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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.
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>
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.
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.
박정수(Jungsu Park),장주희(Juhee Chang),문수연(Suyeon Moon),신세현(Sehyun Shin) 대한기계학회 2003 대한기계학회 춘추학술대회 Vol.2003 No.4
The present study investigated the microcirculation of blood in the left common artery (LCCA). In order<br/> to develop a mathematical model for microcirculation in LCCA, the present study adopted preexisted set of<br/> measured morphological data on anatomy, mechanical properties of the coronary vessels, viscosity of blood,<br/> the basic laws of physics, and the appropriate boundary conditions. In this study, the statistical distribution of<br/> blood pressure, blood flow, and blood volume in the LCCA were determined based on the anatomical<br/> branching pattern of the coronary arterial tree and the statistical data of blood vessel dimensions. Our<br/> calculations were in good agreement with the previous studies. The present results showed that the mean<br/> longitudinal pressure drop profile was function of the vessel order numbers. It was found that the normalized<br/> pressure drop was a logarithmic function of the compliance.
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.