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30th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Catak, Yigit Sahin, Kerem Gunye, Osman Berke Ozkan, Huseyin Sabanci Univ Muhendislik & Doga Bilimleri Fak Istanbul Turkey Cozum Makina AR GE Dept Istanbul Turkey

ISBN: (纸本)9781665450928

Maintenance of the machinery is a crucial task in industrial production sectors working with machinery. The most important aspect of maintenance is timing. Executing maintenances more frequently or sparsely than the necessary amount causes separate problems resulting with unnecessary expenses or halts in the production. To prevent these problems, a smart system to decide the timing of the maintenance must be established. In this study, we develop an auto-encoder extension of previously proposed deep convolutional network that is trained successfully on the modelling of electroencephalogram (EEG) signals with high performance. The auto-encoder extracts features from the vibration signals collected from the machinery. This method allows us to synthesize multi-channel vibration data which we use to classify the type of the failure that the machinery bearing is going to face, without expert field knowledge and with a high accuracy. The performance of the proposed network is tested on the publicly available Case Western Reserve University (CWRU) bearing dataset with the classification accuracy. Proposed network showed a better classification performance, allowed smaller bottleneck feature sizes and faster training times compared to the Normalized Sparse Auto-Encoder - Locally Connected Network (NSAE-LCN), which is one of the best performing networks on the same dataset.

关键词： autoencoder Predictive Maintenance Time Series Processing Deep Learning

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Time-frequency enhanced characterization method based on asymmetric image reconstruction autoencoder

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 035107-035107页

作者： Han, Ruiyu Mao, Zhiwei Zhang, Zhenjing Zhang, Jinjie Beijing Univ Chem Technol Key Lab Engine Hlth Monitoring Control & Networkin Minist Educ Beijing 100029 Peoples R China Beijing Univ Chem Technol State Key Lab High End Compressor & Syst Technol Beijing 100029 Peoples R China Weichai Power Co Ltd Weifang Peoples R China

The vibration signals of mechanical equipment are subject to the influence of complex and variable working conditions, often exhibiting non-smooth and non-linear characteristics. The conventional time-frequency (TF) analysis (TFA) method, which relies on energy concentration, is susceptible to noise and impact, making it challenging to accurately extract fault characteristics. To overcome these limitations, this paper proposes an innovative approach. In this paper, we introduce an asymmetric image reconstruction autoencoder model, which is based on two well-known TFA methods, namely, short-time Fourier transform (STFT) and synchroextracting transform (SET), effectively reducing noise and improving the TF energy concentration process through learning the mapping relationship between STFT and SET. To address the clarity issue in the reconstructed TF images, the paper incorporates a channel attention mechanism known as SE Block into the encoding-decoding structure. Additionally, a skip connection structure is introduced to aid in restoring the structural details of the reconstructed TF images. Moreover, an improved weighted joint loss function is proposed to adaptively enhance various types of TF distribution features. This enhancement ensures that different characteristics of TF distribution are adequately addressed during the reconstruction process. The proposed method is put to the test using both simulated signals and experimental signals from gearbox rolling bearing faults. The results demonstrate that compared to traditional TFA and post-processing methods, the proposed model exhibits superior capabilities in enhancing the TF characterization of multi-source time-varying signals. Furthermore, it demonstrates remarkable robustness to noise and can accurately extract instantaneous frequency. These findings point to the promising potential of this method for mechanical fault identification and diagnosis applications.

关键词： time-frequency enhancement autoencoder multi-source time-varying signal instantaneous frequency mechanical fault diagnosis

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A Novel CNN-based autoencoder with Channel Feedback for Intelligent Maritime Communications

A Novel CNN-based Autoencoder with Channel Feedback for Inte...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Lin, Bin Han, Xiaoling Wu, Nan Li, Jiaye Wang, Haocheng Shao, Shuai Dalian Maritime Univ Informat Sci & Technol Dalian Peoples R China

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

Driven by the rapid growth of maritime business, the research of reliable maritime communications has attracted great attention from both academic and industry. This paper proposes a novel convolutional neural networks (CNN)-based autoencoder with channel feedback (CNN-AE-CF) for intelligent maritime communications with complex and changeable environment. A CNN-AE-CF is comprised of CNN layers, which inherits the breakthrough characteristics of CNN, such as generalization, feature learning, classification, and fast training convergence. In the CNN-AE-CF, we introduce a feedback channel and a feedback decoder at the transmitter to decode the feedback signals. Then, the transmitter combines feedback signals with input signals for secondary encoding. We leverage Rician fading channel to simulate the marine environment, in which CNN-AE-CF is trained. Finally, simulation results illustrate the superiority in terms of reliability and robustness. The proposed autoencoder is promising for intelligent maritime communications.

