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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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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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Reduced-order modeling via convolutional autoencoder for emulating combustion of hydrogen/methane fuel blends

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COMBUSTION AND FLAME 2025年 274卷

作者： Ding, Siyu Ni, Chenxu Chu, Xu Lu, Qingzhou Wang, Xingjian Tsinghua Univ Dept Energy & Power Engn Beijing 100084 Peoples R China

Numerical simulations are essential in comprehending the processes of flow and combustion in aerospace and power-generation systems. Yet, substantial computational demands render these simulations prohibitively expensive for design and optimization, owing to the large amount of design parameters to be surveyed in a wide design space. This study presents parametric reduced order models (ROMs) that leverage deep neural network- based dimension reduction through a convolutional autoencoder (AE) to emulate spatial distributions of physical flowfields in combustion problems. The proposed AE-based ROMs integrate multiple advancements, including design of experiment, nonlinear dimension reduction, and regression methods like kriging and deep neural networks (DNN) to enable parametric predictability. For comparison, proper orthogonal decomposition (POD)- based ROMs are also developed. Two distinct test scenarios are outlined: steam-diluted methane/hydrogenblending oxy-combustion from a triple-coaxial nozzle and hydrogen-enriched combustion in a practical aeroderivative combustor. Results suggest that ROMs with kriging show superior performance against those with DNN. In both test scenarios, AE-based ROMs exhibit better prediction accuracy in emulating spatial distributions for physical variables of interest than POD-based ROMs. This is mainly due to the effective capture of nonlinear features by AE through intricate network structure. Such nonlinearity effect is examined by introducing interpretable "AE modes", which demonstrate multiscale characteristics, distinct from POD modes, enabling a more detailed representation of local flow features. Both AE- and POD-based ROMs achieve a dramatic acceleration in prediction process, 8-9 orders of magnitude faster than traditional combustion simulations. While POD-based ROMs slightly underperform concerning prediction accuracy, they remain competitive in terms of training and prediction efficiency with a limited number of reduced ba

关键词： Reduced-order model (ROM) Kriging convolutional autoencoder Nonlinear feature Proper orthogonal decomposition

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A Fuzzy-Operated convolutional autoencoder for Classification of Wearable Device-Collected Electrocardiogram

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第1期33卷 267-277页

作者： Xing, Lumin Li, Xin Liu, Wenjian Wang, Xing Shandong First Med Univ Jinan 250014 Peoples R China Shandong Prov Qianfoshan Hosp Jinan 250014 Peoples R China City Univ Macau Taipa 999078 Peoples R China Shandong Univ Polit Sci & Law Jinan 250014 Peoples R China Shandong First Med Univ & Shandong Acad Med Sci Jinan 250117 Peoples R China

Utilizing electrocardiogram (ECG) to assess patients' cardiac health is currently the popular diagnostic method. This approach demonstrates high accuracy and helps alleviate the workload of medical professionals. However, the ECG signals consist of physiological signals with low-frequency and low-amplitude features, including various interference noises. Traditional feature extraction methods of classification models have proven ineffective in handling this data type. Moreover, previous classification models fail to capture the spatial and local structures within ECG data, resulting in learned features that do not include the key features of ECG signals. In addition, traditional models often adopt neural network as prediction modules, which are likewise inefficient in handling noise within the data. To end these issues, we propose a fuzzy-operated convolutional autoencoder (FOCAE) for the classification of ECG collected from wearable devices. The FOCAE model integrates convolutional autoencoder and fuzzy neural network, enabling it to learn high-quality features from noisy data to ensure the model's inferential capability. Furthermore, the FOCAE model inputs the learned features into the fuzzy neural network and fully connected network to obtain the classification results of ECG. Using fuzzy rules in the fuzzy neural network to describe the mapping between features and classification results enhances the robustness of the predictive outcomes. Some experimental results on a real ECG dataset validate the superior performance of the FOCAE model. The performance of the FOCAE model is superior to other three baseline models. Specifically, the Precision, Accuracy, and F1-Score of the FOCAE model are 0.971, 0.968, and 0.965, respectively.

关键词： Electrocardiography Feature extraction Data models Predictive models Arrhythmia Noise Fuzzy neural networks Classification task convolutional autoencoder electrocardiography fuzzy neural network (FNN) wearable devices

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One-dimensional decoupled convolutional autoencoder with sparse self-attention mechanism for process monitoring

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 199卷

作者： Yang, Yuguo Shi, Hongbo Song, Bing Tao, Yang Guo, Lei East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Industrial processes are constantly disturbed by environmental and human factors during operation. Undifferentiated alarming of these disturbances will bring serious alarm disaster *** distinguishing the disturbances that have different effects on the process operation state can help the field operators to make a reasonable risk *** achieve the above purposes, this paper proposes a one-dimensional decoupled convolutional autoencoder network with sparse self-attention mechanism under process knowledge constraints (PKC-SSAM-DCAE). Firstly, aiming at the change of data distribution caused by feedback control adjustment, the window normalization strategy is adopted for the standardized data. Realize data distribution alignment at the input end of the model. Subsequently, one-dimensional decoupled convolutional encoder (DCAE) is constructed to extract the features of each process variable. The sparse self-attention mechanism network (SSAM) is constructed under the constraint of process knowledge to realize the interaction between process variable features. Then the detection index is established according to the network prediction results. When the fault is detected, the variable oblivion contribution plot is given to locate the key fault ***, through the experiments on Tennessee Eastman process, it is verified that the proposed model can solve the problem of data distribution change caused by process feedback adjustment, and can accurately distinguish process normal adjustment from faults.

关键词： convolutional autoencoder Process monitoring Data distribution alignment Variable-level feature extraction and interaction Process knowledge embedding

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Near-lossless medical image compression using wavelet subband thresholding and convolutional autoencoder

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2025年第3期28卷 329-345页

作者： Venugopal, Muthalaguraja Palanisamy, Kalavathi Viswanathan, Punitha Deemed be Univ Gandhigram Rural Inst Dept Comp Sci & Applicat Gandhigram Tamil Nadu India

In the digital era of healthcare, telemedicine services are continuously evolving. Medical images with massive file sizes increase storage and transmission complexity while providing telemedicine services. To address this, image compression becomes obligatory. Learning-based methods show promising results in image compression tasks. However, the problem of maintaining image reconstruction quality still needs to be addressed. This work proposes a wavelet-based convolutional autoencoder for near-lossless medical image compression. Thresholded wavelet subbands of medical images were used to train the compression model. A convolutional encoder-decoder model with a simple encoder network and an extended decoder network is proposed to achieve a near-lossless image compression standard. A combined loss function is employed to improve the model's reconstruction performance. The combined loss function includes mean squared error and structural similarity index metric, focusing on image reconstruction quality. Extensive experiments show the efficiency of the proposed method over the existing image compression techniques.

关键词： image compression CNN wavelet convolutional autoencoder CAE deep learning medical image compression

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