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A report of eight unrecorded radiation resistant bacterial species in Korea isolated in 2018
Jang, Jun Hwee,Sathiyaraj, Gayathri,Sathiyaraj, Srinivasan,Lee, Jin Woo,Kim, Ju-Young,Maeng, Soohyun,Lee, Ki-Eun,Lee, Eun young,Kang, Myung Suk,Kim, Myung Kyum The National Institute of Biological Resources 2018 Journal of species research Vol.7 No.3
Eight bacterial strains assigned to the phylum Firmicutes were isolated from the soil samples in Korea. Phylogenetic analysis based on 16S rRNA gene sequences showed that strains 18JY14-16, 18JY14-35, 18JY42-5, 18JY12-20, 18JY35-8, 18JY76-9, 18JY39-1 and 18JY54-12 were most closely related to Paenibacillus lupini (MH497638; 99.4%), Paenibacillus illinoisensis (MH497643; 99.8%), Paenibacillus tundrae (MH497658; 99.7%), Paenibacillus selenitireducens (MH497639; 99.4%), Paenibacillus eucommiae (MH 497640; 99.9%), Paenibacillus vini (MH497654; 99.4%), Paenibacillus gorillae (MH497647; 99.5%), and Paenibacillus macquariensis (MH497649; 99.9%) respectively. These Paenibacillus species were Gram-stain-positive, rod-shaped and radiation resistant bacteria. This is the first report of these nine bacterial species in Korea.
클라우드 컴퓨팅 기반의 자동차 부하정보 모니터링 시스템 개발
조휘 ( Hwee Cho ),김기태 ( Ki Tae Kim ),장윤희 ( Yun Hee Jang ),김승환 ( Seung Hwan Kim ),김준수 ( Jun Su Kim ),박건영 ( Keoun Young Park ),장중순 ( Joong Soon Jang ),김종만 ( Jong Man Kim ) 한국품질경영학회 2015 품질경영학회지 Vol.43 No.4
Purpose: For improving result of estimated remaining useful life in Prognostics and Health Management (PHM),a system which is able to consider a lot of environment and load data is required.Method: A load profile monitoring system was presented based on cloud computing for gathering and processing raw data which is included environment and load data.Result: Users can access results of load profile information on the Internet. The developed system provides information which consists of distribution of load data, basic statistics, etc.Conclusion: We developed the load profile monitoring system for considering much environment and load data. This system has advantages such as improving accessibility through smart device, reducing cost, and covering various conditions.
The Load Profile Monitoring System Based on Cloud Computing in HYBRID Electric Vehicle
( Kitae Kim ),( Young Gi An ),( Hwee Cho ),( Dawei Wang ),( Keon Young Park ),( Jun Soo Kim ),( Jong Soon Jang ),( Chong Man Kim ) 한국품질경영학회 2014 한국품질경영학회 학술대회 Vol.2014 No.2
In Prognostics and Health Monitoring (PHM) applications, the monitoring of life-cycle load data on system plays essential role. However, the load cycle monitoring system usually suffers from the lack of accuracy, since an estimated load profile limited by the number of vehicle cannot represent loads during the system life cycle. The limited load profile causes an inaccurate residual life estimation of battery to an individual system`s life cycle. In order to improve the accuracy of the load profile modeling, it is required to collect and analyze the numerous operation, load, environment profiles in individual vehicles. To resolve these problems, this paper proposes a load profile monitoring system based on cloud computing in battery of hybrid electric vehicle (HEV) and electric vehicle (EV). The proposed system comprises two ideas: 1) to monitor a lot of vehicles and their operation parameters, the system uses a distributed data processing architecture for a lot of various load profile parameters and 2) the system proposes a method of displaying and summarizing the load profile for PHM in automotive applications. An implemented system provides large scale data processing from numerous vehicles and a load profile model for electric motors in hybrid electric vehicle.