Quantitative analysis of glucose from hazardous plants in Korea for industrial feedstock

Jungsu PARK,Yongkeun CHOI,Seongmin LEE,Hyunjin JEON,Jangsoon LIM,Young-Hoo KIM,Heehun JUNG,Sang Hyun LEE,Yung-Hun YANG,Hyung Seop KIM,Hyung Joo KIM 한국생물공학회 2010 한국생물공학회 학술대회 Vol.2010 No.10

A novel method for cell counting of Microcystis colonies in water resources using a digital imaging flow cytometer and microscope

Jungsu Park,Yongje Kim,Minjae Kim,Woo Hyoung Lee 대한환경공학회 2019 Environmental Engineering Research Vol.24 No.3

Microcystis sp. is one of the most common harmful cyanobacteria that release toxic substances. Counting algal cells is often used for effective control of harmful algal blooms. However, Microcystis sp. is commonly observed as a colony, so counting individual cells is challenging, as it requires significant time and labor. It is urgent to develop an accurate, simple, and rapid method for counting algal cells for regulatory purposes, estimating the status of blooms, and practicing proper management of water resources. The flow cytometer and microscope (FlowCAM), which is a dynamic imaging particle analyzer, can provide a promising alternative for rapid and simple cell counting. However, there is no accurate method for counting individual cells within a Microcystis colony. Furthermore, cell counting based on two-dimensional images may yield inaccurate results and underestimate the number of algal cells in a colony. In this study, a three-dimensional cell counting approach using a novel model algorithm was developed for counting individual cells in a Microcystis colony using a FlowCAM. The developed model algorithm showed satisfactory performance for Microcystis sp. cell counting in water samples collected from two rivers, and can be used for algal management in fresh water systems.

Rate-dependent hardening model for polymer-bonded explosives with an HTPB polymer matrix considering a wide range of strain rates

Park, Chunghee,Huh, Hoon,Park, Jungsu SAGE Publications 2015 Journal of composite materials Vol.49 No.4

<P>This article is concerned with the effect of the strain rate on the strain hardening behavior of polymer-bonded explosives at a wide range of strain rates ranging from 0.0001 s<SUP>–1</SUP> to 3870 s<SUP>−1</SUP>. Inert polymer-bonded explosive simulants are prepared as specialized particulate composites to acquire analogous mechanical characteristics to polymer-bonded explosives for safety reasons. Uniaxial compressive tests were conducted from quasi-static states to intermediate strain rates ranging from 0.0001 s<SUP>−1</SUP> to 100 s<SUP>−1</SUP> with cylindrical specimens using a dynamic material testing machine (INSTRON 8801) and a high-speed material testing machine. An experimental method was developed for uniaxial compressive tests at intermediate strain rates ranging from 10 s<SUP>−1</SUP> to 100 s<SUP>−1</SUP>. Split Hopkinson pressure bar tests were performed at high strain rates ranging from 1250 s<SUP>−1</SUP> to 3870 s<SUP>−1</SUP>. Deformation behavior was investigated using captured images from a high-speed camera. The strain hardening behavior of polymer-bonded explosive simulants was formulated as a function of the strain rate with the proposed rate-dependent hardening model based on the DSGZ model. The model is capable of representing the complicated strain rate effects on the strain hardening behavior for rate-sensitive materials with a second-order exponentially-increasing function of the strain rate sensitivity. The rate-dependent hardening model of polymer-bonded explosives can be readily applied to prediction of deformation modes of polymer-bonded explosives in a warhead that undergoes severe dynamic loads.</P>

알칼리-열 전처리에 의한 슬러지 분해 및 혐기성 소화 향상: 경제성 평가 및 미생물 군집 분석

박정수 ( Jungsu Park ),( Roent Dune A. Cayetano ),김기범 ( Gi-beom Kim ),조유라 ( Yura Jo ),권예령 ( Yeelyung Kwon ),김상현 ( Sang-hyoun Kim ) 한국물환경학회 2022 한국물환경학회·대한상하수도학회 공동 춘계학술발표회 Vol.2022 No.-