关键词： AI CNN channel feedback autoencoder maritime communications

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Recreating Fingerprint Images by Convolutional Neural Network autoencoder Architecture

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IEEE ACCESS 2021年 9卷 147888-147899页

作者： Saponara, Sergio Elhanashi, Abdussalam Zheng, Qinghe Univ Pisa Dipartimento Ingn Informaz I-56126 Pisa Italy Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China

Fingerprint recognition systems have been applied widely to adopt accurate and reliable biometric identification between individuals. Deep learning, especially Convolutional Neural Network (CNN) has made a tremendous success in the field of computer vision for pattern recognition. Several approaches have been applied to reconstruct fingerprint images. However, these algorithms encountered problems with various overlapping patterns and poor quality on the images. In this work, a convolutional neural network autoencoder has been used to reconstruct fingerprint images. An autoencoder is a technique, which is able to replicate data in the images. The advantage of convolutional neural networks makes it suitable for feature extraction. Four datasets of fingerprint images have been used to prove the robustness of the proposed architecture. The dataset of fingerprint images has been collected from various real resources. These datasets include a fingerprint verification competition (FVC2004) database, which has been distorted. The proposed approach has been assessed by calculating the cumulative match characteristics (CMC) between the reconstructed and the original features. We obtained promising results of identification rate from four datasets of fingerprints images (Dataset I, Dataset II, Dataset III, Dataset IV) with 98.1%, 97%, 95.9%, and 95.02% respectively by CNN autoencoder. The proposed architecture was tested and compared to the other state-of-the-art methods. The achieved experimental results show that the proposed solution is suitable for recreating a complex context of fingerprinting images.

关键词： Fingerprint recognition Image matching Deep learning Convolutional neural networks Feature extraction Filtering Image reconstruction Fingerprint images convolution neural networks autoencoder feature extraction system identification

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Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 4517-4533页

作者： Al-Andoli, Mohammed Cheah, Wooi Ping Tan, Shing Chiang Multimedia Univ Fac Informat Sci & Technol Jalan Ayer Keroh Lama Melaka 75450 Melaka Malaysia Univ Nottingham Ningbo China Fac Sci & Engn Sch Comp Sci Ningbo Peoples R China

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

关键词： Complex networks community detection autoencoder particle swarm optimization continuation method

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Improved autoencoder for unsupervised anomaly detection

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第12期36卷 7103-7125页

作者： Cheng, Zhen Wang, Siwei Zhang, Pei Wang, Siqi Liu, Xinwang Zhu, En Natl Univ Def Technol Sch Comp 109 Deya Rd Changsha 410073 Hunan Peoples R China

Deep autoencoder-based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish anomalies from normal data. However, this assumption does not always hold in practice, especially in unsupervised anomaly detection, where the training data is anomaly contaminated. We observe that the autoencoder generalizes so well on the training data that it can reconstruct both the normal data and the anomalous data well, leading to poor anomaly detection performance. Besides, we find that anomaly detection performance is not stable when using reconstruction error as anomaly score, which is unacceptable in the unsupervised scenario. Because there are no labels to guide on selecting a proper model. To mitigate these drawbacks for autoencoder-based anomaly detection methods, we propose an Improved autoencoder for unsupervised Anomaly Detection (IAEAD). Specifically, we manipulate feature space to make normal data points closer using anomaly detection-based loss as guidance. Different from previous methods, by integrating the anomaly detection-based loss and autoencoder's reconstruction loss, IAEAD can jointly optimize for anomaly detection tasks and learn representations that preserve the local data structure to avoid feature distortion. Experiments on five image data sets empirically validate the effectiveness and stability of our method.

关键词： autoencoder deep learning unsupervised anomaly detection

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Deep learning-driven pavement crack analysis: autoencoder-enhanced crack feature extraction and structure classification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 132卷

作者： Zhang, Miaomiao Zhong, Jingtao Zhou, Changhong Jia, Xiaoyang Zhu, Xingyi Huang, Baoshan Univ Tennessee Dept Civil & Environm Engn Knoxville TN 37996 USA Guilin Univ Elect Technol Sch Architecture & Transportat Engn Guilin 541004 Guangxi Peoples R China Tennessee Dept Transportat Nashville TN 37243 USA Tongji Univ Coll Transportat Engn Dept Rd & Airport Engn Shanghai 201804 Peoples R China

In practice, some asphalt pavements may exhibit significant cracking despite being structurally sound, or conversely, minimal cracking while having structural weaknesses. This inconsistency could be attributed to the limitation of commonly used crack features, i.e., the crack area, which is defined based on human experience and may not fully capture the crack information relevant to pavement structural conditions. To address it, this study proposed a computer -oriented method that utilizes the autoencoder to extract crack features (called encoded features) from pavement crack images, thus linking pavement cracks to their corresponding structural conditions. The original crack images (128*128 pixels) are compressed into the encoded features (4*4 pixels) by the proposed autoencoder. The encoded feature is represented digitally and is difficult for humans to understand, but it retains as much useful information as possible and is therefore a more accurate representation of pavement cracks. On this basis, random forests (RF) classifiers were developed to classify crack images into strong and weak pavement structures. RF classifiers trained on encoded features outperform those trained on original images. For crack images with more than 20 % cracks, cracks on wheel paths play the most important role in the structure classification, and their presence indicates potential pavement structural weakness. However, for crack images with less than 5 % cracks, cracks are randomly distributed and contain less structural information. Overall, it is recommended to encode the original crack images by autoencoders for data storage and RF structural classification.

