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convolutional autoencoder-based ground motion clustering and selection

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2025年 191卷

作者： Jia, Yiming Sasani, Mehrdad Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA 94720 USA Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA

Ground motion selection has become increasingly central to the assessment of earthquake resilience. The selection of ground motion records for use in nonlinear dynamic analysis significantly affects structural response. This, in turn, will impact the outcomes of earthquake resilience analysis. This paper presents a new ground motion clustering algorithm, which can be embedded in current ground motion selection methods to properly select representative ground motion records that a structure of interest will probabilistically experience. The proposed clustering-based ground motion selection method includes four main steps: 1) leveraging domainspecific knowledge to pre-select candidate ground motions;2) using a convolutional autoencoder to learn low-dimensional underlying characteristics of candidate ground motions' response spectra - i.e., latent features;3) performing k-means clustering to classify the learned latent features, equivalent to cluster the response spectra of candidate ground motions;and 4) embedding the clusters in the conditional spectra-based ground motion selection. The selected ground motions can represent a given hazard level well (by matching conditional spectra) and fully describe the complete set of candidate ground motions. Three case studies for modified, pulse-type, and non-pulse-type ground motions are designed to evaluate the performance of the proposed ground motion clustering algorithm (convolutional autoencoder +k-means). Considering the limited number of pre-selected candidate ground motions in the last two case studies, the response spectra simulation and transfer learning are used to improve the stability and reproducibility of the proposed ground motion clustering algorithm. The results of the three case studies demonstrate that the convolutional autoencoder +k-means can 1) achieve 100 % accuracy in classifying ground motion response spectra, 2) correctly determine the optimal number of clusters, and 3) outperform established cluster

关键词： Ground motion selection convolutional autoencoder K -means clustering Response spectra Conditional spectra Machine learning

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AGCTO: Attributed Graph Clustering With Transitive Order convolutional autoencoder

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Xie, Ying Wang, Jixiang Liu, Weihua Wang, Hao Pan, Yushan Wen, Lijie Xu, Rongbin Ding, Weiping Yang, Yun Sch Mech Elect & Informat Engn Putian 351100 Peoples R China Putian Univ Fujian Key Lab Financial Informat Proc Putian 351100 Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Xian Jiaotong Liverpool Univ Dept Comp Suzhou 215123 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China City Univ Macau Facultyof Data Sci Macau 999078 Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3022 Australia

Attributed graphs, which associate vertices or edges with attributes, are essential in applications like social network analysis, recommendation systems, bioinformatics, and healthcare analysis. However, their high-dimensional and non-Euclidean nature complicates tasks such as vertex classification, community detection, graph visualization, and graph embedding. Existing techniques often inadequately integrate structural and attribute data, and reconstruction-centric models can compromise graph topology. Additionally, many methods require extensive manual hyperparameter tuning. To address these challenges, we introduce Attributed Graph Clustering with Transitive Order convolutional autoencoder (AGCTO). AGCTO uses a Graph Transitive convolutional autoencoder (GTCAE) framework that integrates structural and attribute information. A key innovation is the Graph ORder Distance (GORD), capturing topological relationships to enhance clustering performance. AGCTO's unified loss function combines GTCAE loss on the original graph, GTCAE loss on a simplified graph via GORD, and a topology-preserving loss. This ensures effective fusion of attribute and structure data and preserves graph topology. Evaluations on four real-world and two synthetic attributed graphs show AGCTO's superiority in clustering accuracy, normalized mutual information, F1-score, and Q-modularity. AGCTO offers a robust solution for attributed graph clustering, maintaining graph topology and delivering superior performance.

关键词： autoencoders Feature extraction Topology Vectors Fuses Decoding Data mining Manuals Image edge detection convolutional codes GNNs clustering embedding convolutional autoencoder

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A hybrid convolutional autoencoder training algorithm for unsupervised bearing health indicator construction

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 139卷

作者： Milani, Ali Eftekhari Zappala, Donatella Watson, Simon J. Delft Univ Technol Kluyverweg 1 NL-2629 HS Delft Netherlands

