Search Results - 6: Technology::621: Applied Physics::621.8: Machine Engineering

A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets

Authors: Sepe M. Graziano A. Badora M. et al.

Source: Journal of the Global Power and Propulsion Society. :1-15

Subject Terms: Turbomachinery, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Predictive maintenance, Anomaly detection, 02 engineering and technology, 0101 mathematics, Monitoring and diagnostics, 01 natural sciences, Digital twin

File Description: application/pdf

Access URL: https://journal.gpps.global/pdf-134845-65000?filename=A physics_informed.pdf
https://journal.gpps.global/A-physics-informed-machine-learning-framework-for-predictive-maintenance-applied
134845
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2.html
https://journal.gpps.global/pdf-134845-65000?filename=A physics_informed.pdf

Physics-Informed Machine Learning Applied to Complex Compositional Model in a Giant Field

Authors: Chris Carpenter

Source: Journal of Petroleum Technology. 76:68-70

Subject Terms: 02 engineering and technology, 0204 chemical engineering, 01 natural sciences, 0105 earth and related environmental sciences

Digital twin, physics-based model, and machine learning applied to damage detection in structures

Authors: Fernando A. Rochinha Thiago G. Ritto

Source: Mechanical Systems and Signal Processing. 155:107614

Subject Terms: Signal Processing (eess.SP), 0209 industrial biotechnology, FOS: Electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Electrical Engineering and Systems Science - Signal Processing, 0201 civil engineering

Access URL: http://arxiv.org/pdf/2005.14360
http://arxiv.org/abs/2005.14360
https://arxiv.org/abs/2005.14360
https://arxiv.org/pdf/2005.14360.pdf
https://www.sciencedirect.com/science/article/pii/S0888327021000091
https://ui.adsabs.harvard.edu/abs/2021MSSP..15507614R/abstract

Applied physics. The material is the machine

Authors: Kaushik, Bhattacharya Richard D, James

Source: Science (New York, N.Y.). 307(5706)

Subject Terms: 0209 industrial biotechnology, 02 engineering and technology, 0210 nano-technology

Access URL: https://pubmed.ncbi.nlm.nih.gov/15637259

On the prediction of critical heat flux using a physics-informed machine learning-aided framework

Authors: Fengdi Guo Koroush Shirvan Xingang Zhao et al.

Contributors: Fengdi Guo Koroush Shirvan Xingang Zhao et al.

Source: arXiv

Subject Terms: Physics - Data Analysis, Statistics and Probability, Fluid Dynamics (physics.flu-dyn), 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, FOS: Physical sciences, Physics - Applied Physics, Physics - Fluid Dynamics, Applied Physics (physics.app-ph), 02 engineering and technology, 7. Clean energy, Data Analysis, Statistics and Probability (physics.data-an)

File Description: application/pdf

Access URL: https://www.sciencedirect.com/science/article/am/pii/S1359431119332065
http://arxiv.org/abs/1906.11124
https://inspirehep.net/literature/1741470
https://www.sciencedirect.com/science/article/pii/S1359431119332065
https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902258920681039
https://arxiv.org/pdf/1906.11124
https://data.mendeley.com/datasets/5p5h37tyv7
https://ui.adsabs.harvard.edu/abs/2019arXiv190611124Z/abstract
https://hdl.handle.net/1721.1/124412

Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations

Authors: Duquesnoy, Marc Liu, Chaoyue Dominguez, Diana Zapata et al.

Contributors: Duquesnoy, Marc Liu, Chaoyue Dominguez, Diana Zapata et al.

Source: Energy Storage Materials
arXiv (preprint)

Subject Terms: [CHIM.MATE] Chemical Sciences/Material chemistry, ELECTRODES, FOS: Physical sciences, [CHIM.MATE]Chemical Sciences/Material chemistry, Physics - Applied Physics, Applied Physics (physics.app-ph), 02 engineering and technology, 01 natural sciences, 7. Clean energy, LI-ION BATTERIES, 0104 chemical sciences, DESIGN, MORPHOLOGY, 0210 nano-technology

File Description: application/pdf

Access URL: http://arxiv.org/abs/2205.01621
https://u-picardie.hal.science/hal-04001716v1
https://doi.org/10.1016/j.ensm.2022.12.040
https://u-picardie.hal.science/hal-04001716v1/document

Machine Learning Techniques Applied to Identify the Two-Phase Flow Pattern in Porous Media Based on Signal Analysis

Authors: Xiangyu Li Liangxing Li Wenjie Wang et al.

Source: Applied Sciences, Vol 12, Iss 17, p 8575 (2022)

Subject Terms: flow patterns identification, porous media, two-phase flow, support vector machine, BP network, feature extraction, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/12/17/8575; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/d358e0b7757841608529d10c05036052

FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNS

Authors: J. Xu K.-P. Heue M. Coldewey-Egbers et al.