동적막을 이용한 하수슬러지 혐기성 소화에 대한 알칼리-열 전처리의 영향

박정수 ( Jungsu Park ),김기범 ( Gi-beom Kim ),김상현 ( Sang-hyoun Kim ) 한국폐기물자원순환학회 2023 젊은 연구자 학술연구발표회 Vol.20 No.0

딥러닝 사물 인식 알고리즘(YOLOv3)을 이용한 미세조류 인식 연구

박정수 ( Jungsu Park ),백지원 ( Jiwon Baek ),유광태 ( Kwangtae You ),남승원 ( Seung Won Nam ),김종락 ( Jongrack Kim ) 한국물환경학회(구 한국수질보전학회) 2021 한국물환경학회지 Vol.37 No.4

Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

알칼리-열 전처리가 폐활성 슬러지의 회분식 혐기성 소화에 미치는 영향

박정수(Jungsu Park),김상현(Sang-Hyoun Kim),Roent Dune A. Cayetano,김기범(Gi-Beom Kim),조유라(Yura Jo),권예령(Yeelyung Kwon) 대한환경공학회 2021 대한환경공학회 학술발표논문집 Vol.2021 No.11

입력 자료 측정빈도에 따른 클로로필-a 농도 예측 자동 머신러닝 모형 성능 비교

박정수(Jungsu Park) 대한환경공학회 2023 대한환경공학회지 Vol.45 No.4

목적 : 자동 머신러닝은 모형의 선정부터 최적화까지 머신러닝 모형의 구축을 자동으로 수행해주는 최신 알고리즘으로, 본 연구에서는 자동 머신러닝(automated machine learning) 알고리즘 중 하나인 auto H2O를 이용하여 하천 클로로필-a(chl-a) 농도를 예측하는 모형을 구축하였다. 방법 : 본 연구에서는 1h, 2h, 8h, 24h 및 1 week 측정빈도로 구축된 입력 자료를 구축하여 입력 자료의 측정빈도가 auto H2O 알고리즘을 이용하여 구축된 자동 머신러닝 모형의 성능이 미치는 영향에 대한 분석을 수행하였다. 또한, 입력 자료의 농도가 모형 성능에 미치는 영향을 비교하기 위해 chl-a 실측값이 30 ㎎g/㎥를 초과하는 자료로 구축된 모형과 성능 차이를 함께 비교하였다. 모형 성능은 mean absolute error (MAE), nash-sutcliffe coefficient of efficiency (NSE) 및 root mean squared error-observation standard deviation ration (RSR)의 3가지 지수를 이용하여 평가하였다. 결과 및 토의 : 측정빈도 1h의 입력 자료를 이용한 모형의 MAE, NSE, RSR이 각각 0.8977, 0.9710, 0.1704로 분석되었다. 전체자료를 이용할 경우 1h, 2h, 8h, 24h, 1 week 측정빈도에서의 NSE가 각각 0.9710, 0.9552, 0.8856, 0.8396, 0.7509로 분석되어 입력 자료의 측정빈도가 높을수록 모형의 성능이 좋은 경향을 확인하였다. Chl-a 실측값이 30 ㎎g/㎥를 초과하는 경우 NSE가 각각 0.8971, 0.8164, 0.5704, 0.5141, 0.2052로 분석되어 전체자료를 이용하는 경우보다 상대적으로 측정빈도 차이에 따른 모형 성능 차이가 큰 것으로 분석되었다. 결론 : 자동 머신러닝 auto H2O 알고리즘을 이용하여 조류예측 모형을 구축하였으며 측정빈도가 높을수록 모형 성능이 좋으며 측정빈도에 따른 성능차이는 chl-a의 실측값이 30 ㎎g/㎥인 구간에서 더 큰 것으로 분석되었다. Objectives : Automated machine learning is a recent field of study that automates the process of machine learning model development including proper model selection and optimization. In this study, auto H2O, a novel automated machine learning algorithm, was used to develop a model to predict chlorophyll-a (chl-a). Methods : This study used datasets with different observation frequencies of 1h, 2h, 8h, 24h and 1 week for the development of a machine learning model using an auto H2O algorithm to analyze the effects of measurement frequency of input data on model performance. The effect of the concentration of the input datasets on the performance of the model was also compared by building a model using datasets with observed values of chl-a exceeding 30 ㎎/㎥. The model performance was evaluated using three indices mean absolute error (MAE), Nash-Sutcliffe coefficient of efficiency (NSE) and root mean squared error-observation standard deviation ratio (RSR). Results and Discussion : The MAE, NSE, and RSR of the model using the input data with a measurement frequency of 1h were analyzed as 0.8977, 0.9710, and 0.1704, respectively. The higher the measurement frequency of the input data, the better the performance of the model as the NSE of the model using full data was 0.9710, 0.9552, 0.8856, 0.8396, and 0.7509 for the input datasets with 1h, 2h, 8h, 24h and 1 week observation frequencies, respectively. The difference in model performance according to the difference in measurement frequency was larger for the model using data with the measured value of chl-a exceeding 30 ㎎/㎥, as the NSE was analyzed to be 0.8971, 0.8164, 0.5704, 0.5141, and 0.2052, respectively. Conclusion : The auto H2O model for predicting chl-a showed better model performance as the measurement frequency of the input data increased, and the difference in performance according to the measurement frequency was larger in the range of observed chl-a concentrations that exceeded 30 ㎎/㎥.

