Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Darine Mansour"'
Publikováno v:
Day 2 Wed, March 09, 2022.
The industry has focused mainly on extracting key performance indicators (KPI) from its operational processes that aggregate data in different forms. The computation of average values has been one of the most important ways to measure process perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7bc181dbccebc646081b52b4a26e4d2d
https://doi.org/10.2118/208676-ms
https://doi.org/10.2118/208676-ms
Publikováno v:
Day 1 Tue, March 03, 2020.
One of the practical challenges in the oil and gas industry is the lack of quality data for applying machine learning techniques. A way to tackle this problem is to build a hybrid system that combines physics models with machine learning workflows. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::71ebbac6817abaadad823acb8bed312f
https://doi.org/10.2118/199629-ms
https://doi.org/10.2118/199629-ms
Publikováno v:
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2013
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2013, 2013, Tokyo, Japan. pp.4137-4144, 2013, 〈10.1109/IROS.2013.6696949〉
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2013, 2013, Tokyo, Japan. pp.4137-4144, ⟨10.1109/IROS.2013.6696949⟩
IROS
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2013, 2013, Tokyo, Japan. pp.4137-4144, 2013, 〈10.1109/IROS.2013.6696949〉
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2013, 2013, Tokyo, Japan. pp.4137-4144, ⟨10.1109/IROS.2013.6696949⟩
IROS
Conference of 2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 ; Conference Date: 3 November 2013 Through 8 November 2013; Conference Code:102443; International audience; This paper presents a meth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9b608e58c2020907e3ece4ab5190c08
https://hal-cea.archives-ouvertes.fr/cea-01813734
https://hal-cea.archives-ouvertes.fr/cea-01813734
Publikováno v:
Humanoids
This paper presents a new approach for humanoid push recovery in a generalized noncoplanar multicontact context. Our approach is based on a simplified model and consists in stabilizing the perturbed system while maintaining fixed contacts with the en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9513a0144980583b2615f3f5048b88b0
https://doi.org/10.1109/humanoids.2011.6100838
https://doi.org/10.1109/humanoids.2011.6100838
Publikováno v:
IROS
This paper presents a new computational approach for humanoid push recovery in a generalized noncoplanar multicontact context. Our approach is based on a simplified model and consists of two main steps. The first one predicts whether the perturbed sy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::93ae2ab08a91c549879817f78a4af041
https://doi.org/10.1109/iros.2011.6094874
https://doi.org/10.1109/iros.2011.6094874
Publikováno v:
Scopus-Elsevier
The following paper describes a system that facilitates the creation of a machine-understandable representation of well construction operating procedures, and a runtime component which automatically identifies, from sensor data, the degree of complia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1de0ccaeec574905685e4ea60f136401
http://www.scopus.com/inward/record.url?eid=2-s2.0-85063131625&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-85063131625&partnerID=MN8TOARS
Autor:
Chris Carpenter
Publikováno v:
Journal of Petroleum Technology. 73:56-57
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19714, “Deep Learning for Monitoring Well-Testing Burner Efficiency,” by Darine Mansour, SPE, Hakim Arabi, and Yingwei Yu, SPE, Schlumberger, et al.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5788227f473df5706788b922b943746e
https://doi.org/10.2118/0221-0056-jpt
https://doi.org/10.2118/0221-0056-jpt
Autor:
Srikanta Mishra
The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-ener