Zobrazeno 1 - 10
of 575
pro vyhledávání: '"Sankaran Mahadevan"'
Publikováno v:
Frontiers in Materials, Vol 9 (2022)
Externí odkaz:
https://doaj.org/article/18f9505120534b889fd80002461f3c3c
Publikováno v:
Nuclear Engineering and Technology, Vol 49, Iss 1, Pp 123-133 (2017)
In human reliability analysis, dependence assessment is an important issue in risky large complex systems, such as operation of a nuclear power plant. Many existing methods depend on an expert's judgment, which contributes to the subjectivity and res
Externí odkaz:
https://doaj.org/article/d6cd701380eb4f90b3adadd3986c6ab3
Autor:
Guowei Cai, Sankaran Mahadevan
Publikováno v:
International Journal of Prognostics and Health Management, Vol 7, Iss 4 (2016)
This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous dat
Externí odkaz:
https://doaj.org/article/50bab5dd96024204a0580895dbf9b32e
Autor:
Saideep Nannapaneni, Abhishek Dubey, Sherif Abdelwahed, Sankaran Mahadevan, Sandeep Neema, Ted Bapty
Publikováno v:
International Journal of Prognostics and Health Management, Vol 7, Iss 1 (2016)
This paper develops a framework for the extraction of a reliability block diagram in component-based systems for reliability prediction with respect to specific missions. A mission is defined as a composition of several high-level functions occurring
Externí odkaz:
https://doaj.org/article/3bfdab526ddb45c09b21cca7cc24f8b3
Publikováno v:
International Journal of Prognostics and Health Management, Vol 6, Iss 4 (2015)
Uncertainty plays an important role in diagnostics, prognostics, and health management of engineering systems. The presence of uncertainty leads to an imprecise understanding of the behavior of such systems; as a result, this may adversely affect the
Externí odkaz:
https://doaj.org/article/a6179fc86fda40dbb501495157364792
Autor:
Gregory Bartram, Sankaran Mahadevan
Publikováno v:
International Journal of Prognostics and Health Management, Vol 6, Iss 4 (2015)
This paper proposes a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN). Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of for
Externí odkaz:
https://doaj.org/article/b4fc53051e374122a0ccf2f58018917f
Publikováno v:
International Journal of Prognostics and Health Management, Vol 2, Iss 1, Pp 1-15 (2011)
This paper presents a methodology to quantify the uncertainty in fatigue crack growth prognosis, applied to structures with complicated geometry and subjected to variable amplitude multi-axial loading. Finite element analysis is used to address the c
Externí odkaz:
https://doaj.org/article/1531083ab13c4f9f9a60097bb68a631f
Publikováno v:
PLoS ONE, Vol 10, Iss 12, p e0145028 (2015)
Identifying influential spreaders in networks, which contributes to optimizing the use of available resources and efficient spreading of information, is of great theoretical significance and practical value. A random-walk-based algorithm LeaderRank h
Externí odkaz:
https://doaj.org/article/229e7682b8bc419d9dbcf705a97b48d3
Publikováno v:
The Scientific World Journal, Vol 2014 (2014)
Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is origina
Externí odkaz:
https://doaj.org/article/58dfc048699f46bea9c0388a492efe9b
Publikováno v:
AIAA Journal. 61:1334-1348
Surrogate models are employed in engineering analysis to replace detailed physics-based models to achieve computational efficiency in problems that require multiple evaluations of the model. The accuracy of the surrogate model depends on the quality
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4cd6126f560b6d96a54aa53f3fb8133b
https://doi.org/10.2514/1.j061901
https://doi.org/10.2514/1.j061901