• Korean Chemical Engineering Research
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• 제59권1호
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• pp.77-84
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• 2021

Plug flow reactor (PFR) 내의 과열점(hot spot) 온도를 조절하는 것은 생성물의 수득률 및 순도, 안전성 측면에서 중요하다. 본 연구에서는 더 현실에 가깝게 모델링 하기 위하여 PFR 내부의 냉각액 온도를 상태변수로 설정하고 방사 방향의 열 및 물질전달을 고려하였다. 모델은 반응물의 농도 및 온도와 냉각액의 온도 총 3개의 상태변수로 이루어져 있으며, 등온 냉각액의 유량을 조작변수로 가진다. 본 연구에서는 방사 방향의 열 및 물질전달을 고려한 제어식이 그렇지 않은 제어식보다 과열점의 온도를 set point 부근으로 더 효과적으로 유지한다는 것을 보였다. 본 연구에서 제안한 제어식은 냉각액의 온도가 반응물 온도의 약 0.7배 부근일 때 St가 1.3 이상이고 Ac/A가 2.0 이하인 조건에서 강건성을 유지하였다. 이 조건에서 반응기로 유입되는 반응물의 온도가 5% 범위에서 바뀔 때 본 연구에서 제안된 제안식을 이용하면 과열점의 온도를 set point의 1% 이내로 유지할 수 있다.

• 한국정보기술학회논문지
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• 제17권3호
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• pp.31-41
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• 2019

본 논문에서는 CCSDS에서 권고하고 있는 대역폭 효율적인 변조 방식 중 채널당 2.0, 2.25, 2.5, 2.75 bits/symbol의 전송효율을 가지는 4D-8PSK TCM 시스템의 송신부와 수신부를 설계하고 시뮬레이터를 구현하여 AWGN 환경에서 모의시험을 통하여 BER 성능을 분석한다. 송신부는 CCSDS 표준을 준용하여 설계하고, 수신부는 차동 부호화 및 복호화를 일반화하여 차동 복호기를 설계하며, 트렐리스 복호 알고리즘은 보조격자의 정보와 비터비 알고리즘을 적용하여 설계하고, CCSDS 표준에서 주어진 8차원 성상도 맵퍼의 방정식을 가감법으로 풀어 성상도 디맵퍼를 설계한다. 특히, 컴퓨터 모의실험을 통해 비터비 복호기 설계 시 역추적 깊이에 따른 오류 성능을 제시하여 4D-8PSK TCM 시스템의 최적화된 송/수신부를 구현하고 성능을 분석한다.

• 한국추진공학회지
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• 제26권1호
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• pp.28-37
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• 2022

밀폐용기(Closed Bomb)시험을 통해 고압에서 작동하는 고체 추진제의 연소속도를 추정하는 방법을 연구하였다. CEA를 이용하여 연소가스의 조성을 계산였으며 밀폐용기 내부의 고온, 고압의 환경을 묘사하기 위해 Noble-Abel 상태방정식을 적용하였다. 분자의 부피를 고려한 분자 간의 충돌을 묘사하는 인자인 Covolume을 분자의 LJ potential을 이용하여 모델링하였다. 또한 추진제의 부피 변화율을 고려하기 위해 3차 형상함수(Cubic form function)를 적용하였다. 각 모델을 사용하여 고압용기에서 측정된 5개의 압력-시간 선도로부터 연소속도를 계산하고 이를 BRLCB 결과와 비교 검증하였다. 각 실험에서 약 6% 이내의 최대 오차를 갖는 연소속도를 추정함으로써 초고압 환경에서의 연소속도 추정 방법을 정립하였다.

