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Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022年第1期2022卷 1-20页

作者： Jin, Pian Wang, Biao Li, Lebo Chao, Peng Xie, Fangtong Jiangsu Univ Sci & Technol Zhenjiang Jiangsu Peoples R China Hangzhou Appl Acoust Res Inst Hangzhou Peoples R China Harbin Engn Univ Sci & Technol Underwater Vehicle Technol Lab Harbin Peoples R China

Passive localization of underwater targets was a thorny problem in underwater acoustics. For traditional model-driven passive localization methods, the main challenges are the inevitable environmental mismatch and the presence of interference and noise everywhere. In recent years, data-driven machine learning approaches have opened up new possibilities for passive localization of underwater acoustics. However, the acquisition and processing of underwater acoustics data are more restricted than other scenarios, and the lack of data is one of the most enormous difficulties in the application of machine learning to underwater acoustics. To take full advantage of the relatively easy accessed unlabeled data, this paper proposes a framework for underwater acoustic source localization based on a two-step semi-supervised learning classification model. The first step is trained in unsupervised mode with the whole available dataset (labeled and unlabeled dataset), and it consists of a convolutional autoencoder (CAE) for feature extraction and self-attention (RA) mechanism for picking more useful features by applying constraints on the CAE. The second step is trained in supervised mode with the labeled dataset, and it consists of a multilayer perceptron connected to an encoder from the first step and is used to perform the source location task. The proposed framework is validated on uniform vertical line array data of SWellEx-96 event S5. Compared with the supervised model and the model without the RA, the proposed framework maintains good localization performance with the reduced labeled dataset, and the proposed framework is more robust when the training dataset and the test dataset of the second step are distributed differently, which is called "data mismatch."

关键词： Underwater acoustic source localization Semi-supervised learning convolutional autoencoder Self-attentive mechanism Data mismatch

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Pansharpening based on convolutional autoencoder and multi-scale guided filter

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2021年第1期2021卷 1-20页

作者： AL Smadi, Ahmad Yang, Shuyuan Kai, Zhang Mehmood, Atif Wang, Min Alsanabani, Ala Xidian Univ Sch Artificial Intelligence 2 South Taibai Rd Xian 710071 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China Xidian Univ Key Lab Radar Signal Proc 2 South Taibai Rd Xian Peoples R China

In this paper, we propose a pansharpening method based on a convolutional autoencoder. The convolutional autoencoder is a sort of convolutional neural network (CNN) and objective to scale down the input dimension and typify image features with high exactness. First, the autoencoder network is trained to reduce the difference between the degraded panchromatic image patches and reconstruction output original panchromatic image patches. The intensity component, which is developed by adaptive intensity-hue-saturation (AIHS), is then delivered into the trained convolutional autoencoder network to generate an enhanced intensity component of the multi-spectral image. The pansharpening is accomplished by improving the panchromatic image from the enhanced intensity component using a multi-scale guided filter;then, the semantic detail is injected into the upsampled multi-spectral image. Real and degraded datasets are utilized for the experiments, which exhibit that the proposed technique has the ability to preserve the high spatial details and high spectral characteristics simultaneously. Furthermore, experimental results demonstrated that the proposed study performs state-of-the-art results in terms of subjective and objective assessments on remote sensing data.

关键词： Pansharpening convolutional autoencoder Guided image filtering Adaptive intensity-hue-saturation AIHS

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Knowledge extraction and retention based continual learning by using convolutional autoencoder-based learning classifier system

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INFORMATION SCIENCES 2022年 591卷 287-305页

作者： Irfan, Muhammad Zheng, Jiangbin Iqbal, Muhammad Masood, Zafar Arif, Muhammad Hassan Northwestern Polytech Univ Sch Software Xian Peoples R China Higher Coll Technol Fac Comp & Informat Sci Fujairah U Arab Emirates CESAT Islamabad Pakistan

