoder (DAE), which learns a mapping between potentially perturbed data and its corresponding unperturbed input. We test our defense across multiple attacks and in multiple threat models and demonstrate its ability to (i) mitigate the effects of adversarial attacks on power allocation networks using two common precoding schemes, (ii) outperform previously proposed benchmarks for mitigating regression-based adversarial attacks on maMIMO networks, (iii) retain accurate performance in the absence of an attack, and (iv) operate with low computational overhead. Code is publicly available at https://***/Jess-jpg-txt/DAE_for_adv_attacks_in_MIMO.

关键词： Adversarial attacks deep learning denoising autoencoder massive MIMO wireless security

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MIDIA: exploring denoising autoencoders for missing data imputation

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DATA MINING AND KNOWLEDGE DISCOVERY 2020年第6期34卷 1859-1897页

作者： Ma, Qian Lee, Wang-Chien Fu, Tao-Yang Gu, Yu Yu, Ge Dalian Maritime Univ Coll Informat Sci & Technol Dalian Peoples R China Penn State Univ Dept Comp Sci & Engn State Coll PA 16801 USA Northeastern Univ Sch Engn & Comp Sci Shenyang Peoples R China

Due to the ubiquitous presence of missing values (MVs) in real-world datasets, the MV imputation problem, aiming to recover MVs, is an important and fundamental data preprocessing step for various data analytics and mining tasks to effectively achieve good performance. To impute MVs, a typical idea is to explore the correlations amongst the attributes of the data. However, those correlations are usually complex and thus difficult to identify. Accordingly, we develop a new deep learning model calledMIssing Data Imputation denoising autoencoder(MIDIA) that effectively imputes the MVs in a given dataset by exploring non-linear correlations between missing values and non-missing values. Additionally, by considering various data missing patterns, we propose two effective MV imputation approaches based on the proposed MIDIA model, namely MIDIA-Sequential and MIDIA-Batch. MIDIA-Sequential imputes the MVs attribute-by-attribute sequentially by training an independent MIDIA model for each incomplete attribute. By contrast, MIDIA-Batch imputes the MVs in one batch by training a uniform MIDIA model. Finally, we evaluate the proposed approaches by experimentation in comparison with existing MV imputation algorithms. The experimental results demonstrate that both MIDIA-Sequential and MIDIA-Batch achieve significantly higher imputation accuracy compared with existing solutions, and the proposed approaches are capable of handling various data missing patterns and data types. Specifically, MIDIA-Sequential performs better than MIDIA-Batch for data with monotone missing pattern, while MIDIA-Batch performs better than MIDIA-Sequential for data with general missing pattern.

关键词： Missing data imputation denoising autoencoder MIDIA Deep learning

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Examining the Mapping Functions of denoising autoencoders in Singing Voice Separation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 266-278页

作者： Mimilakis, Stylianos Ioannis Drossos, Konstantinos Cano, Estefania Schuller, Gerald Fraunhofer IDMT Semant Mus Technol Grp D-98693 Ilmenau Germany Tampere Univ Audio Res Grp Tampere 33720 Finland Tech Univ Ilmenau Inst Media Technol D-98693 Ilmenau Germany

The goal of this article is to investigate what singing voice separation approaches based on neural networks learn from the data. We examine the mapping functions of neural networks based on the denoising autoencoder (DAE) model that are conditioned on the mixture magnitude spectra. To approximate the mapping functions, we propose an algorithm inspired by the knowledge distillation, denoted the neural couplings algorithm (NCA). The NCA yields a matrix that expresses the mapping of the mixture to the target source magnitude information. Using the NCA, we examine the mapping functions of three fundamental DAE-based models in music source separation;one with single-layer encoder and decoder, one with multi-layer encoder and single-layer decoder, and one using skip-filtering connections (SF) with a single-layer encoding and decoding. We first train these models with realistic data to estimate the singing voice magnitude spectra from the corresponding mixture. We then use the optimized models and test spectral data as input to the NCA. Our experimental findings show that approaches based on the DAE model learn scalar filtering operators, exhibiting a predominant diagonal structure in their corresponding mapping functions, limiting the exploitation of inter-frequency structure of music data. In contrast, skip-filtering connections are shown to assist the DAE model in learning filtering operators that exploit richer inter-frequency structures.

