• Food Science and Preservation
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• v.24 no.6
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• pp.778-785
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• 2017

This study was conducted to develop a predictive model for the growth of Escherichia coli strain RC-4-D isolated from red kohlrabi sprout seeds. We collected E. coli kinetic growth data during red kohlrabi seed sprouting under isothermal conditions (10, 15, 20, 25, and $30^{\circ}C$). Baranyi model was used as a primary order model for growth data. The maximum growth rate (${\mu}max$) and lag-phase duration (LPD) for each temperature (except for $10^{\circ}C$ LPD) were determined. Three kinds of secondary models (suboptimal Ratkowsky square-root, Huang model, and Arrhenius-type model) were compared to elucidate the influence of temperature on E. coli growth rate. The model performance measures for three secondary models showed that the suboptimal Huang square-root model was more suitable in the accuracy (1.223) and the suboptimal Ratkowsky square-root model was less in the bias (0.999), respectively. Among three secondary order model used in this study, the suboptimal Ratkowsky square-root model showed best fit for the secondary model for describing the effect of temperature. This model can be utilized to predict E. coli behavior in red kohlrabi sprout production and to conduct microbial risk assessments.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.

• Korean Journal of Food Science and Technology
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• v.27 no.3
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• pp.387-393
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• 1995

Diffusion coefficients of moisture and solid, reaction rate constants of carotene destruction, and the fitness of drying models for moisture transfer were determined to study the characteristics of mass transfer during osmotic dehydration. Moisture loss and solid gain were increased with increase of temperature and concentration; temperature had higher osmotic effect than concentration. Diffusion coefficient showed similar trend with osmotic effect. Diffusion coefficients of solids were larger than those of moisture because the movement of solid was faster than that of moisture at the high temperature. Reaction rate constants were affected to the greater extent by concentration changes than by temperature changes. Arrhenius equation was applied to determine the effect of temperature on diffusion coefficients and reaction rate constants. Moisture diffusion required high activation energy in $20^{\circ}Brix$, while relatively low in $60^{\circ}Brix$. To predict the diffusion coefficients and reaction rate constants, a model was established by using the optimum functions of temperature and concentration. The model had high $R^2$ value when applied to diffusion coefficients, but low when applied to reaction rate constants. Quadratic drying model was most fittable to express moisture transfer during drying. In conclusion, moisture content of carrots could be predictable during the osmotic dehydration process, and thereby mass transfer characteristics could be determined by predicted moisture content and diffusion coefficient.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.758-761
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• 2010

본 연구에서는 원전격납구조물과 같이 고품질의 대형 매스콘크리트의 설계, 시공, 품질예측 및 관리에 필요한 구조물 건전성 평가시스템 구축의 일환으로 수화열 예측 프로그램을 개발하였다. 개발된 수화열 예측 프로그램은 국내에서 생산된 시멘트 및 결합재의 화학조성성분, 분말도와 같은 기초정보 및 배합정보를 입력하여 경시변화에 따른 수화발열량을 계산하는 방식으로서 기존의 연구결과를 바탕으로 개발되었다. 개발된 프로그램은 배합환경조건을 고려하여 초기배합온도 3종류(10, 20, $30^{\circ}C$)로 실험한 단열온도상승 실험 결과와 비교해 보았으며, 좋은 상관성을 나타냄을 확인할 수 있었다.

• Food Science and Preservation
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• v.16 no.5
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• pp.719-725
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• 2009

We investigated the absorption characteristics of protein-bound polysaccharide powders of various molecular weights isolated from the mushroom Agaricus blazei Murill. The monolayer moisture content calculated using the GAB equation showed a higher level of significance than did the BET equation. The higher the water activity, the lower the isosteric heat of sorption. The fitness of the isotherm curve was shown to be in the order of the Khun, Oswin, Caurie and Henderson models. The prediction model equations for moisture content were established by use of ln(time), water activity, and temperature.

• Korean Journal of Materials Research
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• v.5 no.5
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• pp.503-511
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• 1995

금속/실리콘계에 대한 이온선 혼합시의 비정질상 및 결정상 형성여부를 예측할 수 있는 모델(ADF Model)과초기 결정상 예측 모델(PDF Model)의 적용을 실험적으로 조사하기 위하여 Ni/Si계에 대한 이온선 혼합을 온도와 이온선량을 변수로 하여 행하였으며 상형ㅅㅇ과정을 해석하였다. 이온선 혼합은 80keV가속기를 이용하여 상온~20$0^{\circ}C$의 온도 범위에서 1.0 $\times$ $10^{15}$Ar^{+}$/$cm^{2}$~2.0 $\times$ $10^{-16}$Ar^{+}$/$cm^{2}$의 이온선량을 변화시키면서 실험하였고, 상분석은 TEM과 GXRD를 이용하였다. Ni/Si게에 대한 ADF값은 0.804로 양의 값을 가지므로 이온선 혼합시 비정실상이 형성되고, $Ni_{2}$Si상이 다른 화합물상보다 훨씬 큰 음의 PDF값을 갖으므로 초기 결정상이 $Ni_{2}$Si가 될 것을 예측하였다. 이러한 예측은 실험결과와 매우 잘 일치하였다. 이상의 연구결과로부터 ADF 및 PDF모델을 이용하여 박막에서 형성되는 상을 보다 정확히 예측할수 잇음을 알 수 있었다.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.107-119
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• 2013

