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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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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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TDEC: Evidential Clustering Based on Transfer Learning and deep autoencoder

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第10期32卷 5585-5597页

作者： Jiao, Lianmeng Wang, Feng Liu, Zhun-Ga Pan, Quan Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Evidential clustering is a promising clustering framework using Dempster-Shafer belief function theory to model uncertain data. However, evidential clustering needs to estimate more parameters compared with other clustering algorithms, and thus the clustering performance of evidential clustering will be greatly affected if data is insufficient or contaminated. In addition, the existing evidential clustering algorithms can not well deal with high-dimensional data such as texts and images. To solve the above problems, an evidential clustering algorithm based on transfer learning and deep autoencoder (TDEC) is proposed. The TDEC utilizes deep autoencoder to obtain evidential clustering-friendly representations of the original data, and applies the maximum mean discrepancy (MMD) constraint between the source network and the target network, so that the network can learn domain-invariant features. The algorithm jointly trains the deep evidential clustering networks in the source domain and the target domain, and realizes the deep feature representations of high-dimensional data in the target domain for evidential clustering by minimizing reconstruction loss, entropy-based evidential clustering loss, MMD loss and the regular penalty term of the network parameters. In addition, an iterative optimization method to solve the TDEC objective function is proposed. Extensive experiments were conducted to evaluate the clustering performance of the proposed TDEC algorithm compared with the existing shallow transfer clustering algorithms and deep clustering algorithms. For both image and text clustering tasks, the proposed TDEC achieved approximately 5% performance improvement over the comparison algorithms on average. In addition, the practical application value of the proposed TDEC algorithm was demonstrated in unsupervised remote sensing image scene classification.

关键词： Clustering algorithms Partitioning algorithms Transfer learning Evidence theory Feature extraction Fuzzy systems Approximation algorithms deep autoencoder evidential clustering unsupervised transfer learning

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IMU-CNN: implementing remote sensing image restoration framework based on Mask-Upgraded Cascade R-CNN and deep autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第27期83卷 69049-69081页

作者： Dewangan, Somesh Kumar Choubey, Siddharth Patra, Jyotiprakash Choubey, Abha Shri Shankaracharya Tech Campus Dept Comp Sci Engn Bhilai 490001 Chhattisgarh India Shri Shankaracharya Inst Profess Management & Tech Dept Comp Sci Engn Raipur 492015 Chhattisgarh India

The effective restoration of degraded remote sensing images is one of the major concerns as it directly affects imaging system performance. In recent years, investigators have developed innumerable systems and methods for remotely sensed hazy or blurred image restoration to enhance the system's performance. However, the existing remotely sensed image restoration system and methods still have untold limitations such as texture degradation, high computational complexity, preparative parametric tunning, more time consumption in restoration procedure, lesser accuracy, and many more. Hence, effective and fast restoration of the degraded remote sensing images with more accuracy is still a challenging problem, which requires enormous attention toward a novel image restoration approach. In this work, the researchers implement a remote sensing image restoration framework namely IMU-CNN, which is based on mask-upgraded cascade R-CNN and deep autoencoder. Our proposed IMU-CNN model performs hazy or blurred remotely sensed image restoration in a faster manner with improved accuracy, as well as saves time in the image restoration process, significantly. The outcome of the proposed image restoration framework is found improved and optimal and measured accuracy, precision, F1 score, and Recall is 99.59%, 97.24%, 97.69%, and 96.49%, respectively. Recently, researchers have conducted much investigation on remotely sensed image restoration systems by using various hybrid approaches. However, still, there is a need for further investigation in the future for building sophisticated and less computationally complex systems for effective remotely sensed image restoration in less time.

关键词： Cascade R-CNN deep autoencoder Image restoration IMU-CNN Remote sensing

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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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Network anomaly detection using deep autoencoder and parallel Artificial Bee Colony algorithm-trained neural network

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2333-e2333页

作者： Hacilar, Hilal Dedeturk, Bilge Kagan Bakir-Gungor, Burcu Gungor, Vehbi Cagri Abdullah Gul Univ Dept Comp Engn Kayseri Turkiye Erciyes Univ Dept Software Engn Kayseri Turkiye Turkcell Istanbul Turkiye

Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back- propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process;global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a deep autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSWNB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against

关键词： Artificial neural network Artificial bee colony Metaheuristics deep autoencoder Network intrusion detection systems (NIDS) Anomaly detection UNSW-NB15 Swarm intelligence NF-UNSW-NB15-v2

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NDAMA: A Novel deep autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection

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IEEE ACCESS 2023年 11卷 140740-140751页

作者： Dashdondov, Khongorzul Kim, Mi-Hye Jo, Kyuri Gachon Univ Coll IT Convergence Dept Comp Engn Seongnam 13120 South Korea Chungbuk Natl Univ Sch Elect & Comp Engn Dept Comp Engn Cheongju 28644 Chungcheongbuk South Korea

Natural gas is widely used for domestic and industrial purposes, and whether it is being leaked into the air cannot be directly known. The current problem is that gas leakage is not only economically harmful but also detrimental to health. Therefore, much research has been done on gas damage and leakage risks, but research on predicting gas leakages is just beginning. In this study, we propose a method based on deep learning to predict gas leakage from environmental data. Our proposed method has successfully improved the performance of machine learning classification algorithms by efficiently preparing training data using a deep autoencoder model. The proposed method was evaluated on an open dataset containing natural gas and environmental information and compared with extreme gradient boost (XGBoost), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), and naive Bayes (NB) algorithms. The proposed method is evaluated using accuracy, F1-score, mean square error (MSE), mean intersection over union (mIoU), and area under the ROC curve (AUC). The presented method in this study outperformed all compared methods. Moreover, the deep autoencoder and ordinal encoder-based XGBoost (DA-MA-XGBoost) showed the best performance by giving 99.51% accuracy, an F1-score of 99.53%, an MSE of 0.003, mIoU of 99.40 and an AUC of 99.62%.

关键词： Leak detection Machine learning algorithms Data models Predictive models Pipelines Labeling Surveys Encoding Multivariate regression deep autoencoder multivariate analysis methane gas risk detection XGBoost

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LDAEXC: LncRNA-Disease Associations Prediction with deep autoencoder and XGBoost Classifier

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2023年第3期15卷 439-451页

作者： Lu, Cuihong Xie, Minzhu Hunan Normal Univ Coll Informat Sci & Engn Changsha Peoples R China

Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA-Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA-disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 +/- 0.0043, 0.9449 +/- 0.022, 0.9375 +/- 0.0331 and 0.9556 +/- 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA-disease associations. [GRAPHICS] .

关键词： deep autoencoder XGBoost classifier LncRNA-disease associations prediction

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