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노성유 ( Seongyu Noh ),신유나 ( Yuna Shin ),최희락 ( Heelak Choi ),이재윤 ( Jaeyoon Lee ),이재안 ( Jaean Lee ),류덕희 ( Doughee Rhew ) 한국환경영향평가학회 2015 환경영향평가 Vol.24 No.3
환경변화(체류 시간)에 따른 조류발생기작 및 이동특성 연구를 위해 현장규모 모의실험장치를 제작하여 낙동강 수계의 강정·고령보를 대상으로 체류 시간에 따른 조류발생 및 이동특성을 파악하고자 하였다. 현장규모 모의실험장치는 조류배양의 효율성 및 조류성장 관찰의 편리성 등을 고려하여 투명아크릴로 제작하였다(직경 1 m, 높이 4 m, 가변형 원통수조, 3 sets). 빛 차단장치, 수심별 유입장치, 재이용수저류조 등의 부대시설을 설치하였다. 본 연구에서 체류 시간 조건은 2일(보설치 전, 실험조 1), 8일(보설치 후 2013년 체류 시간, 실험조 2), 30일(2014년 체류 시간, 실험조 3)로 선정하였다. 실험결과, 실험조별 수온은 실험조 1에서는 큰 차이를 보이지 않았으며 실험조 3에서는 표층(0 m)과 저층(4 m) 간 약 3℃ 이상의 차이를 보였다. 용존산소(DO), pH 변화 분석 결과 모든 실험조에서 표층 0 m에서 저수심(2 m, 4 m) 보다 상대적으로 높은 값을 보였다. 영양염류(TN, PO4-P)는 모든 실험조에서 부영양 상태를 나타냈다. Chlorophyll-a 분석 결과 실험조 1은 평균 19.8 μg/L, 실험조 2는 평균 35.0 μg/L, 실험조 3은 평균36.6 μg/L로 실험조 1 보다 실험조 2, 3에서 약 2배 높은 농도를 나타냈다. 따라서 환경 요인 중 체류 시간은 식물플랑크톤 발생에 많은 영향을 미치는 것으로 판단된다. Pilot scale system was designed to identify the growth and movement of algae, depending on environmental changes(retention time, nutrient concentration, etc) in Gangjeong- Goryeong Weir of the Nakdong River. Considering the stability of algal culture and easy observation of algal growth, pilot scale system was made of transparent acrylic material(3 sets of flexible cylindrical water tanks with 1 m diameter and 4 m height). Auxiliary equipments include light intercepter, water inflow device for different water depth and storage of reclaimed water. The retention time was 2 days(before construction of weir; treatment 1), 8 days(after construction of weir, 2013; treatment 2) and 30 days(2014; treatment 3). According to the water temperature of treatment 1 were similar by depth, treatment 3 showed a difference between the surface(0 m) and bottom(4 m) more than 3 °C. DO, pH showed relatively high in the surface than the bottom. Nutrients showed eutrophic condition in all experiments. The Chlrophyll-a concentration of the treatment 1 showed a relatively lower value than the Chlrophylla concentration of the treatment 2 and 3. Therefore, the retention time was considered to influence the growth of phytoplankton.
베이지안 추정을 이용한 팔당호 유역의 계절별 클로로필a 예측 및 오염특성 연구
김미아 ( Mi Ah Kim ),신유나 ( Yuna Shin ),김경현 ( Kyung Hun Kim ),허태영 ( Tae Young Heo ),유문규 ( Moon Kyu Yoo ),이수웅 ( Su Woong Lee ) 한국물환경학회 2013 한국물환경학회지 Vol.29 No.6
In recent years, eutrophication in the Paldang Lake has become one of the major environmental problems in Korea as it may threaten drinking water safety and human health. Thus it is important to understand the phenomena and predict the time and magnitude of algal blooms for applying adequate algal reduction measures. This study performed seasonal water quality assessment and chlorophyll-a prediction using Bayseian simple/multiple linear regression analysis. Bayseian regression analysis could be a useful tool to overcome limitations of conventional regression analysis. Also it can consider uncertainty inprediction by using posterior distribution. Generally, chlorophyll-a of a P2(Paldang Dam 2) site showed high concentration in spring and it was similar to that of P4(Paldang Dam 4) site. For the development of Bayseian model, we performed seasonal correlation. As a result, chlorophyll-a of a P2 site had a high correlation with P5(Paldang Dam 5) site in spring (r =0.786, p<0.05) and with P4 in winter (r =0.843, p<0.05). Based on the DIC (Deviance Information Criterion) value, critical explanatory variables of the best fitting Bayesian linear regression model were selected as a PO4-P (P2), Chlorophyll-a (P5) in spring, NH3-N (P2), Chlorophyll-a (P4), NH3-N (P4) in summer, DTP (P2), outflow (P2), TP (P3), TP (P4) fall, COD (P2), Chl-a (P4) and COD (P4) in winter. The results of chlorophyll-a prediction showed relatively hagh R2 and low RMSE values in summer and winter.
트랜스포머 기반 2-Phase 학습법을 통한 EEG 데이터 분류 모델 성능 개선
임예빈(Yeabin Lim),신유나(Yuna Shin),양희재(Heejae Yang),진창균(Changgyun Jin) 대한전자공학회 2024 대한전자공학회 학술대회 Vol.2024 No.6
Electroencephalogram (EEG) brain signals provide a wealth of information, and Brain-Computer Interface (BCI) technology leveraging these signals offers hope for individuals with disabilities. Recent research has explored various fields with EEG signal like Motor Imagery, Emotion recognition etc. Among them, there are several studies on object imagery to synthesize object class from brain signals. However, previous work has demonstrated poor performance in EEG classification. This is because EEG signals have different characteristics from person to person, making them difficult to generalize, and it led to itself in differences between train and test performance. In our study, we address this limitation by employing a Transformer-based approach and a two-phase training strategy, leading to improved classifier performance. Consequently, our enhanced classifier show better performance and less gap between train and test performance. Also when we visualize the features for each class, we can see that they are organized into a better refined distribution by label. And we can expect that this can contribute to various applications including assistive technologies for individuals with disabilities.