Search Results - convolutional astm autoencoder

Compact representation and identification of important regions of metal microstructures using complex-step convolutional autoencoders

Authors: Dharanidharan Arumugam Ravi Kiran

Source: Materials & Design, Vol 223, Iss , Pp 111236- (2022)

Subject Terms: Interpretable AI, ASTM A992, Saliency maps, Pixel relevance, Convolutional autoencoder, Materials of engineering and construction. Mechanics of materials, TA401-492

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S0264127522008589; https://doaj.org/toc/0264-1275

Access URL: https://doaj.org/article/f86f63c05f2043d7b5571943ad9899a3

A-MSTM: ATTEMPTED CATCH ESCAPE-OPTIMIZED MIXED ATTENTION-ENABLED BILSTM MODEL FOR BRAIN TUMOR CLASSIFICATION.

Authors: Kumari, Divya Anoop, B. K.

Source: Biomedical Engineering: Applications, Basis & Communications; Aug2025, Vol. 37 Issue 4, p1-22, 22p

Subject Terms: LONG short-term memory, ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, GRAPH neural networks, PATTERN recognition systems, DEEP learning

Rapid and Accurate Prediction of Soil Texture Using an Image-Based Deep Learning Autoencoder Convolutional Neural Network Random Forest (DLAC-CNN-RF) Algorithm.

Authors: Zhao, Zhuan Feng, Wenkang Xiao, Jinrui et al.

Source: Agronomy; Dec2022, Vol. 12 Issue 12, p3063, 16p

Subject Terms: DEEP learning, CROP quality, SOIL texture, CONVOLUTIONAL neural networks, COMPUTER vision, RANDOM forest algorithms, GRAPHICAL user interfaces, SAND waves

Unsupervised Classification and Remaining Useful Life Prediction for Turbofan Engines Using Autoencoders and Gaussian Mixture Models: A Comprehensive Framework for Predictive Maintenance.

Authors: Lodygowski, Tomasz Szrama, Slawomir

Source: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 14, p7884, 24p

Subject Terms: REMAINING useful life, TURBOFAN engines, INDUSTRIAL applications, CONDITION-based maintenance, CLUSTER analysis (Statistics), GAUSSIAN mixture models, AUTOENCODERS

Damage detection and classification of carbon fiber-reinforced polymer composite materials based on acoustic emission and convolutional recurrent neural network.

Authors: Shao, Yun-Fei Guo, Fuping Jiang, Peng et al.

Source: Structural Health Monitoring; Nov2025, Vol. 24 Issue 6, p3344-3362, 19p

Subject Terms: CARBON fiber-reinforced plastics, ACOUSTIC emission, COMPOSITE materials, SIGNAL processing, ARTIFICIAL neural networks, MACHINE learning, STRUCTURAL health monitoring, CLASSIFICATION

A CNN-Based Method for Quantitative Assessment of Steel Microstructures in Welded Zones.

Authors: Almeida, Cássio Danelon de Filgueiras, Thales Tozatto Lagares Jr., Moisés Luiz et al.

Source: Fibers; May2025, Vol. 13 Issue 5, p66, 21p

Subject Terms: CONVOLUTIONAL neural networks, MILD steel, DATA augmentation, STEEL welding, METALLOGRAPHY

Optimizing 3D printing through enhancements in inverse autoencoder models and guided decoders for predicting mechanical properties.

Authors: Nguyen, Phuong Dong Hoang, Tien-Dat Nguyen, Thanh Q.

Source: Progress in Additive Manufacturing; Oct2025, Vol. 10 Issue 10, p8729-8750, 22p

Acoustic sensing and autoencoder approach for abnormal gas detection in a spent nuclear fuel canister mock-up.

Authors: Zhuang, Bozhou Gencturk, Bora Oberai, Assad et al.

Source: Structural Health Monitoring; Jan2026, Vol. 25 Issue 1, p413-431, 19p

Multi-Modal NDE Data Analysis for Bridge Assessment Using the BEAST Dataset and Temporal Graph Convolution Networks.

Authors: Momtaz, Mozhgan Azari, Hoda

Source: Journal of Nondestructive Evaluation; Dec2025, Vol. 44 Issue 4, p1-27, 27p

Subject Terms: BRIDGE testing, MULTISENSOR data fusion, NONDESTRUCTIVE testing, REPAIR & maintenance services, PREDICTION models, STRUCTURAL health monitoring, CONVOLUTIONAL neural networks, SPATIOTEMPORAL processes

Residual life prediction of multi-layer composite coatings based on vector quantised and attention-variational AutoEncoder network.

Authors: Li, Min Pan, Fushuai Li, Xue et al.