Conventional Deep Learning (DL) methods for bearing health indicator (HI) adopt supervised approaches, requiring expert knowledge of the component degradation trend. Since bearings experience various failure modes, assuming a particular degradation trend for HI is suboptimal. Unsupervised DL methods are scarce in this domain. They generally maximise the HI monotonicity built in the middle layer of an autoencoder (AE) trained to reconstruct the run-to-failure signals. The backpropagation (BP) training algorithm is unable to perform this maximisation since the monotonicity of HI subsections corresponding to input sample batches does not guarantee the monotonicity of the whole HI. Therefore, existing methods achieve this by searching AE hyperparameters so that its BP training to minimise the reconstruction error also leads to a highly monotonic HI in its middle layer. This is done using expensive search algorithms where the AE is trained numerous times using various hyperparameter settings, rendering them impractical for large datasets. To address this limitation, a small convolutional autoencoder (CAE) architecture and a hybrid training algorithm combining Particle Swarm Optimisation and BP are proposed in this work to enable simultaneous maximisation of the HI monotonicity and minimisation of the reconstruction error. Asa result, the HI is built by training the CAE only once. The results from three case studies demonstrate this method's lower computational burden compared to other unsupervised DL methods. Furthermore, the CAE-based HIs outperform the indicators built by equivalent and significantly larger models trained with a BP-based supervised approach, leading to 85% lower remaining useful life prediction errors.

关键词： Particle Swarm Optimisation convolutional autoencoder Bearing Health indicator Prognostics Remaining useful life prediction

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CAE SynthImgGen: Revolutionizing cancer diagnosis with convolutional autoencoder-based synthetic image generation

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ALEXANDRIA ENGINEERING JOURNAL 2025年 115卷 343-354页

作者： Hangaragi, Shivalila Neelima, N. Venugopal, Vivek Ganguly, Somnath Mudi, Joyti Choi, Joon-Ho Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Bengaluru India Chonnam Natl Univ Dept Elect Engn Gwangju South Korea Bankura Unnayani Inst Engn Dept Elect Engn Bankura W Bengal India

Medical imaging is pivotal in modern healthcare, offering a visual window into the human body's intricate structures and functions. However, the scarcity of diverse and representative medical images significantly limits research progress. Today, deep learning algorithms are increasingly incorporated into the medical imaging domain to automate the diagnostic process. The success of these algorithms relies heavily on vast and diverse datasets. Insufficient data hampers the training and validation of these algorithms, resulting in suboptimal performance, biased results, and reduced generalizability. This paper introduces a pioneering approach that employs a convolutional autoencoder (CA) to synthetically generate medical images. The synthetic images produced are then added to the existing database to create an augmented dataset. This augmented dataset is subsequently used for classification with a convolutional neural network. Experiments were conducted on publicly available datasets-the chest CT-scan dataset and the IQ-OTH/NCCD lung cancer dataset. The synthetic image generation capability of the CA was compared with traditional augmentation methods such as flipping, rotating, shearing, shifting, zooming, and sub-sampling. Results showed that the CA-based augmented dataset achieved an accuracy of 91 %, compared to 83 % with the traditional augmentation-based dataset.

关键词： convolutional autoencoder Synthetic image generation convolutional neural network SSIM Lung cancer classification

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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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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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Unsupervised defect detection for solar photovoltaic cells based on convolutional autoencoder

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NONDESTRUCTIVE TESTING AND EVALUATION 2025年

作者： Zhang, Yufei Zhang, Xu Tu, Dawei Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

Solar photovoltaic (PV) cells are inevitably subject to defects during the production process, affecting their power generation efficiency and life. Electroluminescence (EL) imaging is the mainstream non-destructive method for PV cell defect detection. Aiming at PV cell EL images, an unsupervised defect detection method was proposed. Specifically, an unsupervised convolutional autoencoder (CAE), the scale structure perception convolutional autoencoder (SSP_CAE), was constructed, whose Squeeze-and-Excitation Attention (SE Attention) and skip connections avoid the blurring of image structure information and the loss of pixel-level details in the encoding and decoding process. Furthermore, to balance the global and local information of the image, a scale perception loss function called SP_SSIM was proposed for model training. The defect segmentation was achieved by using Otsu thresholding method to binarize the obtained Mean Absolute Error (MSE) residual heat image in the testing stage of the model. Finally, the experiments were performed on the test dataset and the experimental results showed that the proposed SSP_CAE can effectively detect PV cell defects. The experimentally obtained defect detection performance metrics Precision, Recall, IoU, F1-score and AUROC values were 0.739, 0.886, 0.723, 0.764 and 0.841, respectively. Compared with other classical methods, the proposed SSP_CAE had a better comprehensive performance for defect detection.

