• 한국전기전자재료학회논문지
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• 제31권3호
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• pp.182-187
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• 2018

In this study, 20.8% of a p-type Si bifacial solar cell was used to develop a photovoltaic (PV) module to obtain the maximum power under a limited installation area. The transparent back sheet material was replaced during fabrication with a white one, which is opaque in commercial products. This is very beneficial for the generation of more electricity, owing to the additional power generation via absorption of light from the rear side. A new model is suggested herein to predict the power of the bifacial PV module by considering the backside reflections from the roof and/or environment. This model considers not only the frontside reflection, but also the nonuniformity of the backside light sources. Theoretical predictions were compared to experimental data to prove the validity of this model, the error range for which ranged from 0.32% to 8.49%. Especially, under $700W/m^2$, the error rate was as low as 2.25%. This work could provide theoretical and experimental bases for application to a distributed and microgrid network.

• 생물환경조절학회지
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• 제29권1호
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• pp.28-35
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• 2020

본 연구는 봄에서 여름 재배기에 토마토를 암면배지 재배에서 누적일사량급액방식(ISR)과 물관수액흐름 속도에 따른 급액방식(SF)에서 급배액량, 수분흡수량, 과실 생육 및 과실 생산량, 과실비대속도를 관찰하였다. 정식 후 28일에서 82일까지 총급액량은 SF 제어구에서 약 5.0L 적게 소비되었으나 지상부와 과실 생체중은 유의차가 없었고 수분이용효율(WUE)과 과실 200g을 생산하는데 소요된 물량도 두처리 간 유의차가 없었다. 정식 후 58일에서 82일까지 급액방식에 따른 실시간 광량과 물관수액흐름속도 상관관계를 관찰하였을 때 상관관계(r²)는 SF제어구에서 더 높게 나타났다. 정식 56일부터 82일까지 실시간 측정된 과경비대속도와 배지함수율의 상관관계를 살펴본 결과 SF제어구에서 야간과 낮 시간대에 ISR제어구 보다 높았고 특히 야간 시간대에 상관관계가 더 높게 나타났다. SF제어구의 물관수액흐름속도와 수분부족분(Humidity Deficit: HD)과의 상관관계(r²=0.6286)도 광량과의 관계만큼 높게 나타났다. 더 많은 현장 연구를 통해 수확량, WUE 및 센서의 정확도과 같은 특성에 관한 결과들을 도출하였을 때 상업적 수경재배 농장 재배자의 관심을 얻을 수 있을 것으로 보인다.

• 전기전자학회논문지
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• 제23권2호
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• pp.743-746
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• 2019

본 논문에서는 새로운 해양 방사선 자동 감시 시스템을 제안한다. 제안하는 시스템은 다음과 같은 특징들을 가진다. 첫 번째로 NaI + PVT 혼합형 검출기를 사용함으로 반응속도가 빠르고 정밀분석이 가능하다. 두 번째로 섬광체형 센서에 온도보상 알고리즘을 적용함으로서 추가적인 냉각장치가 필요 없으며 시시각각 변화하는 해양환경에 안정적인 운영이 가능하다. 세 번째로 냉각장치가 필요 없으므로 전력소비량이 적어 태양열을 활용하여 전력의 안정적인 공급이 가능하므로 해양환경 관측부이에 설치 가능하다. 네 번째로 GPS 및 무선통신을 사용하여 측정지역에 대한 정확한 위치정보와 실시간 데이터 전송기능으로 주변국 등의 원전사고 등 발생 시 즉각적인 경보대응이 가능하다. 제안된 시스템의 성능을 평가하기 위하여 공인시험기관에서 실험한 결과는 방사선 측정범위는 세계 최고 수준인 $5{\mu}Sv/h{\sim}15mSv/h$의 범위에서 측정이 되었고, 정확도는 ${\pm}8.1%$의 측정 불확도가 측정되어 국제 표준인 ${\pm}15%$ 이하에서 정상동작 됨이 확인되었다. 내환경등급(방수)은 IP67을 달성하였고, $-20{\sim}50^{\circ}C$ 동작온도에서 5% 이내로 변동률이 측정되어서 안정성이 확인되었다. 진동시험 후 측정값 변화율이 10% 이내로 측정되어서, 파도에 의한 해양환경에서 진동으로 인한 측정값의 변화가 없을 것으로 확인되었다.

• 생물환경조절학회지
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• 제23권3호
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• pp.181-191
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• 2014

Modern commercial greenhouse requires the use of advanced climate control system to improve crop production and to reduce energy consumption. As an alternative to classical sensor-based control method, this paper introduces a model-based control method that consists of two models: the predictive model and the evaluation model. As a first step, this paper presents straightforward models to predict the effect of natural ventilation in a greenhouse according to meteorological factors, such as outdoor air temperature, soil temperature, solar radiation and mean wind speed, and structural factor, opening rate of roof ventilators. A multiple regression analysis was conducted to develop the predictive models on the basis of data obtained by computational fluid dynamics (CFD) simulations. The output of the models are air temperature drops due to ventilation at 9 sub-volumes in the greenhouse and individual volumetric ventilation rate through 6 roof ventilators, and showed a good agreement with the CFD-computed results. The resulting predictive models have an advantage of ensuring quick and reasonable predictions and thereby can be used as a part of a real-time model-based control system for a naturally ventilated greenhouse to predict the implications of alternative control operation.

