Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Chandrachur Bhattacharya"'
Publikováno v:
Metals, Vol 12, Iss 11, p 1849 (2022)
Fatigue damage detection and its classification in metallic materials are persistently challenging the structural health monitoring community. The mechanics of fatigue damage is difficult to analyze and is further complicated because of the presence
Externí odkaz:
https://doaj.org/article/53773bf264ef4bedb94c184849fc0f67
Publikováno v:
Machines, Vol 9, Iss 10, p 211 (2021)
The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors
Externí odkaz:
https://doaj.org/article/65c9c1489cc14fba95016943add74741
Publikováno v:
Sci, Vol 3, Iss 1, p 14 (2021)
Natural circulation loop (NCL) systems are buoyancy-driven heat exchangers that are used in various industrial applications. The concept of passive heat exchange in NCL systems is attractive, because there is no need for an externally driven equipmen
Externí odkaz:
https://doaj.org/article/57ec32270f2545da854335357863b459
Publikováno v:
IFAC-PapersOnLine. 55:99-106
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b57c0f5c791f84508150a0e169b2e7f
https://doi.org/10.1016/j.ifacol.2022.11.168
https://doi.org/10.1016/j.ifacol.2022.11.168
Autor:
Prasanta Kumar Pujari, Bikramjit Gayen, Dipak Kumar Jha, Chandrachur Bhattacharya, Syamsundar Mondal
Publikováno v:
International Journal of Orthopaedics Sciences. 7:484-487
Introduction: Knee joint is a major weight bearing synovial joint. In frontal radiograph we can see asymmetrical joint spaces between femoral and tibial components due to asymmetric wear and tear. Articular surfaces of tibial and femoral condyles are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::675376ae56b5af39dfc1bb1fbea7ce84
https://doi.org/10.22271/ortho.2021.v7.i4g.2922
https://doi.org/10.22271/ortho.2021.v7.i4g.2922
Publikováno v:
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 380
Despite tangible advances in machine learning (ML) over the last few decades, many of the ML techniques still suffer from fundamental issues like overfitting and lack of explainability. These issues mandate requirements for mathematical rigor to ensu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db8d75b9b64a86214a7d6a4011054ab1
https://doi.org/10.1098/rsta.2021.0196
https://doi.org/10.1098/rsta.2021.0196
