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deep autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP

APPLIED SCIENCES-BASEL 2025年第3期15卷 1209-1209页

作者： Barile, Claudia Casavola, Caterina Mpoyi, Dany Katamba Pappalettera, Giovanni Politecn Bari Dipartimento Meccan Matemat & Management Via E Orabona 4 I-70125 Bari Italy

This study investigates the classification of damage modes in adhesively bonded carbon fiber-reinforced plastic (CFRP) composites, a critical factor in advancing lightweight automotive design. Adhesive bonding, replacing traditional riveting, improves structural integrity while reducing weight and CO2 emissions. Mechanical testing on CFRP composites was performed, and acoustic emission (AE) signals were collected to evaluate damage mechanisms. A deep autoencoder (DAE) framework was developed to automate damage characterization by reducing AE signal dimensionality through singular value decomposition (SVD) and classifying features using the k-means algorithm. This approach effectively identified three primary damage modes: matrix cracking, interfacial debonding, and fiber breakage. Traditional AE features, such as entropy and amplitude were also classified and validated using spectral analysis. The DAE-based strategy demonstrated superior capability in real-time damage mode differentiation. Fractographic analysis confirmed crack growth in the adhesive layer, leading to interfacial debonding, fiber-matrix separation, and eventual fiber rupture. These findings highlight the DAE framework's effectiveness in enhancing damage mode characterization, offering valuable insights for optimizing the structural performance of bonded CFRP composites in automotive applications.

关键词： acoustic emission deep autoencoder k-means CFRP composites adhesive bonding joggled lap shear joints structure lightweighting

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A deep autoencoder with structured latent space for process monitoring and anomaly detection in coal-fired power units

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 261卷

作者： Hu, Di Zhang, Chen Yang, Tao Fang, Qingyan Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China Hubei Univ Technol Hubei Engn Res Ctr Safety Monitoring New Energy & Wuhan 430068 Peoples R China

In the big data era, deep autoencoder (DAE)-based methods for anomaly detection are widely used in monitoring coal-fired power units (CFPUs). However, these methods often overlook essential latent space information crucial for detecting anomalies within the DAE model. This study presents a structured latent space deep autoencoder (SLSDAE) that not only intuitively provides both latent space and reconstruction residual information for anomaly detection but also obviates the need for additional hyperparameters in the model's loss function. Furthermore, by leveraging the support vector data description (SVDD) model, this research extracts anomaly discrimination criteria from the SLSDAE model and introduces an end-to-end, real-time online monitoring framework for CFPUs. Comparative analysis on four public datasets demonstrates that the SLSDAE model enhances the G-mean in anomaly detection by 16.05 % over the DAE model and surpasses the performance of both the beta VAE and DAGMM models. When applied to an actual induced draft fan, this framework effectively provides clear status trend tracking and early anomaly detection, up to 20 days in advance.

关键词： Coal-fired power units Condition monitoring deep autoencoder Anomaly detection Latent space information

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Unsupervised detection of multivariate geochemical anomalies using a high-performance deep autoencoder Gaussian mixture model

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JOURNAL OF GEOCHEMICAL EXPLORATION 2025年 271卷

作者： Wang, Xuemei Chen, Yongliang Jilin Univ Coll Earth Sci Changchun 130061 Jilin Peoples R China

It is of great significance to construct an efficient geochemical anomaly detection model for the successful accomplishment of a mineral exploration process in a complex geological environment. However, the complex geological environment of the prospecting area often results in the high-dimensional unknown complex population distribution of geochemical exploration data. This complex distribution is difficult to fit with a theoretical probability distribution model. As a result, it becomes a challenge to carry out an effective detection of geochemical anomalies. Therefore, to develop an anomaly detection model that can effectively fit the complex population distribution of geochemical exploration data is the key for accurately detecting geochemical anomalies. For this reason, the deep autoencoder Gaussian mixture model (DAGMM) was adopted to model the geochemical exploration data obtained in the 1:200,000 geological survey conducted in the Baishan area (Jilin, China) to check its superiority in identifying multivariate geochemical anomalies. As an innovative deep learning framework for unsupervised anomaly detection, DAGMM ingeniously combines the data dimensionality reduction and compression capabilities of a deep autoencoder (DAE) with the probability density estimation advantage of the Gaussian mixture model (GMM). The DAGMM model can deeply explore the deep-level features of geochemical exploration data and effectively model the complex unknown data distribution through the synergistically work and joint optimization strategy in training the DAE and GMM model, so it can accurately identify geochemical anomalies. To show the superiority of the DAGMM model in detecting polymetallic geochemical anomalies, the DAGMM model was compared with the GMM and DAE models. The receiver operating characteristic (ROC) curves of the three models were plotted, and the areas under the ROC curves (AUCs) and lift indices were calculated. The ROC curve of the DAGMM model dominates tha

