Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Adedotun Akintayo"'
Autor:
Tryambak Gangopadhyay, Vikram Ramanan, Adedotun Akintayo, Paige K Boor, Soumalya Sarkar, Satyanarayanan R Chakravarthy, Soumik Sarkar
Publikováno v:
Energy and AI, Vol 4, Iss , Pp 100067- (2021)
While analytical solutions of critical (phase) transitions in dynamical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example is thermoacoustic instability in combustion, w
Externí odkaz:
https://doaj.org/article/1672f29ae157493f904d01b94e71e7d5
Publikováno v:
International Journal of Prognostics and Health Management, Vol 7, Iss 4 (2016)
The thermo-acoustic instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. The phenomenon is described as selfsustaining and
Externí odkaz:
https://doaj.org/article/a744c00a6d474aeab7a05cca8735a81e
Publikováno v:
International Journal of Prognostics and Health Management, Vol 7, Iss 4 (2016)
The thermo-acoustic instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. The phenomenon is described as selfsustaining and
Publikováno v:
Applied Energy. 211:1106-1122
Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain
Publikováno v:
Applied Energy. 206:1022-1039
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the
Autor:
Arti Singh, Gregory L. Tylka, Adedotun Akintayo, Asheesh K. Singh, Baskar Ganapathysubramanian, Soumik Sarkar
Publikováno v:
Scientific Reports
Scientific Reports, Vol 8, Iss 1, Pp 1-11 (2018)
Scientific Reports, Vol 8, Iss 1, Pp 1-11 (2018)
In order to identify and control the menace of destructive pests via microscopic image-based identification state-of-the art deep learning architecture is demonstrated on the parasitic worm, the soybean cyst nematode (SCN), Heterodera glycines. Soybe
Publikováno v:
ACC
Numerous studies on non-intrusive load monitoring (NILM) of electrical demand have been performed for the purpose of identifying load components only using univariate data, such as the identification of a certain type of end-use (e.g., lighting load)
Probabilistic graphical models (PGMs) have been shown to efficiently capture the dynamics of physical systems as well as model cyber systems such as communication networks. This chapter focuses on some recent developments in applying PGMs as data-dri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b413a5ccd0c1f45c5bc5165d3c62a83
https://doi.org/10.1016/b978-0-12-803801-7.00018-3
https://doi.org/10.1016/b978-0-12-803801-7.00018-3
Autor:
Soumik Sarkar, Adedotun Akintayo
This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale non-stationary behaviors are considered to be a mixture of quasi-stati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b97c6c386c51a31635df176f0b3a8e11