关键词： Deep learning Cracking Asphalt pavement Image processing autoencoder Deflection

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A residual autoencoder-based transformer for fault detection of multivariate processes

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APPLIED SOFT COMPUTING 2024年 163卷

作者： Shang, Jilin Yu, Jianbo Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

The complexity of high-dimensional and noisy process signals reduces the effectiveness of conventional fault detection methods in industrial processes. Based on the hypothesis that data collected from normal and faulty processes has different characteristics, unsupervised deep neural networks, e.g., autoencoders, have been widely applied in process fault detection and achieved good performance. Many variants have been proposed to improve feature learning by combining different network structures. In this paper, a new transformer model, residual autoencoder-based transformer, is proposed for process fault detection. Firstly, autoencoder and transformer are integrated for better unsupervised feature learning of process signals. Secondly, linear embedding and attention mechanisms with bias are proposed to generate effective features from process signals. Finally, residual connections are constructed between the encoder and decoder of RATransformer to address overfitting in training. Four industrial cases are used to test the performance of RATransformer for process fault detection. The results show that the fault detection rate of RATransformer is at least 1 % higher than other comparison methods. Moreover, the testing results show that the model structure improves the fault detection performance of RATransformer. The complex models like RATransformer can be used in the industrial process when sufficient normal process data is available. An end-to-end training method can be further developed to improve the applicability of RATransformer in process fault detection in the future.

关键词： Industrial process Process fault detection Transformer autoencoder Feature learning Residual learning

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An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2021年第15期122卷 3780-3811页

作者： Phillips, Toby R. F. Heaney, Claire E. Smith, Paul N. Pain, Christopher C. Imperial Coll London Dept Earth Sci & Engn Appl Modelling & Computat Grp London England Jacobs Poundbury England

Using an autoencoder for dimensionality reduction, this article presents a novel projection-based reduced-order model for eigenvalue problems. Reduced-order modeling relies on finding suitable basis functions which define a low-dimensional space in which a high-dimensional system is approximated. Proper orthogonal decomposition (POD) and singular value decomposition (SVD) are often used for this purpose and yield an optimal linear subspace. autoencoders provide a nonlinear alternative to POD/SVD, that may capture, more efficiently, features or patterns in the high-fidelity model results. Reduced-order models based on an autoencoder and a novel hybrid SVD-autoencoder are developed. These methods are compared with the standard POD-Galerkin approach and are applied to two test cases taken from the field of nuclear reactor physics.

关键词： autoencoder machine learning reduced-order modeling model reduction neutron diffusion equation reactor physics

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TransCNN-HAE: Transformer-CNN Hybrid autoencoder for Blind Image Inpainting 22

TransCNN-HAE: Transformer-CNN Hybrid AutoEncoder for Blind I...

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30th ACM International Conference on Multimedia (MM)

作者： Zhao, Haoru Gu, Zhaorui Zheng, Bing Zheng, Haiyong Ocean Univ China Intelligent Informat Sensing & Proc Lab Qingdao Peoples R China Ocean Univ China Coll Elect Engn Qingdao Peoples R China Ocean Univ China Sanya Oceanog Inst Qingdao Peoples R China

ISBN: (纸本)9781450392037

Blind image inpainting is extremely challenging due to the unknown and multi-property complexity of contamination in different contaminated images. Current mainstream work decomposes blind image inpainting into two stages: mask estimating from the contaminated image and image inpainting based on the estimated mask, and this two-stage solution involves two CNN-based encoderdecoder architectures for estimating and inpainting separately. In this work, we propose a novel one-stage Transformer-CNN Hybrid autoencoder (TransCNN-HAE) for blind image inpainting, which intuitively follows the inpainting-then-reconstructing pipeline by leveraging global long-range contextual modeling of Transformer to repair contaminated regions and local short-range contextual modeling of CNN to reconstruct the repaired image. Moreover, a Crosslayer Dissimilarity Prompt (CDP) is devised to accelerate the identifying and inpainting of contaminated regions. Ablation studies validate the efficacy of both TransCNN-HAE and CDP, and extensive experiments on various datasets with multi-property contaminations show that our method achieves state-of-the-art performance with much lower computational cost on blind image inpainting. Our code is available at https://***/zhenglab/TransCNN-HAE.

关键词： Blind Image Inpainting Transformer CNN autoencoder

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