关键词： deep autoencoder Gaussian mixture model deep autoencoder Gaussian mixture model Mineral exploration Geochemical exploration data Multivariate anomaly detection

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deep autoencoder based hybrid dimensionality reduction approach for classification of SERS for melanoma cancer diagnostics

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期43卷 7647-7661页

作者： Yousuff, Mohamed Babu, Rajasekhara VIT Univ Vellore Inst Technol Vellore Tamil Nadu India

Melanoma, a kind of fatal skin cancer, originates in melanin secreting cells of the dermis. Disease identification in the early stages assures a high survival rate for the patient. Most of the existing techniques retard the cancer detection phase. Surface-Enhanced Raman Spectroscopy (SERS) can capture fine details from the specimens that machine learning models can utilize to discriminate between healthy and diseased individuals rapidly. Our research work proposes a deep autoencoder based hybrid dimensionality reduction approach with a machine learning model on SERS spectrums of human skin fibroblast for melanoma cancer diagnostics. SERS measurements of 307 samples in total, belonging to two different classes, such as normal (157 samples) and malignant melanoma (150 samples), are used in this study. The SERS spectra measurements for both the samples lie between 100cm(-1) and 4278cm(-1). The variations in the intensity of Raman bands between both classes are intrinsically subtle. Neighborhood Component Analysis (NCA) technique has been exerted to transform 2090 dimensional spectral features into 2090 dimensional vectors and then the deep autoencoder (DAE) model is used to handle the nonlinearity in the data and produce the latent space, while Linear Discriminant Analysis (LDA) classifier have been employed for discriminating the normal and cancer cells. The k-fold cross-validation technique with a k value of 10 is implemented to assess the metrics of the model. The stated hybrid (NCA and DAE) model with 10-dimension latent space achieves an accuracy of 98%, the sensitivity of 99% and specificity of 97%, respectively. Due to the high-intensity nature of the SERS spectrum, the existing linear dimensionality reduction based discriminating model fails if the class label (Normal or Cancer) gets distributed on the low variance side. The proposed methodology captures both linear and nonlinear underlying structures present in the spectrums, resulting in better classification

关键词： Dimensionality reduction neighborhood component analysis deep autoencoder linear discriminant analysis surface enhanced raman spectroscopy melanoma

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deep autoencoder for Mass Spectrometry Feature Learning and Cancer Detection

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IEEE ACCESS 2020年 8卷 45156-45166页

作者： Zhou, Qingguo Yong, Binbin Lv, Qingquan Shen, Jun Wang, Xin Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730030 Peoples R China Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730030 Peoples R China State Grid Gansu Elect Power Res Inst Lanzhou 730030 Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia

Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used to select optimized features from mass spectrometry data. The learned features are further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also cancer diagnosis.

关键词： Cancer Mass spectroscopy Cancer detection Feature extraction Tumors Proteomics Machine learning Early cancer diagnosis deep autoencoder particle swarm optimization mass spectrometry feature learning

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Satellite-based remote sensing analysis for the exploration of MVT Pb-Zn mineralization using an integrated approach of minimum distance classification, deep autoencoder and fuzzy logic modeling

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Remote Sensing Applications: Society and Environment 2025年 38卷

作者： Qaderi, Soran Maghsoudi, Abbas Pour, Amin Beiranvand Yousefi, Mahyar Department of Mining Engineering Amir Kabir University of Technology Tehran Iran Institute of Oceanography and Environment (INOS) University Malaysia Terengganu (UMT) Kuala Nerus Terengganu 21030 Malaysia Department of Mining Engineering Faculty of Engineering Malayer University Malayer Iran

