Suchergebnisse - (beta OR bela)-convolutional variational autoencoder~

History matching of a three dimensional channelized reservoir using a beta-convolutional variational autoencoder and ensemble smoother with multiple data assimilation

Autoren: Youngbin Ahn Jonggeun Choe Baehyun Min

Quelle: Engineering Applications of Artificial Intelligence. 159:111620

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

Autoren: Serag Mohamed Akila Elbrus Imanov Khaled Almezhghwi

Quelle: Diagnostics, Vol 13, Iss 13, p 2199 (2023)

Schlagwörter: chest pneumonia, classification, unsupervised learning, convolution, autoencoder, variational, Medicine (General), R5-920

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2075-4418/13/13/2199; https://doaj.org/toc/2075-4418

Zugangs-URL: https://doaj.org/article/3f5772b5ed7f4a0e867c5884e5bbc516

앙상블 기반 방법과 beta-convolutional variational autoencoder를 사용한 3차원 채널저류층 히스토리매칭 ; 3D Channel Reservoir History Matching Using Ensemble-Based Method and Beta-Convolutional Variational Autoencoder

Autoren: 안영빈 최종근 YoungBin Ahn et al.

Weitere Verfasser: 안영빈 최종근 YoungBin Ahn et al.

Schlagwörter: 히스토리매칭, 채널저류층, 3D 저류층, 앙상블 기반 방법, 기계 학습, beta-convolutional variational autoencoder, 622.33

Dateibeschreibung: xiii, 158

Relation: 000000178440; https://hdl.handle.net/10371/196356; https://dcollection.snu.ac.kr/common/orgView/000000178440; 000000000050▲000000000058▲000000178440▲

Verfügbarkeit: https://hdl.handle.net/10371/196356
https://dcollection.snu.ac.kr/common/orgView/000000178440

Markov-CVAELabeller: A Deep Learning Approach for the Labelling of Fault Data.

Autoren: Velasco-Gallego, Christian Cubo-Mateo, Nieves

Quelle: Informatics; Jun2025, Vol. 12 Issue 2, p35, 17p

Schlagwörter: CONVOLUTIONAL neural networks, TIME series analysis, ARTIFICIAL intelligence, DEEP learning, AUTOENCODERS

Reconstruction-based visual anomaly detection in wound rotor synchronous machine production using convolutional autoencoders and structural similarity.

Autoren: Kohler, Markus Mitsios, Dionysios Endisch, Christian

Quelle: Journal of Manufacturing Systems. Feb2025, Vol. 78, p410-432. 23p.

Schlagwörter: ELECTRIC drives, COMPUTER vision, ELECTRIC faults, MACHINE learning, AUTOENCODERS

Intrusion Detection for Blockchain‐Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model.

Autoren: Om Kumar, C. U. Marappan, Suguna Murugeshan, Bhavadharini et al.

Quelle: International Journal of Network Management; Nov/Dec2024, Vol. 34 Issue 6, p1-17, 17p

Schlagwörter: SMART cities, SMART devices, DEEP learning, INTERNET of things, DATA quality, INTRUSION detection systems (Computer security)

Graph-Convolutional-Beta-VAE for synthetic abdominal aortic aneurysm generation.

Autoren: Fabbri, Francesco Scarpolini, Martino Andrea Iollo, Angelo et al.

Quelle: Medical & Biological Engineering & Computing. Dec2025, p1-19.

Schlagwörter: *DATA protection, AORTIC aneurysms, GRAPH neural networks, DATA augmentation, LATENT variables, MEDICAL research, ANATOMICAL variation, METADATA

Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment.

Autoren: Ning, Chunxiao Xie, Yazhou

Quelle: Computer-Aided Civil & Infrastructure Engineering; Jan2024, Vol. 39 Issue 2, p165-185, 21p

Schlagwörter: GROUND motion, LATENT variables, CONCRETE construction, K-means clustering, EARTHQUAKE magnitude

Smart manufacturing under limited and heterogeneous data: a sim-to-real transfer learning with convolutional variational autoencoder in thermoforming.

Autoren: Ramezankhani, Milad Harandi, Mehrtash Seethaler, Rudolf et al.

Quelle: International Journal of Computer Integrated Manufacturing; 2024, Vol. 37 Issue 1/2, p18-36, 19p

Schlagwörter: THERMOGRAPHY, STATISTICAL correlation, PRODUCT quality, HEAT transfer, MANUFACTURING processes, DEEP learning

Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows

Autoren: Eivazi, Hamidreza Le Clainche, Soledad Hoyas, Sergio et al.