• Animal Bioscience
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• 제36권3호
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• pp.492-497
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• 2023

Objective: The objectives were to demonstrate that the nitrogen and energy in pig urine supplemented with hydrochloric acid (HCl) are not volatilized and to determine the minimum amount of HCl required for nitrogen preservation from pig urine. Methods: In Exp. 1, urine samples of 3.0 L each with 5 different nitrogen concentrations were divided into 2 groups: 1.5 L of urine added with i) 100 mL of distilled water or ii) 100 mL of 6 N HCl. The urine in open plastic containers was placed on a laboratory table at room temperature for 10 d. The weight, nitrogen concentration, and gross energy concentration of the urine samples were determined every 2 d. In Exp. 2, three urine samples with different nitrogen concentrations were added with different amounts of 6 N HCl to obtain varying pH values. All urine samples were placed on a laboratory table for 5 d followed by nitrogen analysis. Results: Nitrogen amounts in urine supplemented with distilled water decreased linearly with time, whereas those supplemented with 6 N HCl remained constant. Based on the linear broken-line analysis, nitrogen was not volatilized at a pH below 5.12 (standard error = 0.71 and p<0.01). In Exp. 3, an equation for determining the amount of 6 N HCl to preserve nitrogen in pig urine was developed: additional 6 N HCl (mL) to 100 mL of urine = 3.83×nitrogen in urine (g/100 mL)+0.71 with R² = 0.96 and p<0.01. If 62.7 g/d of nitrogen is excreted, at least 240 mL of 6 N HCl should be added to the urine collection container. Conclusion: Nitrogen in pig urine is not volatilized at a pH below 5.12 at room temperature and the amount of 6 N HCl required for nitrogen preservation may be up to 240 mL per day for a 110-kg pig depending on urinary nitrogen excretion.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.244-244
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• 2023

Both the frequency and the magnitude of hydrometeorological extreme events such as severe floods and droughts are increasing. In order to prevent a damage from the climatic disaster, hydrological models are often simulated under various meteorological conditions. While performing the simulations, a synthetic data generated through time series models which maintains the key statistical characteristics of the sample data are widely applied. However, the synthetic data can easily maintains both the average and the variance of the sample data, but the quantile is not maintained well. In this study, we proposes a data generation method which maintains the quantile of the sample data well. The equations of the former maintenance of variance extension (MOVE) are expanded to maintain quantile rather than the average or the variance of the sample data. The equations are derived and the coefficients are determined based on the characteristics of the sample data that we aim to preserve. Monte Carlo simulation is utilized to assess the performance of the proposed data generation method. A time series data (data length of 500) is regarded as the sample data and selected randomly from the sample data to create the data set (data length of 30) for simulation. Data length of the selected data set is expanded from 30 to 500 by using the proposed method. Then, the average, the variance, and the quantile difference between the sample data, and the expanded data are evaluated with relative root mean square error for each simulation. As a result of the simulation, each equation which is designed to maintain the characteristic of data performs well. Moreover, expanded data can preserve the quantile of sample data more precisely than that those expanded through the conventional time series model.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.85-85
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• 2020

South Korea experiences few large scale erosion and sedimentation problems, however, there are numerous local sedimentation problems. A reliable and consistent approach to modelling and management for sediment processes are desirable in the country. In this study, field measurements of sediment concentration from 34 alluvial river basins in South Korea were used with the Modified Einstein Procedure (MEP) to determine the total sediment load at the sampling locations. And then the Flow Duration-Sediment Rating Curve (FD-SRC) method was used to estimate the specific degradation for all gauging stations. The specific degradation of most rivers were found to be typically 50-300 tons/㎢·yr. A model tree data mining technique was applied to develop a model for the specific degradation based on various watershed characteristics of each watershed from GIS analysis. The meaningful parameters are: 1) elevation at the middle relative area of the hypsometric curve [m], 2) percentage of wetland and water [%], 3) percentage of urbanized area [%], and 4) Main stream length [km]. The Root Mean Square Error (RMSE) of existing models is in excess of 1,250 tons/㎢·yr and the RMSE of the proposed model with 6 additional validations decreased to 65 tons/㎢·yr. Erosion loss maps from the Revised Universal Soil Loss Equation (RUSLE), satellite images, and aerial photographs were used to delineate the geospatial features affecting erosion and sedimentation. The results of the geospatial analysis clearly shows that the high risk erosion area (hill slopes and construction sites at urbanized area) and sedimentation features (wetlands and agricultural reservoirs). The result of physiographical analysis also indicates that the watershed morphometric characteristic well explain the sediment transport. Sustainable management with the data mining methodologies and geospatial analysis could be helpful to solve various erosion and sedimentation problems under different conditions.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• Journal of Radiation Protection and Research
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• 제48권4호
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• pp.175-183
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• 2023