An ideal artificial intelligence-based autonomous system, interacting with a dynamic envi-ronment, is required to learn continuously as human do. Human beings retain the learned knowledge, accumulate, and utilize it to solve related problems. Currently, most artificial intelligence-based systems lack this capability and work in an isolated learning paradigm. In this paper, we present a novel continual learning model to solve the challenging problem of real-world images classification. The proposed model is capable of learning continuously by utilizing the previously learned knowledge. It can handle both multi-task and single incremental task scenarios as opposed to various existing models that cover only the multi-task scenarios. In the proposed model, a deep convolutional autoencoder is presented to extract features from images. In addition, a learning classifier system with an effective knowledge encoding scheme is proposed for mapping real-world images to code fragment-based compact knowledge representation. Experiments are conducted on three benchmark image datasets to validate the model: (i) CORe50, (ii) iCubWorld28, and (iii) STL-10. Experiments results demonstrate that the proposed model outperforms the baseline method as well as various state-of-the-art methods for both continual learning scenarios.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Continual learning Image classification Learning classifier systems convolutional autoencoder

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Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第5期46卷 992-1011页

作者： Wang, Enxiu Zhou, Haoxuan Wen, Guangrui Huang, Ziling Liu, Zimin Chen, Xuefeng Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian Peoples R China Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Syst Xian Peoples R China Politecn Milan Energy Dept Via Masa Milan Italy Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China

Bearing operation states will directly determine the performance of the equipment;thus, monitoring operation status and degradation indicators is the key to ensuring continuous and healthy operation of the equipment. However, most of the research uses single-source information data, which makes it difficult to model when dealing with multi-source information, complex data distribution, and noise. In this paper, a bearing performance degradation assessment method based on multi-source information is proposed to comprehensively utilize the data signals of different structures, spaces, types, and sources. First, the adversarial fusion convolutional autoencoder is constructed for obtaining the degradation index of the bearing, while the adversarial learning strategy is applied to achieve the effect of enhancing the robustness and sensitivity of the degradation indicators extracted by the network. Then the degradation index is input into the support vector data description to determine the fault anomalies of the degradation index adaptively and the fuzzy c-means algorithms to obtain the final rolling bearing performance degradation evaluation results. Through the verification results of two experiment datasets, it is found that the proposed model can achieve accurate evaluation and quantitative analysis of the performance degradation process of bearings. As a result, the entire network ensures the reconstruction accuracy of normal samples while simultaneously stretching the reconstruction error of abnormal samples to achieve accurate monitoring of degradation onset.

关键词： Multi-source fusion degradation assessment convolutional autoencoder deep learning

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Sparse Representation convolutional autoencoder for Feature Learning of Vibration Signals and its Applications in Machinery Fault Diagnosis

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第12期69卷 13565-13575页

作者： Miao, Mengqi Sun, Yuanhang Yu, Jianbo Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

Vibration signals are widely utilized in many fields, which can reflect machine health state. Those typical deep learning techniques cannot learn impulsive features from vibration signals due to interference of strong background noise. Supervised learning greatly rely on vast labeled data, which limits the implementation of deep learning in industry applications. Hence, in this article, a new deep neural network (DNN), sparse representation convolutional autoencoder (SRCAE), is proposed to extract impulsive components of vibration signals for machinery fault diagnosis in an unsupervised manner. A sparse representation (SR) block is proposed to extract impulsive components of vibration signals and transform the time-domain signal to a sparse domain by sparse mapping of a convolutional graph. The SR block is inserted into a deep network to remove noise and learn impulsive features for machinery fault diagnosis. Furthermore, an unsupervised selective feature transmission mechanism is proposed to improve training efficiency and realize feature filtering simultaneously. Finally, the effectiveness of SRCAE is verified on rotary machine fault diagnosis experiments. The testing results show that SRCAE has good noise filtering and impulsive components extraction performance. The recognition accuracy of SRCAE reached 97.16% based on the fivefold cross validation, which demonstrates the outperformance of SRCAE in comparison with state-of-the-art DNNs.