바이오매스 가스화는 세계적인 증가 추세에 있는 에너지 수요를 충족할 수 있는 기술 중의 하나이다. 바이오매스 가스화를 통해서 농업 폐기물 등 다양한 바이오매스 자원을 에너지로 전환할 수 있고 $CO_2$ 배출량 또한 줄일 수 있다. 본 연구에서는 COMSOL$^{(R)}$ 3.4 소프트웨어를 이용하여 바이오매스 원료와 운전 조건에 따른 가스화 효율 및 합성가스 조성의 변화를 분석하였다. 원료와 구동조건을 최적화하기 위해 가스화 모델을 세우고 원료와 구동조건을 달리하여 합성가스의 성분을 분석 및 예측하였다. 이 모델은 물리적인 실험을 통해 알고 있는 조건을 통해서 합성가스 성분을 시간에 따라 예측할 수 있다. 모델을 이용하여 함수비 5~30 %, 공기중 산소함량 5~50 %, 공기공급 유량 5~45 L/min, 온도 973~1273 K의 조건에서 합성가스의 성분을 예측한 결과 실제 실험 결과와 일치하는 것을 알 수 있다. 모델링 결과 양질의 합성가스를 생산하려면 원료의 회분함량이 적어야 하고 수소 함량이 높은 합성가스를 생산하려면 반응 온도가 높게 유지되고 원료의 함수비가 높아야 한다. 가스화장치의 온도를 높이면 합성가스의 성분 중 CO의 함량이 많아지고, CO의 함량이 많아지면 가스의 발열량이 높아지는 것을 알 수 있다. 또한 CO의 농도가 높고 발열량이 높은 합성가스를 생산하기 위해서는 ER값은 작아야 한다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.3
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• pp.195-201
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• 2001

Sowing date experiments were carried out by employing a rice variety "Kwanganbyeo" in both field and phytotron with natural daylength. In phytotron, temperatures were controlled at daily mean of 21$^{\circ}C$ and 24$^{\circ}C$. The responses of final leaf number and beading date were analyzed in relation to daylength during photo-sensitive period (PSP). Based on the component models predicting the final leaf number and leaf appearance rate, a rice phenology model was established and verified. Days from sowing to flowering (DSF) were shortened and final number of leaves (FNL) increased as sowing dates were delayed from 25 April to 5 June in field and phytotron. The increased leaf appearance rate (LAR) and the reduced FNL, respectively, due to the higher temperature and the shorter daylength in delayed sowings in the field brought about greater shortening of DSF than in the phytotron where only FNL was reduced by shorter daylength in delayed sewings. FNL showed very close relationship with the average daylength during PSP of six-leaf stage to panicle initiation, being well fitted to the following rational function ($R^2$=0.98):(equation omitted) where D is daylength and a, b, and c are the constants that were estimated as 14.694, -0.992, and -0.068 in Kwanganbyeo, respectively. The rice phonology model, which was composed of two component models for LAR and FNL, predicted DSF very accurately. The differences between the observed and predicted DSF was less than two days in the sewing date field experiments in 1999 and 2000 of which data were not used for the model construction.struction.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.4
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• pp.338-344
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• 2018

The lunar surface temperature is important as a environmental parameter for the thermal design of the lunar exploration vehicles such as orbital spacecraft, lander, and rovers. In this study, the temperature is numerically predicted through a simplified lumped system model for the energy conservation. The physical values required for the analysis of the energy equation are derived by considering the geometric shape, and the values presented in the previous research results. The areal specific heat, which is the most important thermo-physical property of the lumped system model, was extracted from the temperature measurements by the Diviner loaded on the LRO, and the value was predicted by calibration of the analytical model to the measurements. The predicted temperature distribution obtained through numerical integration has sufficient accuracy to be applied to the thermal design of the lunar exploration vehicles.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.206-206
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• 2022

제주도는 투수성이 좋은 대수층이 발달한 화산섬으로 지하수가 가장 중요한 수자원이다. 인위적 요인과 기후변화로 인해 제주도의 지하수위가 저하하는 추세를 보이고 있음에 따라 지하수의 적정 관리를 위해 지하수위의 정확하고 장기적인 예측이 매우 중요하다. 다양한 환경적인 요인이 지하수의 함양 및 수위에 영향을 미치는 것으로 알려져 있지만, 제주도의 특징적인 기상인자가 지하수 시스템에 어떻게 영향을 미치는지를 파악하기 위한 연구는 거의 진행되지 않았다. 지하수위측에 있어서 물리적 모델을 이용한 방안은 다양한 조건에 의해 변화하는 지하수위의 정확하고 빠른 예측에 한계가 있는 것으로 알려져 있다. 이에 본 연구에서는 제주도 애월읍과 남원읍에 위치한 지하수위 관측정의 일 수위자료와 강수량, 온도, 강설량, 풍속, VPD의 다양한 기상 자료를 대상으로 인공신경망 알고리즘인 다층 퍼셉트론(MLP)와 Long Short Term Memory(LSTM)에 기반한 표준지하수지수(SGI) 예측 모델을 개발하였다. MLP와 LSTM의 표준지하수지수(SGI) 예측결과가 상당히 유사한 것으로 나타났으며 MLP과 LSTM 예측모델의 결정계수(R²)는 애월읍의 경우 각각 0.98, 남원읍의 경우 각각 0.96으로 높은 값을 보였다. 본 연구에서 개발한 지하수위 예측모델을 통해 효율적인 운영과 정밀한 지하수위 예측이 가능해질 것이며 기후변화 대응을 위한 지속가능한 지하수자원 관리 방안 마련에 도움을 줄 것이라 판단된다.