Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Clement Vachet"'
Publikováno v:
Microscopy and Microanalysis. 27:1465-1475
Determining the composition of a mixed material is an open problem that has attracted the interest of researchers in many fields. In our recent work, we proposed a novel approach to determine the composition of a mixed material using convolutional ne
Publikováno v:
Microscopy and Microanalysis. 27:2522-2525
Autor:
Clement Vachet, Luther W. McDonald, Tolga Tasdizen, Ian J. Schwerdt, Rachel Nicholls Lee, Alexa B. Hanson
Publikováno v:
Analytical Chemistry. 91:10081-10087
The morphological effect of impurities on α-U3O8 has been investigated. This study provides the first evidence that the presence of impurities can alter nuclear material morphology, and these changes can be quantified to aid in revealing processing
Autor:
Clement Vachet, Brock J. Mower, Luther W. McDonald, Nhat-Cuong Ly, Tolga Tasdizen, Ian J. Schwerdt, Sean Heffernan
Publikováno v:
Radiochimica Acta. 108:29-36
In the present study, surface morphological differences of mixtures of triuranium octoxide (U3O8), synthesized from uranyl peroxide (UO4) and ammonium diuranate (ADU), were investigated. The purity of each sample was verified using powder X-ray diffr
Publikováno v:
Chemometrics and Intelligent Laboratory Systems. 225:104556
Publikováno v:
2020 Intermountain Engineering, Technology and Computing (IETC).
A challenge in quality control for synthetic fiber-reinforced concrete is determining the actual spatial distribution of fibers. This paper presents the first computer algorithm to identify synthetic macrofibers within hardened concrete that has been
Autor:
Vivek Srikumar, Robert Paine, Jessica Chan, Tao Li, Clement Vachet, Joyce D. Schroeder, Tolga Tasdizen, Ricardo Bigolin Lanfredi
Publikováno v:
International Journal of Chronic Obstructive Pulmonary Disease
Joyce D Schroeder,1 Ricardo Bigolin Lanfredi,2 Tao Li,3 Jessica Chan,1 Clement Vachet,4 Robert Paine III,5 Vivek Srikumar,3 Tolga Tasdizen2 1Department of Radiology and Imaging Sciences, School of Medicine, University of Utah, Salt Lake City, UT, USA
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597122
MICCAI (2)
MICCAI (2)
The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels. This information is critical for building trust in models and finding their biases.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9349ddc6f2b2969b63938b76d6ca1870
https://doi.org/10.1007/978-3-030-59713-9_71
https://doi.org/10.1007/978-3-030-59713-9_71
Autor:
Irma D Fleming, Zachary T Huston, Matthew A Firpo, Clement Vachet, Emily M Graham, Michael P Peterson, Giavonni M. Lewis
Publikováno v:
Journal of Burn Care & Research. 41:S35-S36
Introduction Although trained specialists easily identify most full and superficial partial thickness burn injuries, deep partial-thickness injuries present a true challenge to the clinician. Various imaging modalities to assess perfusion and determi
Autor:
Alexa B, Hanson, Rachel Nicholls, Lee, Clement, Vachet, Ian J, Schwerdt, Tolga, Tasdizen, Luther W, McDonald
Publikováno v:
Analytical chemistry. 91(15)
The morphological effect of impurities on α-U