Mississippi Valley-type (MVT) Pb-Zn mineralization is a key economic resource, yet its exploration is challenging due to complex alteration patterns and high costs. This study integrates ASTER satellite imagery with deep learning to enhance prospectivity mapping. We applied image processing techniques, including Principal Component Analysis (PCA), Band Ratios (BR), Band Math (BM), and Spectral Angle Mapper (SAM), to identify alteration zones. The Minimum Distance Classification (MDC) method classified these zones, extracting key evidence layers. These layers—dolomitization (MDC-PCA, SAM) and carbonate-iron oxide (MDC-BR, MDC-BM)—were integrated using deep autoencoder (DAE) and Fuzzy Logic Modeling (GFO) to generate prospectivity maps. Prediction-area (P-A) plots showed the DAE model outperformed GFO, achieving a normalized density (Nd) of 4.1 compared to 3.61 for GFO, indicating a more precise delineation of high-potential mineralization zones. Field validation confirmed strong alignment with known Pb-Zn occurrences. This study highlights the effectiveness of remote sensing and deep learning in cost-effective mineral exploration and provides a scalable framework for similar metallogenic provinces. © 2025 Elsevier B.V.

关键词： ASTER deep autoencoder Fuzzy logic modeling Minimum distance classification MVT Pb-Zn mineralization mapping

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deep autoencoder on Personalized Facet Selection 26th

Deep Autoencoder on Personalized Facet Selection

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Chantamunee, Siripinyo Wong, Kok Wai Fung, Chun Che Murdoch Univ Coll Sci Hlth Engn & Educ Discipline Informat Technol Math & Stat Perth WA Australia

ISBN: (纸本)9783030368081;9783030368074

Information overloading leads to the need for an efficient search tool to eliminate a considerable amount of irrelevant or unimportant data and present the contents in an easy-browsing form. Personalized faceted search has been one of the potential tools to provide a hierarchical list of facets or categories that helps searchers to organize the information of the search results. Facet selection is one of the important steps to pursue a good faceted search. Collaborative-based personalization was introduced to facet selection. Previous studies have been performed on the use of Collaborative Filtering techniques for personalized facet selection. However, none of the study has investigated Artificial neural network techniques on personalized facet selection. Therefore, this study aims to investigate the possible use of deep autoencoder on the prediction of facet interests. autoencoder model was applied to address the association of collaborative interest in facets. The experiments were conducted on 100K and 1M rating records of Movielen dataset. Rating score was used to represent the explicit feedback on facet interests. The performance was reported by comparing the proposed technique and the state-of-the-art model-based Collaborative Filtering techniques in terms of prediction accuracy and computational time. The results showed that the proposed autoencoder-based model achieved better performance and it was able to significantly improve the prediction of personal facet interests.

关键词： deep autoencoder Faceted search Personalized facet selection

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Diagnosis of Incipient Fault Based on Sliding-Scale Resampling Strategy and Improved deep autoencoder

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IEEE SENSORS JOURNAL 2020年第15期20卷 8336-8348页

作者： Yang, Jing Yang, Yanxi Xie, Guo Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China

Currently, the fault diagnosis with balanced data and distinct characteristics has received mass concern, and the related research achievements are remarkable. However, because of the weakness and scarcity of incipient fault signals, the diagnosis of incipient fault commonly existing in industrial systems is still an intractable problem. In order to solve the problem, an incipient fault diagnosis method based on a sliding-scale resampling strategy and improved sparse autoencoder with multi-particle noise addition (MpNA-SAE) is proposed in this paper. Firstly, the original time domain signals are preprocessed, and a sliding-scale resampling strategy is designed to construct the balanced sample sets. Secondly, a multi-particle noise addition strategy and an adaptive loss function are designed, and then an improved sparse autoencoder with multi-particle noise addition (MpNA-SAE) fault diagnosis model is constructed to identify the fault pattern and determine the severity degree. Thirdly, a diagnostic performance evaluation criterion is proposed to quantify the application range of the model. Finally, the effectiveness and practicability of the proposed method are verified by incipient artificial damage and real fault experiments, respectively.

