• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.3
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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.

• Journal of Bio-Environment Control
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• v.29 no.1
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• pp.28-35
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• 2020

This study is to compare irrigation efficiency between sap flow sensor automated system (SF) and conventional irrigation system based on integrated solar radiation automated system (ISR) in tomato rockwool hydroponics. Total irrigated volumes was higher in the ISR system by 5.0L per plant, a lower drainage rate was found in the SF system, compared to the ISR system. There was no difference in shoot and fruit fresh weights, water use efficiency (WUE) and water amount consumed for producing 200g of tomato fruit. The daily average sap flow density (SFD) was closer to the change of solar irradiance (SI) in the plant grown under the SF system, compared to the ISR system. The correlation coefficient (r²) between the fruit diameter and the volumetric water content during the 56 and 82 days after transplant showed the SF treatment was higher than the ISR at night and daytime, and the correlation was higher at night time. The sap flow density and humidity deficit (HD) of SF treatment was related as closely as the solar irradiance. Further studies should demonstrate that SF irrigation system is a convenient method for hydroponic farmers with advantages, such as growth, higher yield, WUE, and accuracy.

• Journal of IKEEE
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• v.23 no.2
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• pp.743-746
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• 2019

In this paper we proposed a new ocean radiation automatic monitoring system. The proposed system has the following characteristics: First, using NaI + PVT mixed detectors, the response speed is fast and precision analysis is possible. Second, the application of temperature compensation algorithm to scintillator-type sensors does not require additional cooling devices and enables stable operation in the changing ocean environment. Third, since cooling system is not needed, electricity consumption is low, and electricity can be supplied reliably by utilizing solar energy, which can be installed at the observation deck of ocean environment. Fourth, using GPS and wireless communications, accurate location information and real-time data transmission function for measurement areas enables immediate warning response in the event of nuclear accidents such as those involving neighboring countries. The results tested by the authorized testing agency to assess the performance of the proposed system were measured in the range of $5{\mu}Sv/h$ to 15mSv/h, which is the highest level in the world, and the accuracy was determined to be ${\pm}8.1%$, making normal operation below the international standard ${\pm}15%$. The internal environmental grade (waterproof) was achieved, and the rate of variation was measured within 5% at operating temperature of $-20^{\circ}C$ to $50^{\circ}C$ and stability was verified. Since the measured value change rate was measured within 10% after the vibration test, it was confirmed that there will be no change in the measured value due to vibration in the ocean environment caused by waves.

• Journal of Bio-Environment Control
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• v.23 no.3
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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.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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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 $6.1\%-18.8\%$.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.6
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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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• v.23 no.4
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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.

• Korean Journal Plant Pathology
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• v.14 no.2
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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.

• Journal of Korea Multimedia Society
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• v.24 no.10
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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.