关键词： Source separation Computational modeling Noise reduction Neural networks Approximation algorithms Multiple signal classification Decoding Music source separation singing voice denoising autoencoder DAE skip connections neural couplings algorithm NCA

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Dynamic Feature Acquisition Using denoising autoencoders

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019年第8期30卷 2252-2262页

作者： Kachuee, Mohammad Darabi, Sajad Moatamed, Babak Sarrafzadeh, Majid Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

In real-world scenarios, different features have different acquisition costs at test time which necessitates cost-aware methods to optimize the cost and performance tradeoff. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test time. The method incrementally asks for features based on the available context that are known feature values. The proposed method is based on sensitivity analysis in neural networks and density estimation using denoising autoencoders with binary representation layers. In the proposed architecture, a denoising autoencoder is used to handle unknown features (i. e., features that are yet to be acquired), and the sensitivity of predictions with respect to each unknown feature is used as a context-dependent measure of informativeness. We evaluated the proposed method on eight different real-world data sets as well as one synthesized data set and compared its performance with several other approaches in the literature. According to the results, the suggested method is capable of efficiently acquiring features at test time in a cost-and context-aware fashion.

关键词： Context aware cost aware denoising autoencoder feature acquisition test time

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Convolutional adaptive denoising autoencoders for hierarchical feature extraction

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Frontiers of Computer Science 2018年第6期12卷 1140-1148页

作者： Qianjun ZHANG Lei ZHANG Machine Intelligence Laboratory College of Computer ScienceSichuan UniversityChengdu 610065China

Convolutional neural networks (CNNs) are typical structures for deep learning and are widely used in image recognition and classification. However, the random initialization strategy tends to become stuck at local plateaus or even diverge, which results in rather unstable and ineffective solutions in real applications. To address this limitation, we propose a hybrid deep learning CNN-AdapDAE model, which applies the features learned by the AdapDAE algorithm to initialize CNN filters and then train the improved CNN for classification tasks. In this model, AdapDAE is proposed as a CNN pre-training procedure, which adaptively obtains the noise level based on the principle of annealing, by starting with a high level of noise and lowering it as the training progresses. Thus, the features learned by AdapDAE include a combination of features at different levels of granularity. Extensive experimental results on STL-10, CIFAR-10, andMNIST datasets demonstrate that the proposed algorithm performs favorably compared to CNN (random filters), CNNAE (pre-training filters by autoencoder), and a few other unsupervised feature learning methods.

关键词： convolutional neural networks annealing denoising autoencoder adaptive noise level pre-training

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Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2018年 884卷 97-104页

作者： Lee, Donghoon Choi, Sunghoon Kim, Hee-Joung Yonsei Univ Res Inst Hlth Sci Dept Radiat Convergence Engn 1 Yonseidae Gil Wonju 220710 Gangwon South Korea Yonsei Univ Coll Hlth Sci Dept Radiol Sci 1 Yonseidae Gil Wonju 220710 Gangwon South Korea

When processing medical images, image denoising is an important pre-processing step. Various image denoising algorithms have been developed in the past few decades. Recently, image denoising using the deep learning method has shown excellent performance compared to conventional image denoising algorithms. In this study, we introduce an image denoising technique based on a convolutional denoising autoencoder (CDAE) and evaluate clinical applications by comparing existing image denoising algorithms. We train the proposed CDAE model using 3000 chest radiograms training data. To evaluate the performance of the developed CDAE model, we compare it with conventional denoising algorithms including median filter, total variation (TV) minimization, and non-local mean (NLM) algorithms. Furthermore, to verify the clinical effectiveness of the developed denoising model with CDAE, we investigate the performance of the developed denoising algorithm on chest radiograms acquired from real patients. The results demonstrate that the proposed denoising algorithm developed using CDAE achieves a superior noise-reduction effect in chest radiograms compared to TV minimization and NLM algorithms, which are state-of-the-art algorithms for image noise reduction. For example, the peak signal-to-noise ratio and structure similarity index measure of CDAE were at least 10% higher compared to conventional denoising algorithms. In conclusion, the image denoising algorithm developed using CDAE effectively eliminated noise without loss of information on anatomical structures in chest radiograms. It is expected that the proposed denoising algorithm developed using CDAE will be effective for medical images with microscopic anatomical structures, such as terminal bronchioles. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Image denoising Deep learning denoising autoencoder Chest radiography

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