关键词： convolutional autoencoder deep learning machinery fault diagnosis selective residual learning sparse representation (SR)

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Spatially Variant convolutional autoencoder Based on Patch Division for Pill Defect Detection

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IEEE ACCESS 2020年 8卷 216781-216792页

作者： Kim, Sora Jo, Youngjae Cho, Jungchan Song, Jiwoo Lee, Younyoung Lee, Minsik Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea Hyundai Mobis Co Ltd ADAS Platform Cell Team Yongin 16891 South Korea Gachon Univ Coll Informat Technol Seongnam 13120 South Korea Daekhon Corp Adv Technol Ctr Seoul 08381 South Korea

Detecting pill defection remains challenging, despite recent extensive studies, because of the lack of defective data. In this paper, we propose a pipeline composed of a pill detection module and an autoencoder-based defect detection module to detect defective pills in pill packages. Furthermore, we created a new dataset to test our model. The pill detection module segments pills in an aluminum-plastic package into individual pills. To segment pills, we used a shallow segmentation network that is then divided into individual pills using the watershed algorithm. The defect detection module identifies defects in individual pills. It is trained only on the normal data. Thus, it is expected that the module will be unable to reconstruct defective data correctly. However, in reality, the conventional autoencoder reconstructs defective data better than expected, even if the network is trained only on normal data. Hence, we introduce a patch division method to prevent this problem. The patch division involves dividing the output of the convolutional encoder network into patch-wise features, and then applying patch-wise encoder layer. In this process, each latent patch has its independent weight and bias. This can be interpreted as reconstructing the input image using multiple local autoencoders. The patch division makes the network concentrate only on reconstructing local regions, thereby reducing the overall capacity. This prohibits the proposed network reconstructing unseen data well. Experiments show that the proposed patch division technique indeed improves the defect detection performance and outperforms existing deep learning based anomaly detection methods. The ablation study shows the efficacy of patch division and compression following the concatenation of patch-wise features.

关键词： Image reconstruction Anomaly detection Pipelines Mathematical model Generative adversarial networks Feature extraction Licenses Pill defect detection deep neural network convolutional autoencoder unsupervised learning

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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

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JOURNAL OF COMPUTATIONAL PHYSICS 2024年 501卷

作者： Pichi, Federico Moya, Beatriz Hesthaven, Jan S. Ecole Polytech Fed Lausanne Chair Computat Math & Simulat Sci CH-1015 Lausanne Switzerland Univ Zaragoza Aragon Inst Engn Res I3A Zaragoza 50018 Spain

The present work proposes a framework for nonlinear model order reduction based on a Graph convolutional autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric Partial Differential Equations (PDEs). Linear techniques such as Proper Orthogonal Decomposition (POD) and Greedy algorithms have been analyzed thoroughly, but they are more suitable when dealing with linear and affine models showing a fast decay of the Kolmogorov n-width. On one hand, the autoencoder architecture represents a nonlinear generalization of the POD compression procedure, allowing one to encode the main information in a latent set of variables while extracting their main features. On the other hand, Graph Neural Networks (GNNs) constitute a natural framework for studying PDE solutions defined on unstructured meshes. Here, we develop a non-intrusive and data -driven nonlinear reduction approach, exploiting GNNs to encode the reduced manifold and enable fast evaluations of parametrized PDEs. We show the capabilities of the methodology for several models: linear/nonlinear and scalar/vector problems with fast/slow decay in the physically and geometrically parametrized setting. The main properties of our approach consist of (i) high generalizability in the low-data regime even for complex behaviors, (ii) physical compliance with general unstructured grids, and (iii) exploitation of pooling and un-pooling operations to learn from scattered data.