Source: Nondestructive Testing & Evaluation; Jan2025, Vol. 40 Issue 1, p225-245, 21p

Subject Terms: COMPOSITE coating, FEATURE extraction, RELIABILITY in engineering, AUTOENCODERS, PREDICTION models

A Brief Outline of Indoor Air Quality: Monitoring, Modeling, and Impacts.

Authors: Tahir Bahadur, Faizan Rasool Shah, Shagoofta Rao Nidamanuri, Rama

Source: Journal of Environmental Engineering; May2025, Vol. 151 Issue 5, p1-33, 33p

Subject Terms: LONG short-term memory, RECURRENT neural networks, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, AIR quality monitoring, INDOOR air pollution, INDOOR air quality

A review of deep learning and artificial intelligence in dyeing, printing and finishing.

Authors: Ingle, Nilesh Jasper, Warren J

Source: Textile Research Journal; Mar2025, Vol. 95 Issue 5/6, p625-657, 33p

Subject Terms: GENERATIVE artificial intelligence, CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, ARTIFICIAL intelligence, SYSTEMS engineering, DEEP learning

Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP.

Authors: Barile, Claudia Casavola, Caterina Katamba Mpoyi, Dany et al.

Source: Applied Sciences (2076-3417); Feb2025, Vol. 15 Issue 3, p1209, 27p

Subject Terms: CARBON fiber-reinforced plastics, SINGULAR value decomposition, FRACTOGRAPHY, ACOUSTIC emission, AUTOMOTIVE engineering

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete.

Authors: Inderyas, Omair Alver, Ninel Tayfur, Sena et al.

Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Feb2025, Vol. 50 Issue 3, p1885-1903, 19p

Subject Terms: CONVOLUTIONAL neural networks, ACOUSTICAL engineering, CIVIL engineering, REINFORCED concrete, NONDESTRUCTIVE testing, ACOUSTIC emission

Corrosion type identification in flanged joints using recurrent neural networks on electrochemical noise measurements.

Authors: Hakimian, Soroosh Bouzid, Abdel-Hakim Hof, Lucas A.

Source: NPJ Materials Degradation; 7/15/2025, Vol. 9 Issue 1, p1-17, 17p

Subject Terms: RECURRENT neural networks, LONG short-term memory, ELECTROCHEMICAL experiments, BOLTED joints, ELECTRONIC data processing, MACHINE learning, STEEL corrosion

Large‐scale structural health monitoring using composite recurrent neural networks and grid environments.

Authors: Eltouny, Kareem A. Liang, Xiao

Source: Computer-Aided Civil & Infrastructure Engineering; Feb2023, Vol. 38 Issue 3, p271-287, 17p

Subject Terms: STRUCTURAL health monitoring, RECURRENT neural networks, SMART structures, CONVOLUTIONAL neural networks, TRACTION (Engineering), CIVIL engineering

Methodology and Experimental Verification for Predicting the Remaining Useful Life of Milling Cutters Based on Hybrid CNN-LSTM-Attention-PSA.

Authors: Zhu, Mengge Zhang, Ji Bu, Lingfan et al.

Source: Machines; Nov2024, Vol. 12 Issue 11, p752, 26p

Subject Terms: REMAINING useful life, MILLING cutters, CONVOLUTIONAL neural networks, AUTOMATION, SEARCH algorithms

Addressing smart city security: machine and deep learning methodology combining feature selection and two-tier cooperative framework tuned by metaheuristics.

Authors: Zivkovic, Miodrag Jovanovic, Luka Jocovic, Vladimir et al.

Source: Cluster Computing; 2025, Vol. 28 Issue 11, p1-55, 55p

Subject Terms: SMART cities, SECURITY management, ARTIFICIAL intelligence, FEATURE selection, METAHEURISTIC algorithms, INTERNET of things, MACHINE learning, DEEP learning

A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment.

Authors: Elahi, Mahboob Afolaranmi, Samuel Olaiya Martinez Lastra, Jose Luis et al.

Source: Discover Artificial Intelligence; 12/7/2023, Vol. 3 Issue 1, p1-78, 78p

Subject Terms: GENERATIVE adversarial networks, ARTIFICIAL intelligence, CONVOLUTIONAL neural networks, AGILE software development, DIGITAL transformation, INDUSTRIAL equipment, RADIOSTEREOMETRY

Spreading anomaly semantic segmentation and 3D reconstruction of binder jet additive manufacturing powder bed images.

Authors: Gourley, Alexander Kaufman, Jonathan Aman, Bashu et al.

Source: International Journal of Advanced Manufacturing Technology; Oct2024, Vol. 134 Issue 7/8, p3139-3151, 13p

Subject Terms: CONVOLUTIONAL neural networks, CERAMIC powders, MANUFACTURING processes, IMAGE analysis, RAPID tooling