Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Nathan Mundhenk"'
Autor:
Chandrakumar Bogguri, Vivek Kurien George, Beheshta Amiri, Alexander Ladd, Nicholas R. Hum, Aimy Sebastian, Heather A. Enright, Carlos A. Valdez, T. Nathan Mundhenk, Jose Cadena, Doris Lam
Publikováno v:
Frontiers in Cellular Neuroscience, Vol 18 (2024)
Organophosphorus nerve agents (OPNA) are hazardous environmental exposures to the civilian population and have been historically weaponized as chemical warfare agents (CWA). OPNA exposure can lead to several neurological, sensory, and motor symptoms
Externí odkaz:
https://doaj.org/article/2c2b58c2460a469e8a5d95815e53ca1f
Autor:
Xiaoting Zhong, Brian Gallagher, Keenan Eves, Emily Robertson, T. Nathan Mundhenk, T. Yong-Jin Han
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-11 (2021)
Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collect
Externí odkaz:
https://doaj.org/article/15813301f7d54bfcb75f7018545b366d
Autor:
Brian Gallagher, Matthew Rever, Donald Loveland, T. Nathan Mundhenk, Brock Beauchamp, Emily Robertson, Golam G. Jaman, Anna M. Hiszpanski, T. Yong-Jin Han
Publikováno v:
Materials & Design, Vol 190, Iss , Pp - (2020)
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g., compressive strength) based on SEM images. We show that it's possible to train ML models to predict materials performance based o
Externí odkaz:
https://doaj.org/article/b0c0c231d88d41bd920defda87945393
Publikováno v:
Applications of Machine Learning 2021.
We recently developed a deep learning method that can determine the critical peak stress of a material by looking at scanning electron microscope (SEM) images of the material’s crystals. However, it has been somewhat unclear what kind of image feat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a44a89710af71c5f0ce1ef42a0ae5cf
https://doi.org/10.1117/12.2594295
https://doi.org/10.1117/12.2594295
Publikováno v:
MIPR
This paper proposes a fundamental answer to a frequently asked question in multimedia evaluation and data set creation: Do artifacts from perceptual compression contribute to error in the machine learning process and if so, how much? Our approach to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11b480ad0ee647b59d7a92e0462fce21
https://doi.org/10.1109/mipr49039.2020.00052
https://doi.org/10.1109/mipr49039.2020.00052
Autor:
T. Nathan Mundhenk, Brian Gallagher, Brock Beauchamp, T. Yong-Jin Han, Anna M. Hiszpanski, Emily Robertson, Matthew Rever, Donald Loveland, Golam G. Jaman
Publikováno v:
Materials & Design, Vol 190, Iss, Pp-(2020)
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g., compressive strength) based on SEM images. We show that it's possible to train ML models to predict materials performance based o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6945238baead4c71dd0da7c5823adc6
http://arxiv.org/abs/1906.02130
http://arxiv.org/abs/1906.02130
Autor:
N. E. Palmer, William Leach, T. Nathan Mundhenk, Robert Hatarik, James Henrikson, Matthew Rever, Judy Liebman
Publikováno v:
High Power Lasers for Fusion Research V.
Hohlraums convert the laser energy at the National Ignition Facility (NIF) into X-ray energy to compress and implode a fusion capsule, creating fusion. The Static X-ray Imager (SXI) diagnostic collects time-integrated images of hohlraum wall X-ray il
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::002421eed1baed730b90630dbd8adb12
https://doi.org/10.1117/12.2512605
https://doi.org/10.1117/12.2512605
Publikováno v:
QCAV
Two machine-learning methods were evaluated to help automate the quality control process for mitigating damage sites on laser optics. The mitigation is a cone-like structure etched into locations on large optics that have been chipped by the high flu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27158bfdc5f9061c5cfb7b16d91901c4
https://doi.org/10.1117/12.2264000
https://doi.org/10.1117/12.2264000
Publikováno v:
WACV
Since the introduction of deep convolutional neural networks (CNNs), object detection in imagery has witnessed substantial breakthroughs in state-of-the-art performance. The defense community utilizes overhead image sensors that acquire large field-o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7aff8adfd54876b178864c7a5e75350
https://doi.org/10.1109/wacv.2017.107
https://doi.org/10.1109/wacv.2017.107
Publikováno v:
CVPR
We develop a set of methods to improve on the results of self-supervised learning using context. We start with a baseline of patch based arrangement context learning and go from there. Our methods address some overt problems such as chromatic aberrat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fab986db0db47d555672c57e9472320