Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Shenghan Guo"'
Publikováno v:
Data in Brief, Vol 51, Iss , Pp 109722- (2023)
In-process thermal melt pool images and post-fabrication porosity labels are acquired for Ti-6Al-4V thin-walled structure fabricated with OPTOMEC Laser Engineered Net Shaping (LENS™) 750 system. The data is collected for nondestructive thermal char
Externí odkaz:
https://doaj.org/article/fbaff39c08c740f2b714c3792398048d
Autor:
Xi Huang, Bo Liu, Shenghan Guo, Weihong Guo, Ke Liao, Guoku Hu, Wen Shi, Mitchell Kuss, Michael J. Duryee, Daniel R. Anderson, Yongfeng Lu, Bin Duan
Publikováno v:
Bioengineering & Translational Medicine, Vol 8, Iss 2, Pp n/a-n/a (2023)
Abstract Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an
Externí odkaz:
https://doaj.org/article/5681e5f9533c47aa85434c25877a1afb
Publikováno v:
Sensors, Vol 23, Iss 11, p 5313 (2023)
In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical conside
Externí odkaz:
https://doaj.org/article/c93ceff1125549409330044b45fcd752
Publikováno v:
Journal of Manufacturing Science & Engineering; Sep2024, Vol. 146 Issue 9, p1-15, 15p
Publikováno v:
IEEE Transactions on Automation Science and Engineering. 20:482-494
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b5959159df488cdb43cdbd69067ca69
https://doi.org/10.1109/tase.2022.3158204
https://doi.org/10.1109/tase.2022.3158204
Publikováno v:
IEEE Transactions on Automation Science and Engineering. 19:3276-3287
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5cb9e1342b3f30610864dbd1e50cfdf
https://doi.org/10.1109/tase.2021.3116040
https://doi.org/10.1109/tase.2021.3116040
Publikováno v:
Sensors; Volume 23; Issue 11; Pages: 5313
In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical conside
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::e184e61a146e3e7d4022db8f0d0ecc2c
Autor:
Robert X. Gao, Mohit Agarwal, Weihong Guo Grace, Yuebin Guo, Clayton Cooper, Qi Tian, Shenghan Guo
Publikováno v:
Journal of Manufacturing Systems. 62:145-163
Machine learning (ML) has shown to be an effective alternative to physical models for quality prediction and process optimization of metal additive manufacturing (AM). However, the inherent “black box” nature of ML techniques such as those repres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::add5b661dce9052bb806783f48c7dcbd
https://doi.org/10.1016/j.jmsy.2021.11.003
https://doi.org/10.1016/j.jmsy.2021.11.003
Autor:
Shenghan Guo, Weihong Guo
Publikováno v:
IEEE Transactions on Automation Science and Engineering. 19:230-242
Multivariate time series (MTS) arise due to multisensor data collection in manufacturing. These data are complex in the sense that attributes have a varying scale, volitivity, continuity, and so on, and interattribute dependence also appears, which c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12942892a837f26f70efd1854f560ca0
https://doi.org/10.1109/tase.2020.3026065
https://doi.org/10.1109/tase.2020.3026065
Autor:
Yuxiang Zhu, Tina Kwok, Joel C. Haug, Shenghan Guo, Xiangfan Chen, Weiheng Xu, Dharneedar Ravichandran, Yourka D. Tchoukalova, Jeffrey L. Cornella, Johnny Yi, Orit Shefi, Brent L. Vernon, David G. Lott, Jessica N. Lancaster, Kenan Song
Publikováno v:
Advanced Materials Technologies. 8
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03e62200caf6e9574d26cafb0d9e6c65
https://doi.org/10.1002/admt.202201421
https://doi.org/10.1002/admt.202201421