Quelle: Expert systems with applications. 202:117038

Schlagwörter: Non-linear mode decomposition, Turbulent flows, Variational autoencoders, Convolutional neural networks, Machine learning

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-314853
https://doi.org/10.1016/j.eswa.2022.117038

Task level disentanglement learning in robotics using βVAE

Autoren: M S, Midhun Kurian, James

Quelle: International journal of electrical and computer engineering systems
Volume 13
Issue 7

Schlagwörter: Machine Learning, Variational Autoencoder, Neural Networks, Robotics, beta-VAE

Dateibeschreibung: application/pdf

Zugangs-URL: https://hrcak.srce.hr/file/412502
https://doi.org/10.32985/ijeces.13.7.8
https://hrcak.srce.hr/284952

Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother.

Autoren: Kang, Xueyuan Kokkinaki, Amalia Kitanidis, Peter K. et al.

Quelle: Water Resources Research; Feb2021, Vol. 57 Issue 2, p1-26, 26p

Schlagwörter: DENSE nonaqueous phase liquids, DEEP learning, GEOLOGICAL statistics

Anomaly Detection for Solder Joints Using β -VAE.

Autoren: Ulger, Furkan Yuksel, Seniha Esen Yilmaz, Atila

Quelle: IEEE Transactions on Components, Packaging & Manufacturing Technology. Dec2021, Vol. 11 Issue 12, p2214-2221. 8p.

Schlagwörter: *INTEGRATED circuits, SOLDER joints, CONVOLUTIONAL neural networks, AUTOMATIC optical inspection, PRINTED circuits

Adaptive Neural Network System for Detecting Unauthorised Intrusions Based on Real-Time Traffic Analysis.

Autoren: Vladov, Serhii Vysotska, Victoria Lytvyn, Vasyl et al.

Quelle: Computation; Sep2025, Vol. 13 Issue 9, p221, 42p

Schlagwörter: INTRUSION detection systems (Computer security), ANOMALY detection (Computer security), KALMAN filtering, LATENT variables, INTERNET security, TRAFFIC monitoring, MACHINE learning, INTELLIGENT networks

Loop closure detection of visual SLAM based on variational autoencoder.

Autoren: Shibin Song Fengjie Yu Xiaojie Jiang et al.

Quelle: Frontiers in Neurorobotics; 2024, p1-13, 13p

Schlagwörter: DEEP learning, IMAGE representation, HANDICRAFT

Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning

Autoren: Yu Zhu Jing Gu Zhuoran Zhao et al.

Quelle: J Chem Inf Model

Schlagwörter: Evolution, Molecular, Deep Learning, Protein Conformation, Stenotrophomonas maltophilia, Molecular Dynamics Simulation, beta-Lactamases

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38687957
https://discovery-pp.ucl.ac.uk/id/eprint/10191804/

MV-MFF: Multi-View Multi-Feature Fusion Model for Pneumonia Classification.

Autoren: Alsulami, Najla Althobaiti, Hassan Alafif, Tarik

Quelle: Diagnostics (2075-4418); Jul2024, Vol. 14 Issue 14, p1566, 15p

Schlagwörter: IMAGE recognition (Computer vision), X-ray imaging, LUNG diseases, X-rays, PNEUMONIA

Gating interactions steer loop conformational changes in the active site of the L1 metallo-β-lactamase.

Autoren: Zhuoran Zhao Xiayu Shen Shuang Chen et al.

Quelle: eLife. 2/24/2023, p1-18. 18p.

Schlagwörter: *MOLECULAR dynamics, *BETA lactam antibiotics, *STENOTROPHOMONAS maltophilia, *ANTIBACTERIAL agents, *DEEP learning, *MARKOV processes

FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion.

Autoren: Wang, Zhengjue Chen, Bo Lu, Ruiying et al.

Quelle: IEEE Transactions on Image Processing; 2020, Vol. 29, p7565-7577, 13p

Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases from Burkholderia spp

Autoren: Gu, J Agarwal, PK Bonomo, RA et al.

Quelle: Journal of Chemical Information and Modeling , 65 (10) pp. 5086-5098. (2025)

Schlagwörter: beta-Lactamases, Burkholderia, Molecular Dynamics Simulation, Evolution, Molecular, Catalytic Domain, Markov Chains, Mutation

Dateibeschreibung: application/pdf

Relation: https://discovery.ucl.ac.uk/id/eprint/10209808/

Verfügbarkeit: https://discovery.ucl.ac.uk/id/eprint/10209808/1/gu-et-al-2025-evolutionary.pdf
https://discovery.ucl.ac.uk/id/eprint/10209808/