• Korean Journal of Food Science and Technology
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• v.16 no.1
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• pp.108-112
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• 1984

The sorption characteristics of red pepper powder stored at various relative humidity and temperature were studied. At low relative humidity below RH 57%, the sorption equilibrium was easily attained, whereas at higher relative humidity above RH 75%, the powder was browned by higher equilibrium moisture content. The moisture content of monolayer value for the powder was ranging from 11.32% to 12.13% with temperature. First order regression equation of equilibrium moisture content for relative humidity was determined.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.945-945
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• 2012

본 연구에서는 낙동강 수계의 안동댐 유역을 대상지역으로 선정하여 미래 기후변화 시나리오에 따른 댐 유역의 수환경 영향을 예측해 보고자 하였다. 특히 미래기후에 대한 수환경 평가는 기후자료를 입력 값으로 요구하는 강우-유출모형을 이용하거나 유량 이외에 유사, 영양물질과 같은 수질인자를 동시에 모의할 수 있는 유역모형을 이용하여 평가하는 것이 일반적이다. 이를 위해 선행연구로 IPCC(Intergovernmental Panel on Climate Change)에서 제공하는 AR4 시나리오의 RCM 자료를 ANN(Artificial Neural Network)기법을 이용하여 안동댐 유역의 총 4개 기상관측소에 대한 과거 20년(1991~2010) 실측자료를 바탕으로 미래 강수 및 습도 그리고 온도에 대해 상세화 하여 미래 기후 시나리오를 생산하였다. 또한 안동댐 유역 단위의 수질을 예측하기 위해 토양과 토지이용 및 토지관리 상태에 따른 수문-수질 모의가 가능한 유역모형인 SWAT(Soil and Water Assessment Tool)을 이용하였다. 과거의 기상자료와 수질자료를 이용하여 유역모델의 검 보정을 실시하였으며 모형의 보정 및 검증결과에 따른 적합성과 상관성을 판단하기 위해 결정계수($R^2$)와 평균제곱근오차(Root Mean Square Error, RMSE)를 사용하였으며, 모형의 효율성 검증으로는 Nash and Sutcliffe(1970)가 제안한 모형효율성계수(NSE)를 사용하였다. 최종적으로 기후 시나리오에 대해서 전망된 지역상세기후를 유역모형의 입력자료로 이용하여 안동댐 유역의 미래수문 및 수질을 예측하고자 하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.892-897
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• 2020

Previous plastic heat exchangers are expensive because the mold must be newly manufactured depending on the air conditioning space. On the other hand, danpla is so thin that the heat exchange performance is excellent. Moreover, danpla can be used easily in ventilation systems in view of fabrication. This study proposes correlations for the cooling performance of an indirect evaporative cooling system. The experimental apparatus consisted of a heat exchanger, spray nozzle, fan, thermostat, pump, and measuring sensors for temperature, humidity, and airflow rate. The results showed that the effectiveness decreased gradually as the airflow rate increased. In addition, there was an optimal condition in terms of effectiveness. The performance prediction correlations were determined using the experimental data from various conditions. The proposed correlations showed performance accuracies within 4 % error.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.701-704
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• 2014

This Paper proposed the analysis and prediction method of beverage sales. We assumed weather had a relationship with beverage sales. We got the output as sales amount from a temperature and humidity of weather as input by using polynomial equation. We had modelling as quadric function with input and output data. In order to verify the effectiveness of proposed method, the sales data were collected over a 4 months during February 2014. The results showed that the proposed method can estimate sales data.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1027-1031
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• 2008

본 연구는 한반도 영역을 대상으로 2001년 7, 8월과 2002년 6월로 홍수기를 대상으로 RDAPS 모형, AWS, 상층기상관측(upper-air sounding)의 자료를 이용하였다. 또한 수치예보자료를 범주적 예측확률로 변환하고 인공신경망기법(ANN)을 이용하여 강수발생확률의 예측정확성을 향상시키는데 있다. 신경망의 예측인자로 사용된 대기변수는 500/ 750/ 1000hpa에서의 지위고도, 500-1000hpa에서의 층후(thickness), 500hpa에서의 X와 Y의 바람성분, 750hpa에서의 X와 Y의 바람성분, 표면풍속, 500/ 750hpa/ 표면에서의 온도, 평균해면기압, 3시간 누적 강수, AWS관측소에서 관측된 RDAPS모형 실행전의 6시간과 12시간동안의 누적강수, 가강수량, 상대습도이며, 예측변수로는 강수발생확률로 선택하였다. 강우는 다양한 대기변수들의 비선형 조합으로 발생되기 때문에 예측인자와 예측변수 사이의 복잡한 비선형성을 고려하는데 유용한 인공신경망을 사용하였다. 신경망의 구조는 전방향 다층퍼셉트론으로 구성하였으며 역전파알고리즘을 학습방법으로 사용하였다. 강수예측성과의 질을 평가하기 위해서 $2{\times}2$ 분할표를 이용하여 Hit rate, Threat score, Probability of detection, Kuipers Skill Score를 사용하였으며, 신경망 학습후의 강수발생확률은 학습전의 강수발생확률에 비하여 한반도영역에서 평균적으로 Kuipers Skill Score가 0.2231에서 0.4293로 92.39% 상승하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.570-576
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• 2020

