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권헌각 ( Hun Gak Kwon ),이재운 ( Jae Woon Lee ),이윤정 ( Youn Jeong Yi ),윤영삼 ( Young Sam Yoon ),이춘식 ( Chun Sik Lee ),이재관 ( Jae Kwan Lee ) 한국물환경학회 2011 한국물환경학회지 Vol.27 No.5
The first flush phenomenon and the Mass First Flush (MFFn) were analyzed for various rainy events in trunk road. Applicability for estimate MFFn using SWMM was evaluated by comparision with observed MFFn. First flush phenomenon was investigated by normalized cumulated (NCL) curve of every pollutant based on ten times of rainfall events monitoring data from 2008 to 2009. As a result, magnitude of first flush phenomenon varied with the pollutants and rainfall events. First flush phenomenon was detected highly in the trunk road. MFFn was estimated by varying n-value from 10 to 90% on the rainfall events. The n-value increases, MFFn is closed to ``1``. As time passed, the rainfall runoff was getting similar to ratio of pollutants accumulation. The result of a measure of the strength of the linear relationship between observed data and expected data under model was good (R2=0.956). As the final outcome, we have good reliability, estimation and application of MFFn using model seem statistically possible.
권헌각 ( Hun Gak Kwon ),이재운 ( Jae Woon Lee ),이윤정 ( Youn Jeong Yi ),신석호 ( Suk Ho Shin ),이춘식 ( Chun Sik Lee ),이재관 ( Jae Kwan Lee ) 한국환경과학회 2012 한국환경과학회지 Vol.21 No.3
The MFFn(Mass First Flush) was analyzed for various rainy events(monitoring data from 2008 to 2009) in Transportation area(Highway, National road, Trunk road). Estimated MFFn using SWMM was evaluated by comparison with observed MFFn. MFFn was estimated by varying n-value from 10% to 90% on the rainy events. The n-value increases, MFFn is closed to ``1``. As time passed, the rainfall runoff was getting similar to ratio of pollutants accumulation. The result of a measure of the strength of the linear relationship between observed data and expected data under model was good (R2=0.89). Pollutants runoff loads by volume showed Highway 26.6%, National road 44.8%, Trunk road 35.0% at the MFF20(20% by total runoff). A case of MFF30, pollutants runoff loads by volume showed Highway 40.2%, National road 54.3%, Trunk road 46.8%. According to the results, Initial precipitation basis were Highway MFF30, National road MFF20, Trunk road MFF30 when the Non-Point source control facilities set up.
권오장,이재관,장범석,엄흥식,Kwon, Oh-Jang,Lee, Jae-Kwan,Chang, Beom-Seok,Um, Heung-Sik 대한치주과학회 2008 Journal of Periodontal & Implant Science Vol.38 No.1
Purpose: The purpose of this study was to investigate the incidence of curet fracture and its contributing factors. Material and Methods: Fifty-eight periodontal curets which were broken during periodontal treatment in Kangnung National University Dental Hospital for 1 year were used as study materials. The blade thickness of new curets and broken ones was measured using a digital micrometer. Types of treatment procedures, clinical experience of operators, point of breakage, and method of removal of broken fragments were recorded for each broken curet. Results: The incidence of curet fracture in root planing (16.4 curets per 1,000 procedures) was higher than those in flap surgery (7.5) or supragingival scaling (2.7). No curet was broken during supportive periodontal treatment. The incidence of fracture did not seem to be related with clinical experience of operators. The most frequent breakage point of the curets were upper 1/3 of blades. Fifty-six of 58 broken fragments were removed by non-surgical methods. Two broken tips which could not removed non-surgically were left in the pockets, and proved to be removed spontaneously 1 week later. Conclusion: Root planing showed higher incidence of curet fracture than any other type of periodontal treatment. Most of the fractured fragments were removed by non-surgical method. Further study is needed to develop methods of removal of the fragments which can not be removed non-surgically.
이민주(Minju Lee),박인권(In Kwon Park),장서일(Seo Il Chang),이재관(Jae Kwan Lee) 한국소음진동공학회 2018 한국소음진동공학회 논문집 Vol.28 No.5
This study estimates the economic value of the damage caused by the noise by analyzing land prices in the area around a military live-fire complex (LFC). To do this, we create a noise map based on the noise level measured in the LFC and surrounding area, and combine it with the data of 10 years of assessed value and characteristics for land in the surrounding area. Using the panel data, the effect of the noise from the LFC on land prices of the surrounding area is statistically analyzed. The results show that the level of land price decreases by about 4250 KRW/m² ~ 5485 KRW/m² as the noise level increases by 10 dB(C), and that the growth of land price varies depending on the noise level of the area. The results will be able to be used to estimate the economic value of the noise damage from the LFC for the entire surrounding area as well as for individual parcels.
도로유지관리를 위한 음향 및 영상 데이터 기반의 도로포장종류 구분 기계학습모형 연구
김보경(Bo Kyeong Kim),이재관(Jae Kwon Lee),최호식(Ho Sik Choi),장서일(Seo Il Chang),이수일(Soo Il Lee) 한국소음진동공학회 2021 한국소음진동공학회 논문집 Vol.31 No.4
The investigation of tire-road noise according to the type of road pavement is time-consuming and expensive. In this study, an artificial neural network model was applied to address this problem. Models to classify road pavement types (for example, transverse-tined, longitudinal-tined, NGCS, DG, and SMA) were implemented and their performance were compared. Input data were constructed by combining the features extracted from tire-road noise and road surface images. The tire-road noise collected using the OBSI measurement method was analyzed for the sound pressure level, sound intensity, and sound quality indices. Road surface image data were analyzed using the image feature extraction algorithms of the Hough transformation and histogram of gradient(HOG). The top 10 important variables were selected by inputting each feature into a random forest model, and artificial neural network models were constructed by each feature. The classification accuracy of the model using only acoustic features was 90.8 % and that using only image features was 88.8 %. The accuracy of the model using both features was 97.3 %. The overall classification performance was improved by using the acoustic and image features.