关键词： Nonlinear reduced order modeling Parametric PDEs Graph neural networks convolutional autoencoder Geometrical parameters

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Semi-supervised multitask learning using convolutional autoencoder for faulty code detection with limited data

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APPLIED INTELLIGENCE 2023年第4期53卷 3877-3888页

作者： Anh Viet Phan Khanh Duy Tung Nguyen Lam Thu Bui Le Quy Don Tech Univ 236 Hoang Quoc Viet St Hanoi Vietnam Acad Cryptog Tech 141 Chien Thang St Hanoi Vietnam

Detecting faults in source code to fix is an important task in the software quality assurance. Building automated detectors using machine learning has been faced two big challenges of data imbalance and shortages. To address the issues, this paper proposes a deep neural network and training procedures to allow learning with limited annotated data. The network is composed of an unsupervised auto-encoder and a supervised classifier. The two components share some first layers that plays as a program feature extractor. Interestingly, we can leverage a large amount of unlabeled data from various sources to train the auto-encoder independently then transfer to the target domain. Additionally, sharing layers, and jointly training the reconstruction and the classification tasks stimulate the generation of the sophisticated features. We conducted the experiments on four real datasets with different amount of labeled data and with adding more unlabeled data. The results have confirmed that the multi-task outperforms the single-task and leveraging the unlabeled data is beneficial. Specifically, when reducing the labeled data from 100% to 75%, 50%, 25%, the performance of several deep networks drops sharply, while it reduces gradually for our model.

关键词： Faulty code detection Semi-supervised learning Multitask learning Self-supervised learning convolutional autoencoder

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Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data

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RENEWABLE ENERGY 2021年 171卷 103-115页

作者： Li, Yanting Jiang, Wenbo Zhang, Guangyao Shu, Lianjie Shanghai Jiao Tong Univ Dept Ind Engn & Logist Management Shanghai Peoples R China Univ Macau Fac Business Adm Taipa Macau Peoples R China

Condition monitoring and fault diagnosis for wind turbines can effectively reduce the impact of failures. However, many wind turbines cannot establish fault diagnosis models due to insufficient data. The operational data of similar wind turbines usually contain some universal information about failure properties. In order to make full use of these useful information, a fault diagnosis method based on parameter-based transfer learning and convolutional autoencoder (CAE) for wind turbines with small-scale data is proposed in this paper. The proposed method can transfer knowledge from similar wind turbines to the target wind turbine. The performance of the proposed method is analyzed and compared to other transfer/non-transfer methods. The comparison results show that the proposed method has advantages in diagnosing faults for wind turbines with small-scale data. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Wind turbine Fault diagnosis Transfer learning convolutional autoencoder Small-scale data

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Residual attention convolutional autoencoder for feature learning and fault detection in nonlinear industrial processes

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NEURAL COMPUTING & APPLICATIONS 2021年第19期33卷 12737-12753页

作者： Liu, Xing Yu, Jianbo Ye, Lyujiangnan Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China Rutgers State Univ Stat Dept New Brunswick NJ 08854 USA

Deep learning has been successfully applied in process monitoring in recent years due to its powerful feature extraction. However, these monitoring methods are difficult to extract intrinsic representations of the process data in complex nonlinear processes. A new deep neural network, residual attention convolutional autoencoder (RACAE) is proposed for process monitoring. The unsupervised learning method of RACAE can extract representative features from high-dimensional data, which can significantly improve process monitoring performance in nonlinear processes. RACAE effectively integrates convolution calculation with an autoencoder to perform effective feature extraction of multivariate data. Moreover, residual attention block is embedded in the autoencoder to select these key features and then reduce the feature dimension for detector. A new process monitoring model is proposed and two kinds of statistics are developed for fault detection. The effectiveness of RACAE in fault detection is evaluated through a numerical case and two benchmark processes. The convolutional autoencoder based on residual attention provides a new approach for feature learning and process monitoring of complex processes.

关键词： Nonlinear process Deep learning Fault detection convolutional autoencoder

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