• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.97.1-97.1
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• 2010

유연금속기판위에 DC 마그네트론 스퍼터링을 이용하여 Ag/ZnO 이중구조의 후면반사막을 증착하고 Ag 표면조도 변화에 따른 후면반사막의 반사특성 변화와 플렉서블 비정질 실리콘 박막 태양전지의 셀 특성에 미치는 영향을 조사하였다. Substrate구조를 갖는 플렉서블 실리콘 박막 태양전지에서는 실리콘 박막 광흡수층의 상대적으로 낮은 광 흡수율로 인하여 입사광에 대한 태양전지 내에서의 광 산란 및 포획이 태양전지 효율을 증대시키는데 매우 중요한 역할을 하는 것으로 알려져 있다. 플렉서블 실리콘 박막 태양전지에서의 후면반사막은 광 흡수층에서 흡수되지 않는 입사광을 다시 반사시켜 광 흡수를 증대시키며 이때 후면반사막 표면에서 반사 빛을 효율적으로 산란시켜 이동경로를 증대시킴으로써 광 흡수율을 더욱 향상시킬 수 있다. 본 연구에서는 유연금속 기판위에 Ag와 ZnO:Al($Al_2O_3$ 2.5wt%) 타겟을 사용한 DC 마그네트론 스퍼터링법으로 Ag/AZO 이중구조의 후면반사막을 제조하고, Ag 박막의 표면형상 변화와 이에 따른 후면반사막의 반사도 변화를 비교, 분석하였다. 증착 조건 변화에 따른 표면 형상 및 반사 특성은 Atomic Force Mircroscope(AFM), Scanning electron miroscopy(SEM), UV-visible-nIR spectrometry를 통하여 분석하였다. 서로 다른 표면 거칠기를 갖는 후면반사막 위에 n-i-p구조의 a-Si:H 실리콘 박막 태양전지를 제조한 후 태양전지 동작 특성에 미치는 영향을 조사하였다. n,p층은 13.56MHz PECVD, i층은 60MHz VHF CVD를 사용하여 각각 제조 하였으며, Photo I-V, External Quantum Efficiency(EQE) 분석을 통하여 태양전지 특성을 조사 하였다. SEM 분석결과 공정 온도가 증가 할수록 Ag 박막의 표면 결정립 크기도 증가하였으며, AFM분석을 통한 Root-mean-square(Rms)값은 상온에서 $500^{\circ}C$로 증착온도가 증가함에 따라 6.62nm에서 46.64nm까지 증가하였다. Ag 박막의 표면 거칠기 증가에 따라 후면반 사막의 확산 반사도도 함께 증가하였다. 공정온도 $500^{\circ}C$에서 증착된 후면반사막을 사용하여 a-Si:H 태양전지를 제조하였을 때 상온에서 제조한 후면반사막에 비하여 단락전류밀도 (Jsc)값은 9.94mA/$cm^2$에서 13.36mA/$cm^2$로 증가하였으며, 7.6%의 가장 높은 태양전지 효율을 나타내었다.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.9
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• pp.1237-1247
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• 2012

The objective of this study was to evaluate the predictions of dry matter intake (DMI) and average daily gain (ADG) of Vietnamese Yellow (Vang) purebred and crossbred (Vang with Red Sindhi or Brahman) bulls fed under Vietnamese conditions using two levels of solution (1 and 2) of the large ruminant nutrition system (LRNS) model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW) of the animals was 186, with standard deviation ${\pm}33.2$ kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP), accuracy (Cb), coefficient of determination ($r^2$), and mean bias (MB). When all treatment means were used, both levels of solution predicted DMI similarly with low precision ($r^2$ of 0.389 and 0.45 for level 1 and 2, respectively) and medium accuracy (Cb of 0.827 and 0.859, respectively). The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively), the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively). The accuracy (Cb) was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively). The RMSEP indicated that both levels were on average under-or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb) was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known.

• Clean Technology
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• v.25 no.4
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• pp.324-330
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• 2019

The selective catalytic reduction system is a highly effective technique for the denitrification of the flue gases emitted from the industrial facilities. The distribution of mixing ratio between ammonia and nitrogen oxide at the inlet of the catalyst layers is important to the efficiency of the de-NOx process. In this study, computational analysis tools have been applied to improve the uniformity of NH₃/NO molar ratio by controlling the flow rate of the ammonia injection nozzles according to the distribution pattern of the nitrogen oxide in the inlet flue gas. The root mean square of NH₃/NO molar ratio was chosen as the optimization parameter while the design of experiment was used as the base of the optimization algorithm. As the inlet conditions, four (4) types of flow pattern were simulated; i.e. uniform, parabolic, upper-skewed, and random. The flow rate of the eight nozzles installed in the ammonia injection grid was adjusted to the inlet conditions. In order to solve the two-dimensional, steady, incompressible, and viscous flow fields, the commercial software ANSYS-FLUENT was used with the k-𝜖 turbulence model. The results showed that the improvement of the uniformity ranged between 9.58% and 80.0% according to the inlet flow pattern of the flue gas.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.4
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• pp.365-369
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• 2016

Red sorrel (Rumex acetosella L.), as one of exotic weeds in Korea, was dominated in grassland and reduced the quality of forage. Improving current pasture productivity by precision management requires practical tools to collect site-specific pasture weed data. Recent development in unmanned aerial vehicle (UAV) technology has offered cost effective and real time applications for site-specific data collection. To map red sorrel on a hill pasture, we tested the potential use of an UAV system with digital cameras (visible and near-infrared (NIR) camera). Field measurements were conducted on grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17, 2014. Plant samples were obtained at 20 sites. An UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number values of Red, Green, Blue, and NIR channels were extracted from aerial photos. Multiple linear regression analysis results showed that the correlation coefficient between Rumex content and 4 bands of UAV image was 0.96 with root mean square error of 9.3. Therefore, UAV monitoring system can be a quick and cost effective tool to obtain spatial distribution of red sorrel data for precision management of hilly grazing pasture.

• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.589-597
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• 2019

The purpose of this study was improve accuracy the IOPs inversion model(IOPs-IM) developed in 2016 for phycocyanin(PC) concentration estimation in the Nakdong River. Additionally, two optimum models were developed and evaluated with 2017 measurement field spectral data for the Geum River and the Yeongsan River. The used measurement data for IOPs-IM analyzation was randomly classified as training and verification materials at the ratio of 2:1 in all data sets. Using the training data set from 2015-2017, accuracy results of the IOPs-IM generally improved for the Nakdong River. The RMSE(Root Mean Square Error) decreased by 14 % compared to 2016. For the GeumRiver, the results of the IOPs-IM were suitable, except for some point results in 2016. Results of the IOPs-IM in the Yeongsan River followed the overall 1:1 line and MAE(Mean Absolute Error) was lower than other rivers. But the RMSE and MAE values were higher. As a result of applying the validation data to the IOPs-IM, the accuracy of the Nakdong River was reduced to RMSE 17.7 % and MRE 16.4 %, respectively compared with 2016. However, the MRE(Mean Relative Error) was estimated to be higher by 400 % in the Geum River, and the RMSE was more than 100 mg/㎥ of the Yeongsan River. Therefore, it is necessary to get the continuously data with various sections of each river for obtain objective and reliable results and the models should be improved.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.631-640
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• 2017

The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.647-659
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• 2017

Garlic and onion are grown in major cultivation regions that depend on the crop condition and the meteorology of the production area. Therefore, when yields are to be predicted, it is reasonable to use a statistical model in which both the crop and the meteorological elements are considered. In this paper, using a multiple linear regression model, we predicted garlic and onion yields in major cultivation regions. We used the MODIS NDVI that reflects the crop conditions, and six meteorological elements for 7 major cultivation regions from 2006 to 2015. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, the MODIS NDVI in February was chosen the significant independent variable of the garlic and onion yield prediction model. In the case of meteorological elements, the garlic yield prediction model were the mean temperature (March), the rainfall (November, March), the relative humidity (April), and the duration time of sunshine (April, May). Also, the rainfall (November), the duration time of sunshine (January), the relative humidity (April), and the minimum temperature (June) were chosen among the variables as the significant meteorological elements of the onion yield prediction model. MODIS NDVI and meteorological elements in the model explain 84.4%, 75.9% of the garlic and onion with a root mean square error (RMSE) of 42.57 kg/10a, 340.29 kg/10a. These lead to the result that the characteristics of variations in garlic and onion growth according to MODIS NDVI and other meteorological elements were well reflected in the model.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.67-74
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• 2016

Drone images with high spatial resolution are emerging as an alternative to previous studies with extraction limits in high density forests. Individual tree in the dense forests were extracted from drone images. To detect the individual tree extracted through the image segmentation process, the image segmentation results were compared between the combination of DSM and all R,G,B band and the combination of DSM and R,G,B band separately. The changes in the tree density of a deciduous forest was experimented by time and image. Especially the image of May when the forests are dense, among the images of March, April, May, the individual tree extraction rate based on the trees surveyed on the site was 50%. The analysis results of the width of crown showed that the RMSE was less than 1.5m, which was the best result. For extraction of the experimental area, the two sizes of medium and small trees were extracted, and the extraction accuracy of the small trees was higher. The forest tree volume and forest biomass could be estimated if the tree height is extracted based on the above data and the DBH(diameter at breast height) is estimated using the relational expression between crown width and DBH.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.169-180
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• 2017

Individuals with high trait anxiety try to suppress their anger expression, thus there are limits in measuring their anger using subjective behavioral evaluation. In order to overcome this limitation, this study attempted to identify the difference in the autonomic nervous system responses induced by anger in individuals with high trait anxiety. Participants were divided into two groups, anxiety and control groups. Electrocardiogram (ECG), respiration (RESP), electrodermal activity (EDA), and skin temperature (SKT) were measured while participants were presented with an anger-inducing stimulus. Heart rate (HR), standard deviation of NN interval (SDNN), root mean square of successive difference (RMSSD), low frequency (LF), high frequency (HF), LF/HF ratio, respiration rate (RR), skin conductance level (SCL), and maximum skin temperature (maxSKT) were calculated before and after presenting the stimulus. Anxiety group reported greater anger by the anger-inducing stimulus compared to the control group. Anxiety group also showed significant increase in SDNN and LF, and decrease in HF, LF/HF ratio, and RR. These results suggest that the autonomic nervous system responses may be used as objective indicators of anger experiences in individuals with high trait anxiety.