This study uses beacons and smartphone Global Positioning System (GPS) receivers to establish a location-based disaster/hazard prediction system. Beacons are usually installed indoors to locate users using triangulation in the room, but this study is differentiated from previous studies because the system is used outdoors to collect information on registration location and temperature and humidity in hazardous areas. In addition, since it is installed outdoors, waterproof, dehumidifying, and dustproof functions in the beacons themselves are required, and in case of heat and humidity, the sensor must be exposed to the outside, so the waterproof function is supplemented with a separate container. Based on these functions, information on declining and vulnerable areas is identified in real time, and temperature/humidity information is collected. We also propose a system that provides weather and fine-dust information for the area concerned. User location data are acquired through beacons and smartphone GPS receivers, and when users transmit from declining or vulnerable areas, they can establish the data to identify dangerous areas. In addition, temperature/humidity data in a microspace can be collected and utilized to build data to cope with climate change. Data can be used to identify specific areas of decline in a microspace, and various analyses can be made through the accumulated data.

• Journal of Applied Biological Chemistry
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• v.63 no.1
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• pp.1-8
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• 2020

This study investigated the processing characteristics of soybean (HaePum) sprouts based on temperature (5, 15 and room temperature), period (0, 6 and 12 mon) and relative humidity (20, 40, 60, and 80%) during storage. The initial germination rate of soybean sprouts was 76.02±6.32%, which significantly reduced to 57.18±8.51%, and 0% as the storage temperature of soybean increased for a period of 12 mon. The germination rate of soybean sprouts with 30 ℃ and 80% RH decreased after 4 mon of storage to 4.94%. The yellowness of cotyledon of soybean sprouts was not significantly changed during the 12 mon period of storage at 5 ℃, whereas, soybean sprouts stored at 15 ℃ and room temperature demonstrated decreased yellowness. However, the stress of cotyledon decreased with an increase in both storage temperature and time, and the hardness of hypocotyl decreased with an increase of storage time. The stress of cotyledon affected by high temperature (30 ℃) and humidity (80%) during 4 mon was reduced to 44.39±9.38 g/㎟. The asparagine content of soybean sprouts showed a similar result with the germination rate due to the amount of hypocotyl. Therefore, lower temperatures and shorter storage times are suitable for soybean sprout processing. The first order kinetic model and Arrhenius equation (activation energy =29.56 kJ/mol) were able to predict the yield of sprout at various storage temperatures and periods.

• Journal of the Korean Society of Tobacco Science
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• v.12 no.2
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• pp.59-65
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• 1990

In order to predict the shelf-life of cigarettes packaged in typical flexible film under conditions of various temperature, relative humidity and sealing degree, a computer iterative technique was used. Although there were some significant differences at initial equilibrium relative humidity(55%), the experimental results agree fairly well with predictions following the student's t test($\alpha$=0.01) in most cases. Essentially, the higher the storage temperature, the shorter the shelf-life of the cigarette product. The bigger the differences from the initial equilibrium relative humidity, the shorter the storage period of the cigarette. Moisture transfer through the film at relatively high temperature gave higher confidence. The sealing degree, one of the storage parameters, appeared to be a major influencing factor to shelf-life. Slopes($\beta$) of the temp., sealing degree and %rh of the dependent variable to shelf life were 0.49, -0.39 and -0, 28 respectively, when analysed by multiple regression of SPSS software. Below 600m1/min sealing decree of the packed cigarette through the sealing Position at 30mmH20 differential pressure, the shelf-life could be increased by more than six months.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.7-13
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• 2021

This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.