Search Results - "sparse convolutional autoencoder (SCAE)"

Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors.

Authors: Yang, Cheng Yuan, Yannan Wang, Fu et al.

Source: Quality & Reliability Engineering International. Mar2024, Vol. 40 Issue 2, p819-837. 19p.

Subject Terms: *FAULT location (Engineering), *NUCLEAR reactors, *NUCLEAR reactor control, *FAULT diagnosis, *ELECTRONIC equipment, *LINE drivers (Integrated circuits)

Advances and novel applications in systems reliability and safety engineering (selected papers of the International Conference of SRSE 2022).

Authors: Peng, Weiwen Xu, Ancha Hu, Jiawen

Source: Quality & Reliability Engineering International; Oct2024, Vol. 40 Issue 6, p2955-2958, 4p

Subject Terms: INDUSTRIAL safety, SYSTEM failures, ARTIFICIAL intelligence, LIGHT water reactors, RELIABILITY in engineering

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review.

Authors: Jiang, Wen Wang, Yanping Li, Yang et al.

Source: Remote Sensing; Aug2023, Vol. 15 Issue 15, p3742, 41p

Subject Terms: AUTOMATIC target recognition, DEEP learning, MACHINE learning, SIGNAL processing, SYSTEMS engineering, RADAR signal processing, RADAR targets

Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia.

Authors: Akila, Serag Mohamed Imanov, Elbrus Almezhghwi, Khaled

Source: Diagnostics (2075-4418); Jul2023, Vol. 13 Issue 13, p2199, 14p

Subject Terms: MACHINE learning, PNEUMONIA, ANOMALY detection (Computer security), LUNG infections, DIAGNOSIS, KLEBSIELLA infections

Self-organizing maps on "what-where" codes towards fully unsupervised classification.

Authors: Sa-Couto, Luis Wichert, Andreas

Source: Biological Cybernetics; Jun2023, Vol. 117 Issue 3, p211-220, 10p

Subject Terms: SELF-organizing maps, DEEP learning, ONLINE education

Data-driven methods for low-dimensional representation and state identification for the spatiotemporal structure of cavitation flow fields.

Authors: Wang, Zihao Zhang, Guiyong Sun, Tiezhi et al.

Source: Physics of Fluids; Mar2023, Vol. 35 Issue 3, p1-17, 17p

Subject Terms: COMPUTATIONAL fluid dynamics, CAVITATION, UNSTEADY flow, FLUID mechanics, PRINCIPAL components analysis

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder.

Authors: Obayashi, Wataru Aono, Hikaru Tatsukawa, Tomoaki et al.

Source: AIP Advances; Oct2021, Vol. 11 Issue 10, p1-6, 6p

Subject Terms: FLUID dynamics, FEATURE extraction, PROPER orthogonal decomposition, NAVIER-Stokes equations, CONVOLUTIONAL neural networks, UNSTEADY flow, COMPRESSIBLE flow

Artificial Intelligence in Medicine : 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II

Authors: Joseph Finkelstein Robert Moskovitch Enea Parimbelli

Resource Type: eBook.

Subjects: Medical informatics--Congresses, Artificial intelligence--Medical applications--Congresses

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