세종시 스마트시티 구상 및 수립 방안

박정수(Jungsu Park),정한민(Hanmin Jung) 한국정보통신학회 2021 한국정보통신학회 종합학술대회 논문집 Vol.25 No.2

산업혁명 이후 일자리를 찾기 위해 수많은 사람이 도시로 모여들어, 현재 세계 인구 50% 이상이 도시에 살고 있다. 이러한 도시 집중화는 앞으로도 급속히 전개되어 2035년에는 75% 인구가 도시에 살 것으로 전망된다. 대도시는 점점 높아지는 인구 밀도로 인하여 환경 오염, 심각한 교통 체증, 지나치게 빠른 에너지 고갈 및 자연 생태계 파괴 등 부작용이 발생하면서 지속가능성이 떨어지고 있다. 또한, 높은 범죄율과 안전사고, 불평등과 양극화로 인한 일과 삶의 불균형, 지나치게 경쟁적인 교육 등으로 대도시 시민들의 행복 지수 역시 떨어지고 있다. 이러한 문제를 해결하기 위하여 공급자, 관리자 중심이 아닌 사용자와 시민 중심으로 설계, 운영, 관리되는 IT 기술 기반 미래형 도시 모델인 스마트시티가 탄생하게 되었다. 우리나라도 국가 중점 사업으로 스마트시티의 효율적인 건설 및 운영을 통해 도시 경쟁력을 향상하고 지속 가능한 발전을 촉진하려는 시도를 적극적으로 하고 있다. 이를 뒷받침하기 위해 본 연구는 국토종합계획 및 스마트도시 종합계획, 스마트시티 전략계획 등을 기반으로 세종시의 스마트도시서비스 기반 시설 등의 기본 방향 및 추진 전략을 검토하고, 관련 계획의 연관 관계를 조사하여 세종시 스마트시티 관련 사업 검토와 스마트도시 조성을 위한 추진 체계 및 정책을 제언하고자 한다. This urban centralization is expected to develop rapidly, with 75% of the population living in the city by 2035. Large cities are becoming unsustainable due to side effects such as environmental pollution, severe traffic jams, excessive energy depletion, and destruction of the natural ecosystem. In addition, the happiness index of citizens of large cities is also falling because of high crime rates and safety accidents, the work-life imbalance caused by inequality and polarization, and overly competitive education. To solve this problem, Smart City, an IT-based future city model, was born. The Korean government is also actively attempting to improve urban competitiveness and promote sustainable development through efficient construction and operation of smart cities as a national focus project. To support the effort, we review the basic directions and strategies of Sejong City’s Smart City service infrastructure based on the comprehensive national land plan, Smart City plan, and Smart City strategy plan.

입력자료 군집화에 따른 앙상블 머신러닝 모형의 수질예측 특성 연구

박정수 ( Jungsu Park ) 한국물환경학회 2021 한국물환경학회지 Vol.37 No.5

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-observation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.