• 한국농공학회논문집
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• 제54권4호
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• pp.65-71
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• 2012

The objective of this study was to estimate surface runoff from rice paddy plots using an artificial neural network (ANN). A field experiment with three treatment levels was conducted in the NICS saemangum experimental field located in Iksan, Korea. The ANN model with the optimal network architectures, named Paddy1901 with 19 input nodes, 1 hidden layer with 16 neurons nodes, and 1 output node, was adopted to predict surface runoff from the plots. The model consisted of 7 parameters of precipitation, irrigation rate, ponding depth, average temperature, relative humidity, wind speed, and solar radiation on the daily basis. Daily runoff, as the target simulation value, was computed using a water balance equation. The field data collected in 2011 were used for training and validation of the model. The model was trained based on the error back propagation algorithm with sigmoid activation function. Simulation results for the independent training and testing data series showed that the model can perform well in simulating surface runoff from the study plots. The developed model has a main advantage that there is no requirement for any prior assumptions regarding the processes involved. ANN model thus can be a good tool to predict surface runoff from rice paddy fields.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.1142-1146
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• 2004

The objective of this study is to the test applicability of SLURP on Soyanggang-dam watershed. The area of this watershed is $2,694km^2$ and mean elevation and slope is 650 m and $23^{\circ}$ respectively. Topographical parameters were derived from DEM using TOPAZ and SLURPAZ. NDVI was calculated from multi-temporal NOAA/AVHRR images. The daily meteorological data and hydrograph during $1999\~2001$ were selected for model calibration and performance tests. Weather elements (dew-point temperature, solar radiation, maximum and minimum temperature, relative humidity) were required from the S meteorological stations near the study area. The model parameters of each land cover class were optimized by sensitivity analysis and SCE-UA method. Runoff rate shows $49.33\%\~64.06\%$. Simulated results during 4 years were estimated by Nash-Sutcliffe efficiency and WMO volume error. Nash-Sutcliffe efficiency shows $0.61\~0.75$ and WMO volume error shows $6.1\%-18.8\%$.

• 한국대기환경학회지
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• 제18권6호
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• pp.515-525
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• 2002

A field study was conducted to estimate the emission rate of biogenic volatile organic compounds (BVOCs) from pine trees. In addition, the influences of meteological variables on their distribution characteristics have been investigated. A vegetation enclosure chamber was designed and constructed of Tedlar bag and acril. Sorbent tubes made up of Tenax TA and Carbotrap were used to collect biogenic VOCs emitted from each individual tree. Analysis of BVOCs was performed using a GC-FID system. The fundamental analytical parameters including linearity, retention time, recovery efficiency, and breakthrough volume were examined and verified for the determination of monoterpene emission rates. Total average concentration of each component is found to be $\alpha$-pinene (16.5), $\beta$-pinene (4.61) from pine trees, and $\alpha$-pinene (42.4), $\beta$-pinene (18.7 ng(gdw)$^{-1}$ hr$^{-1}$ ) from Korean pine trees. On the basis of our study, $\alpha$-pinene was found to be the major monoterpene emitted from both pine and Korean pine trees which were accompanied by $\beta$-pinene, camphene, and limonene. In ambient air, variable monoterpene compositions of emissions from pine trees were similar to Korean pine trees. Emission rates of monoterpene from each tree were found to depend on such parameters as temperature and solar radiation.

• The Korean Journal of Ecology
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• 제23권4호
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• pp.293-298
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• 2000

To study the effect of global warming on the growth of plants and plant populations throughout their life cycle under a field-like condition, we constructed a Temperature Gradient Chamber (TGC) in Tsukuba, Japan. The chamber had slender shape : 30 m long. 3 m wide, and 2.5 m high. That satisfactory performance was confirmed by a test throughout all seasons in 1998: the projected global warming condition in the near future was simulated. That is, independent of a great daily or seasonal change in ambient meteorological conditions, air temperatures at the air outlet were warmed 5$^{\circ}C$ higher than those at the ambient (the annual mean was 14.3$^{\circ}C$) with precision of ${\pm}$0.2$^{\circ}C$ (the annual means were 19.2$^{\circ}C$) with a rising rate of approximately 1$^{\circ}C$ every 5 m. This chamber will enable us to study the effects of global warming on growth of plants and plant populations because their abilities to control air temperature are excellent. TGC is expected that it would be utilized for studying the effect of global warming on plant growth under natural weather conditions.

• 한국식물병리학회지
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• 제14권2호
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• pp.150-156
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• 1998

To develop a model for prediction of turnip mosaic virus(TuMV) disease progress of Chinese cabbage based on weather information and number of TuMV vector aphids trapped in Taegwallyeong alpine area, data were statistically processed together. As the variables influenced on TuMV disease progress, cumulative portion(CPT) above 13$^{\circ}C$ in daily average temperature was the most significant, and solar radiation, duration of sunshine, vector aphids and cumulative temperature above $0^{\circ}C$ were significant. When logistic model and Gompertz model were compared by detemining goodness of fit for TuMV disease progress using CPT as independent variable, regression coefficient was higher in the logistic model than in the Gompertz model. Epidemic parameters, apparent infection rate and initial value of logistic model, were estimated by examining the relationship between disease proportion linearized by logit transformation equation, In(Y/Yf-Y) and CPT. Models able to describe the progression of TuMV disease were formulated in Y=100/(1+128.4 exp(-0.013.CPT.(-1(1/(1+66.7.exp(-0.11.day). Calculated disease progress from the model was in good agreement with investigated actual disease progress showing high significance of the coefficient of determination with 0.710.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.