关键词： Defect detection PV cell convolutional autoencoder unsupervised learning

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Anomaly detection using convolutional autoencoder with residual gated recurrent unit and weak supervision for photovoltaic thermal heat pump system

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JOURNAL OF BUILDING ENGINEERING 2025年 100卷

作者： John, Lukudu Samuel Yoon, Sungmin Li, Jiteng Wang, Peng Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China Sungkyunkwan Univ Dept Global Smart City Suwon 16419 South Korea Sungkyunkwan Univ Sch Civil Architectural Engn & Landscape Architect Suwon 16419 South Korea Sungkyunkwan Univ Coll Engn Built Environm Res Ctr Suwon 16419 South Korea

Utilizing convolutional autoencoders with Residual Bi-Directional Gated Recurrent Unit bottleneck offers an advanced approach for detecting anomalies in heat pumps. Given the importance of heat pumps in achieving decarbonization goals for residential heating and cooling, accurate diagnosis of their health issues is essential for improving their efficiency and reducing environmental impact. Traditional diagnostic techniques, such as visual inspection, thermography, electrical testing, pressure measurements and temperature differentials require skilled technicians. These methods face some challenges due to the complexities of heat pump systems, which include components like compressors, coils, valves, and therefore require expertise in diagnosing their interactions. Our approach leverages convolutional autoencoders with Gated Recurrent Unit and Weak supervision to automatically detect anomalies and label significantly accelerating the diagnostic process. Our results show that thresholds based on the rolling mean outperform thresholds based on the actual errors by 6-10 %, depending on the random seeds. Additionally, the weak labels generated exhibit a positive correlation of at least 0.5 with error thresholds and 0.62 with rolling mean predictions.

关键词： Anomaly detection convolutional autoencoder Weak supervision Photovoltaic thermal heat pump system Machine learning Unsupervised learning

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Adversarial Defense Based on Denoising convolutional autoencoder in EEG-Based Brain-Computer Interfaces

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IEEE ACCESS 2024年 12卷 146441-146452页

作者： Ding, Yongting Li, Lin Li, Qingyan Jimei Univ Sch Sci Xiamen 361021 Peoples R China Jimei Univ Comp Engn Coll Xiamen 361021 Peoples R China Jimei Univ Digital Fujian Big Data Modeling & Intelligent Com Xiamen 361021 Peoples R China

The exploration and implementation of brain-computer interfaces (BCIs) utilizing electro-encephalography (EEG) are becoming increasingly widespread. However, their safety considerations have received scant attention. Recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks. Remarkably, only a limited amount of literature has addressed adversarial defense strategies against EEG-based BCIs. This study introduces a defense approach based on autoencoders, termed the Denoising convolutional autoencoder (DCAE), which serves as a preprocessing unit preceding the classification model. The DCAE aims to mitigate adversarial disturbances prior to inputting samples into the classifier, thereby preserving the classifier's original structure. Experiments were conducted using two different EEG datasets and three convolutional neural network (CNN) models to evaluate the effectiveness of DCAE. The experimental results show that the proposed method can achieve better defense effect in most cases against various adversarial attack methods. Additionally, the sensitivity of the DCAE to different magnitudes of perturbation was evaluated. The findings indicate that the robustness of DCAE is not affected by the variation of attack intensity, a characteristic not observed in existing defense strategies for EEG-based BCIs. It is our aspiration that these results will advance the frontier of research on defending EEG-based BCIs against adversarial threats.

关键词： Brain-computer interface EEG adversarial defense denoising convolutional autoencoder

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CAERS-CF: enhancing convolutional autoencoder recommendations through collaborative filtering

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第11期66卷 6717-6738页

作者： Ghadami, Amirhossein Tran, Thomas Univ Ottawa Sch Elect Engn & Comp Sci EECS 800 King Edward Ave Ottawa ON K1N 6N5 Canada

Recommendation systems are crucial in boosting companies' revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users' and items' content data, and second, a traditional recommendation system that employs users' past behaviour data. We introduce a novel deep learning-based recommendation system called convolutional autoencoder recommendation system (CAERS). It uses a convolutional autoencoder (CAE) to capture high-order meaningful relationships between users' and items' content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users' past behaviour data, utilizing singular value decomposition (SVD). Finally, in the last step, we combine the two method's predictions with linear regression. We determine the optimal weight for each prediction generated by the collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Across both datasets, the hybrid CAERS-CF model demonstrates an average RMSE improvement of approximately 3.70% and an average MAE improvement of approximately 5.96% compared to the next best models.

关键词： Recommendation system Deep learning convolutional autoencoder Hybrid recommendation system Collaborative filtering

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