Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Joonho Seon"'
Publikováno v:
ICT Express, Vol 9, Iss 6, Pp 1226-1232 (2023)
Imbalanced datasets are common in industrial internet of things (IIoT) systems due to challenges in acquiring faulty labels. Augmentation and graph-based methods have been proposed to improve classification accuracy of deep learning-based systems. Ho
Externí odkaz:
https://doaj.org/article/c06858d0fd7a4ecf9ecd7d3b29cca189
Autor:
Soohyun Kim, Youngghyu Sun, Seongwoo Lee, Joonho Seon, Byungsun Hwang, Jeongho Kim, Jinwook Kim, Kyounghun Kim, Jinyoung Kim
Publikováno v:
Energies, Vol 17, Iss 12, p 3057 (2024)
The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on ene
Externí odkaz:
https://doaj.org/article/76c61ddd68934b1da47d1b6cf750ce9c
Publikováno v:
Energies, Vol 17, Iss 3, p 624 (2024)
Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising sol
Externí odkaz:
https://doaj.org/article/7e6332e2e07c48958869aad52823bb34
Autor:
Youngghyu Sun, Jiyoung Lee, Soohyun Kim, Joonho Seon, Seongwoo Lee, Chanuk Kyeong, Jinyoung Kim
Publikováno v:
Energies, Vol 16, Iss 3, p 1109 (2023)
Energy theft causes a lot of economic losses every year. In the practical environment of energy theft detection, it is required to solve imbalanced data problem where normal user data are significantly larger than energy theft data. In this paper, a
Externí odkaz:
https://doaj.org/article/f69c8c3d253842ec8c92cbadbaff2c8d
Publikováno v:
Energies, Vol 14, Iss 22, p 7630 (2021)
In this paper, a time-lapse image method is proposed to improve the classification accuracy for multistate appliances with complex patterns based on nonintrusive load monitoring (NILM). A log-likelihood ratio detector with a maxima algorithm was appl
Externí odkaz:
https://doaj.org/article/2b23a1c9cddb4bab96416c94b72d1034
Publikováno v:
Energies, Vol 14, Iss 17, p 5515 (2021)
Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their correspondin
Externí odkaz:
https://doaj.org/article/d07330705569487fba3a0b1ad2064f2e
Autor:
Young Ghyu Sun, Soo Hyun Kim, Seongwoo Lee, Joonho Seon, SangWoon Lee, Cheong Ghil Kim, Jin Young Kim
Publikováno v:
Journal of Web Engineering.
Human activity recognition (HAR) is a key technology in many applications, such as smart signage, smart healthcare, smart home, etc. In HAR, deep learning-based methods have been proposed to recognize activity data effectively from video streams. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbb026c9ddcde3b27f389de3439412cd
https://doi.org/10.13052/jwe1540-9589.21512
https://doi.org/10.13052/jwe1540-9589.21512
Publikováno v:
Energies; Volume 14; Issue 17; Pages: 5515
Energies, Vol 14, Iss 5515, p 5515 (2021)
Energies, Vol 14, Iss 5515, p 5515 (2021)
Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their correspondin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd7c38e8215e088c0ea2d9d9ec813690
Publikováno v:
Energies, Vol 14, Iss 7630, p 7630 (2021)
Energies; Volume 14; Issue 22; Pages: 7630
Energies; Volume 14; Issue 22; Pages: 7630
In this paper, a time-lapse image method is proposed to improve the classification accuracy for multistate appliances with complex patterns based on nonintrusive load monitoring (NILM). A log-likelihood ratio detector with a maxima algorithm was appl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7e54a00e16cd9469b8287380afce640
https://doi.org/10.3390/en14227630
https://doi.org/10.3390/en14227630