Background: Recently, biological adsorbents have been developed for removing radionuclides from radioactive liquid waste due to their high selectivity, eco-friendliness, and renewability. However, since they can be damaged by radiation in radioactive waste, a method for estimating the bio-adsorbent performance as a time should consider the radiation damages in terms of their renewability. This paper aims to develop a simulation method that applies a deep learning technique to rapidly and accurately estimate the adsorption performance of bio-adsorbents when inserted into liquid radioactive waste. Materials and Methods: A model that describes various interactions between a bio-adsorbent and liquid has been constructed using numerical methods to estimate the adsorption capacity of the bio-adsorbent. To generate datasets for machine learning, Monte Carlo N-Particle (MCNP) simulations were conducted while considering radioactive concentrations in the adsorbent column. Results and Discussion: Compared with the result of the conventional method, the proposed method indicates that the accuracy is in good agreement, within 0.99% and 0.06% for the R² score and mean absolute percentage error, respectively. Furthermore, the estimation speed is improved by over 30 times. Conclusion: Note that an artificial neural network can rapidly and accurately estimate the survival rate of a bio-adsorbent from radiation ionization compared with the MCNP simulation and can determine if the bio-adsorbents are reusable.

• Anatomy and Cell Biology
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• 제56권2호
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• pp.259-267
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• 2023

Stature is an essential component of biological profile analysis since it determines an individual's physical identity. Long bone dimensions are generally used to estimate the stature of skeletal remains; however, non-long bones such as the sternum, cranium, and sacrum may be necessary for some forensic situations. This study aimed to generate a regression equation for stature estimation of the skeletal remains in the Thai population. Ten measurements of the sacrum were measured from 200 dry sacra. The results revealed that the maximum anterior breadth (MAB) provided the most accurate stature prediction model among males (correlation coefficient [r]=0.53), standard error of estimation (SEE=5.94 cm), and females (r=0.48, SEE=6.34 cm). For the multiple regression model, the best multiple regression models were stature equals 41.2+0.374 (right auricular surface height [RASH])+1.072 (anterior-posterior outer diameter of S₁ vertebra corpus [APOD])+0.256 (dorsal height [DH])+0.417 (transverse inner diameter of S₁ vertebra corpus [TranID])+0.2 (MAB) with a SEE of 6.42 cm for combined sex. For males, stature equals 63.639+0.478 (MAB)+0.299 (DH)+0.508 (APOD) with a SEE of 5.35, and stature equals 75.181+0.362 (MAB)+0.441 (RASH)+0.132 (maximum anterior height [MAH]) with a SEE of 5.88 cm for females. This study suggests that regression equations derived from the sacrum can be used to estimate the stature of the Thai population, especially when a long bone is unavailable.

• 한국전산구조공학회논문집
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• 제37권3호
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• pp.179-186
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• 2024

본 논문에서는 충격파 형태의 폭발 하중을 받는 부재의 소성 범위를 고려한 SDOF 해석의 수정계수를 개발하였다. SDOF 해석의 수정계수는 MDOF 해석 결과 값을 비교하여 도출하였다. SDOF 해석에 영향을 미치는 매개변수로 부재의 경계조건, 폭발 하중 지속시간과 고유주기 비를 선정하였다. 수정계수는 탄성 하중-질량 변환 계수를 기준으로 산정하였다. 수정계수 곡선은 상한, 하한 매개변수 경계 사이에 있도록 타원 방정식을 이용하여 도출하였다. 서로 다른 단면과 경계조건을 가지는 예제에 수정계수를 적용한 결과 SDOF 해석의 오차율이 15%에서 3%로 감소하였다. 본 연구의 결과는 수정계수를 적용하여 SDOF 해석의 정확도를 높임에 따라 폭발 해석에 널리 활용될 수 있다.