Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Kibong Choi"'
Publikováno v:
Biosensors, Vol 14, Iss 1, p 27 (2024)
This study investigates a real-time handheld bioaerosol monitoring system for the detection of biological particles using UV-LED and light-induced fluorescence technology. Biological particles produce both scattering and fluorescence signals simultan
Externí odkaz:
https://doaj.org/article/3b621f02158248f099a8b649169c8f0b
Publikováno v:
Sensors, Vol 22, Iss 9, p 3374 (2022)
Biological agents used in biological warfare or bioterrorism are also present in bioaerosols. Prompt identification of a biological weapon and its characteristics is necessary. Herein, we optimized an environmentally adaptive detection algorithm that
Externí odkaz:
https://doaj.org/article/e44563c8a51e451a954f6aa36a0a3575
Publikováno v:
Energies, Vol 12, Iss 11, p 2201 (2019)
The purpose of this paper is to investigate the effects of using gas to liquid (GTL)-biodiesel blends as an alternative fuel on the physical properties as well as the combustion and emission reduction characteristics in a diesel engine. In order to a
Externí odkaz:
https://doaj.org/article/23fa2e935de843c9a85599d742ec375c
Autor:
Seunghoon Lee, Gyehyun Kim, Sangki Oh, Sunglyul Kim, Hankyoo Ryoo, Dong-hwan Choi, Kibong Choi
Publikováno v:
Transaction of the Korean Society of Automotive Engineers. 29:811-821
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aca353436e3127e2eb500a2937c1a98e
https://doi.org/10.7467/ksae.2021.29.9.811
https://doi.org/10.7467/ksae.2021.29.9.811
Publikováno v:
IEEE Sensors Journal. 20:13471-13479
Real-time monitoring of bio-aerosols in the atmosphere has gained importance in both military and civilian security. As the first stage of defense architecture, a fluorescent particle counter is a compact, low-power, fast response sensor capable of g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::551ad905cd4e960d5cc15099362c684a
https://doi.org/10.1109/jsen.2020.3006880
https://doi.org/10.1109/jsen.2020.3006880
Publikováno v:
Instrumentation Science & Technology. 48:417-430
A handheld biological particle monitoring system that analyzes airborne biological particles in real time was designed, fabricated and its performance was investigated. An ultraviolet-light emittin...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ed37483acb2deea13cd66a5da50e318
https://doi.org/10.1080/10739149.2020.1735413
https://doi.org/10.1080/10739149.2020.1735413
Publikováno v:
Aerosol and Air Quality Research. 23:220218
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b02deabb078bf1fd5b88f9906d7e993e
https://doi.org/10.4209/aaqr.220218
https://doi.org/10.4209/aaqr.220218
Publikováno v:
CIKM
To speed up the training of massive deep neural network (DNN) models, distributed training has been widely studied. In general, a centralized training, a type of distributed training, suffers from the communication bottleneck between a parameter serv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50eb28f94170c889274900c780bda9dc
https://doi.org/10.1145/3459637.3482412
https://doi.org/10.1145/3459637.3482412
Publikováno v:
IPDPS
As the popularity of deep learning in industry rapidly grows, efficient training of deep neural networks (DNNs) becomes important. To train a DNN with a large amount of data, distributed training with data parallelism has been widely adopted. However
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0800da0bfc8baa371f01065173d55c9c
https://doi.org/10.1109/ipdps49936.2021.00108
https://doi.org/10.1109/ipdps49936.2021.00108
A real-time light-emitting-diode-induced fluorescence biological aerosol detection method is developed that enables real-time monitoring of airborne particles derived from organisms so the presence of these contaminants can be identified. To characte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b7c1bcaf1996996b2a9f9a0fc17f15e