Source: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-3, Pp 1995-1998 (2018)

Subject Terms: Technology, satellite remote sensing, Atmosphärenprozessoren, Engineering (General). Civil engineering (General), 7. Clean energy, 01 natural sciences, TA1501-1820, ozone, machine learning, 13. Climate action, Atmospheric composition measurements, Applied optics. Photonics, TA1-2040, UV spectroscopy, 0105 earth and related environmental sciences

File Description: application/pdf

Access URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1995/2018/isprs-archives-XLII-3-1995-2018.pdf
https://isprs-archives.copernicus.org/articles/XLII-3/1995/2018/
https://doaj.org/article/3f68a90d3c6849f18babd9c1223558c6
https://core.ac.uk/display/156965477
https://noa.gwlb.de/receive/cop_mods_00006561
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1995/2018/isprs-archives-XLII-3-1995-2018.pdf
http://ui.adsabs.harvard.edu/abs/2018ISPAr42.3.1995X/abstract
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1995/2018/isprs-archives-XLII-3-1995-2018.html
https://elib.dlr.de/119388/
https://elib.dlr.de/119388/

Random Forests Machine Learning Applied to PEER Structural Performance Experimental Columns Database

Authors: Konstantinos G. Megalooikonomou Grigorios N. Beligiannis

Source: Applied Sciences, Vol 13, Iss 23, p 12821 (2023)

Subject Terms: reinforced concrete, columns, PEER structural performance database, machine learning, random forests, failure mode, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/13/23/12821; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/c253c9ea4a6140c0b59ce3d8074f3210

Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures

Authors: Alain Gomez-Cabrera Ponciano Jorge Escamilla-Ambrosio

Source: Applied Sciences, Vol 12, Iss 21, p 10754 (2022)

Subject Terms: structural health monitoring, machine learning, physics-based model, data-based model, building structures, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/12/21/10754; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/25e0d55e320a455e8542bb1289127022

Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach

Authors: Rafael Travincas Maria Paula Mendes Isabel Torres et al.

Source: Applied Sciences, Vol 14, Iss 23, p 10780 (2024)

Subject Terms: random forest, support vector machine, industrial mortar, substrate, prediction, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/23/10780; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/4ad53e23e462479791d82904137fe172

Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process

Authors: Djonathan Luiz de Oliveira Quadras Ian Cavalcante Mirko Kück et al.

Source: Applied Sciences, Vol 13, Iss 19, p 10904 (2023)

Subject Terms: machine learning, discrete event simulation, agribusiness 4.0, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

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Relation: https://www.mdpi.com/2076-3417/13/19/10904; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/2db124b84b934ff4b0e4c94f15cd6c61

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Authors: Nuria Caballé-Cervigón José L. Castillo-Sequera Juan A. Gómez-Pulido et al.

Source: Applied Sciences, Vol 10, Iss 15, p 5135 (2020)

Subject Terms: human disease, machine learning, data mining, artificial intelligence, big data, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/10/15/5135; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/1ba885c2a05149ada83fcbe9344bdaa4

Modeling Health Data Using Machine Learning Techniques Applied to Financial Management Predictions

Authors: Rafael Leon Sanz Pilar Leon-Sanz

Source: Applied Sciences, Vol 12, Iss 23, p 12148 (2022)

Subject Terms: Big Data, Artificial Intelligence, Artificial Neural Networks, machine learning, health management, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/12/23/12148; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/662196011eb2438892945d30b102e309

An Android and Arduino Based Low-Cost Educational Robot with Applied Intelligent Control and Machine Learning

Authors: Francisco M. Lopez-Rodriguez Federico Cuesta

Source: Applied Sciences, Vol 11, Iss 1, p 48 (2020)

Subject Terms: educational robotics, fuzzy logic, machine learning, vocational training, android, arduino, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/11/1/48; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/5c61bd3ed39248edabe8012d1178b6c2

Physics‐Informed Machine Learning for Optical Modes in Composites

Authors: Abantika Ghosh Mohannad Elhamod Jie Bu et al.

Source: Advanced Photonics Research, Vol 3, Iss 11, Pp n/a-n/a (2022)

Subject Terms: machine learning, metamaterials, 0103 physical sciences, Applied optics. Photonics, QC350-467, 02 engineering and technology, Optics. Light, 0210 nano-technology, composites, metasurfaces, 01 natural sciences, TA1501-1820

Access URL: https://doaj.org/article/51dbcda9800247db8e1468ec3f52d336

Physics-Based Machine Learning Approach for Modeling the Temperature-Dependent Yield Strength of Superalloys

Authors: Steingrimsson, Baldur Fan, Xuesong Adam, Benjamin et al.

Subject Terms: Condensed Matter - Materials Science, 0103 physical sciences, Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, Physics - Applied Physics, 02 engineering and technology, Applied Physics (physics.app-ph), 0210 nano-technology, 01 natural sciences, 7. Clean energy

Access URL: http://arxiv.org/abs/2211.16576

Effectiveness of Machine-Learning and Deep-Learning Strategies for the Classification of Heat Treatments Applied to Low-Carbon Steels Based on Microstructural Analysis

Authors: Jorge Muñoz-Rodenas Francisco García-Sevilla Juana Coello-Sobrino et al.

Source: Applied Sciences, Vol 13, Iss 6, p 3479 (2023)

Subject Terms: machine learning, transfer learning, low-carbon steels, optical microstructure, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/13/6/3479; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/1d25a75a73b5404c8854367bd9b2dd4d

Comparison between Machine Learning and Physical Models Applied to the Evaluation of Co-Seismic Landslide Hazard

Authors: José Carlos Román-Herrera Martín Jesús Rodríguez-Peces Julio Garzón-Roca

Source: Applied Sciences, Vol 13, Iss 14, p 8285 (2023)

Subject Terms: machine learning, Newmark displacement, co-seismic landslide, logistic regression, random forest, artificial neural network, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/13/14/8285; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/a799bc75880f4f70a4ef30ad0ef68e54

Investigation of Classification and Anomalies Based on Machine Learning Methods Applied to Large Scale Building Information Modeling

Authors: Manyu Xiao Zhiqin Chao Rajan Filomeno Coelho et al.

Source: Applied Sciences, Vol 12, Iss 13, p 6382 (2022)

Subject Terms: Building Information Modeling, data classification, data detection, machine learning, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/12/13/6382; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/1c5788cc975d46c1800e8c5fc4c5f1a4