关键词： Fault diagnosis Feature extraction Vibrations Rolling bearings Sensors Time-domain analysis Noise measurement deep autoencoder fault diagnosis incipient fault multi-particle noise addition multiple constraint regularization term sliding-scale resampling

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DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation

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BMC BIOINFORMATICS 2024年第1期25卷 139页

作者： Liu, Yanling Zhang, Ruiyan Dong, Xiaojing Yang, Hong Li, Jing Cao, Hongyan Tian, Jing Zhang, Yanbo Shanxi Med Univ Sch Publ Hlth Dept Hlth Stat Taiyuan Peoples R China Changzhi Med Coll Dept Immunol Changzhi Peoples R China Shanxi Med Univ Hosp 1 Dept Cardiol Taiyuan Peoples R China Shanxi Prov Key Lab Major Dis Risk Assessment Taiyuan Peoples R China Shanxi Univ Chinese Med Sch Hlth Serv & Management Jinzhong Peoples R China

Background MicroRNA (miRNA) has been shown to play a key role in the occurrence and progression of diseases, making uncovering miRNA-disease associations vital for disease prevention and therapy. However, traditional laboratory methods for detecting these associations are slow, strenuous, expensive, and uncertain. Although numerous advanced algorithms have emerged, it is still a challenge to develop more effective methods to explore underlying miRNA-disease *** In the study, we designed a novel approach on the basis of deep autoencoder and combined feature representation (DAE-CFR) to predict possible miRNA-disease associations. We began by creating integrated similarity matrices of miRNAs and diseases, performing a logistic function transformation, balancing positive and negative samples with k-means clustering, and constructing training samples. Then, deep autoencoder was used to extract low-dimensional feature from two kinds of feature representations for miRNAs and diseases, namely, original association information-based and similarity information-based. Next, we combined the resulting features for each miRNA-disease pair and used a logistic regression (LR) classifier to infer all unknown miRNA-disease interactions. Under five and tenfold cross-validation (CV) frameworks, DAE-CFR not only outperformed six popular algorithms and nine classifiers, but also demonstrated superior performance on an additional dataset. Furthermore, case studies on three diseases (myocardial infarction, hypertension and stroke) confirmed the validity of DAE-CFR in *** DAE-CFR achieved outstanding performance in predicting miRNA-disease associations and can provide evidence to inform biological experiments and clinical therapy.

关键词： MiRNA-disease associations Integrated similarity Logistic function transformation deep autoencoder Combined feature representation

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A deep autoencoder with sparse and graph Laplacian regularization for characterizing dynamic functional connectivity during brain development

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NEUROCOMPUTING 2021年 456卷 97-108页

作者： Qiao, Chen Hu, Xin-Yu Xiao, Li Calhoun, Vince D. Wang, Yu-Ping Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA Tulane Univ Ctr Genom & Bioinformat New Orleans LA 70112 USA Georgia State Univ Georgia Inst Technol Triinst Ctr Translat Res Neuroimaging & Data Sci Atlanta GA 30303 USA Emory Univ Atlanta GA 30303 USA

deep-layer autoencoder (DAE) provides a powerful way for medical image analysis, while it remains a daunting challenge due to the limited samples but high dimension. In this paper, a DAE with sparse and graph Laplacian regularization, termed as GSDAE, is presented to identify significant differences of dynamic functional connectivity (dFC) between child and young adult groups. The proposed model incor-porates prior knowledge into sparse learning, i.e., the intrinsic structural information defined by manifold in the data. In this way, the reconstruction ability of unsupervised DAE can be improved, which facilitates the extraction of most discriminative features of dFC changing with age. Results on the fMRI data from the Philadelphia Neurodevelopmental Cohort project reveal essential differences lying in the reoccur-rence patterns of dFC and in the connectivity of resting state networks with increasing age, e.g., there exist different trajectories of connectivity patterns in brain functions: those associated with complex cog-nitive functions generally decreased, while those associated with basic visual or motor control functions usually enhanced. In addition, the brain circuitry moves from segregation to integration during brain development. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep autoencoder Graph regularization Sparsity Dynamic functional connectivity Brain development

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