• Atmosphere
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• v.22 no.2
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• pp.233-243
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• 2012

The Barnes scheme is used in Digital Forecast System (DFS) of the Korea Meteorological Administration (KMA) for real-time analysis. This scheme is an objective analysis scheme with a distance-dependent weighted average. It has been widely used for mesoscale analyses in limited geographic areas. The isotropic Gaussian weight function with a constant effective radius might not be suitable for certain conditions. In particular, the analysis error can be increased for stations located near mountains. The terrain of South Korea is covered with mountains and wide plains that are between successive mountain ranges. Thus, it is needed to consider the terrain effect with the information of elevations for each station. In order to improve the accuracy of the temperature objective analysis, we modified the weight function which is dependent on a distance and elevation in the Barnes scheme. We compared the results from the Barnes scheme used in the DFS (referred to CTL) with the new scheme (referred to EXP) during a year of 2009 in this study. The analysis error of the temperature field was verified by the root-mean-square-error (RMSE), mean error (ME), and Priestley skill score (PSS) at the DFS observation stations which is not used in objective analysis. The verification result shows that the RMSE and ME values are 1.68 and -0.41 in CTL and 1.42 and -0.16 in EXP, respectively. In aspect of spatial verification, we found that the RSME and ME values of EXP decreased in the vicinity of Jirisan (Mt. Jiri) and Taebaek Mountains. This indicates that the new scheme performed better in temperature verification during the year 2009 than the previous scheme.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.55-67
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• 2018

The objectives of this study were to monitor organic farming upland compared with conventional upland field and to evaluate nutrient loads reduction of surface cover effect with long-term historical climate data. APEX(Agricultural Policy Environmental eXtender) model was validated with experimental data and used for assessing surface cover scenarios for 30-year simulation periods. The validated values of RMSE(Root Mean Square Error), RMAE(Root Mean Absolute Error), $R^2$ and E(Nash-Sutcliffe efficiency) for runoff were 1.17-1.37 mm/day, 0.28-0.45 mm/day, 0.88-0.90 and 0.82-0.94 in two treatments, respectively. Those for water quality (nitrogen) were 0.05-0.16 kg/ha, 0.52-0.75 kg/ha, 0.67-0.72 and 0.32-0.70 in two treatments, respectively, and therefore the validated model showed good agreement with the observed runoff and nitrogen load for the study period. When decreasing the surface cover rate of organic farming field to 75%, 50%, 25%, and 0% (conventional field), average annual runoff increased by 7%, 15%, 23% and 31%, respectively. Under same condition of decreasing the surface cover rate, average annual nitrogen loads increased by 1.4 times, 1.7 times, 2.0 times, and 2.3 times compared with organic farming field, respectively. This study showed that it is possible to present an appropriate surface cover ratio to maintain conventional production and minimize nonpoint sources pollution for organic farming system, although long-term monitoring is needed to determine its effects on environmental concerns, crop competition, and other uncertainty.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.5
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• pp.616-626
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• 2012

The purpose of this study is to verify career maturity, achievement goal, learning attitude and academic achievement relation with subjects-related career education of middle school students. To achieve these aims, this study used SELS(Seoul education longitudinal study) of Seoul Education Research & Information Institute. Also, analysis as well as descriptive statistics calculation on average, deviation, skewness and kurtosis of variable factor and calculated characteristic item and degree of reliability(Cronbach ${\alpha}$). For goodness of fit test, this study used TLI(Tucker-Lewis index) and RMSEA(Root mean square error of approximation). To achieve the ultimate objects, this study used LMA(latent mean analysis) for analysis of difference career maturity, achievement goal, learning attitude and academic achievement relation with subjects-related career education in middle school students. The results are as follows. First, experience relation with subjects-related career education were influenced on career maturity with career cognition. Second, experience relation with subjects-related career education were influenced on achievement goal, learning attitude, and larger than career maturity and academic achievement. Third, experience relation with subjects-related career education were influenced on middle school students more than inexperienced relation with subjects-related career education.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.81-90
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• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.309-318
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• 1999

In order to fast predict the wind-driven current in a small bay, a convolution method in which the wind-driven current can be generated only wih the local wind is developed and applied in the Sachon Bay. The root mean square(rms) ratio defined as the ratio of the rms error to the rms speed is 0.37. The rms ratio is generally less than 0.2, except for all the mouths of Junju Bay and Namhae-do and in the region between Saryang Island and Sachon. The spatial average of the recover rate of kinetic energy(rrke) is 87%. Thus, the predicted wind-driven current by the convolution model is in a good agreement with the computed one by the numerical model. The raio of the difference between observed residual current (Vr) and predicted wind-driven current (Vc) to a residual current, that is, (Vr-Vc)/Vr shows 56%, 62% at 2 moorings in the Sachon Bay.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.597-599
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• 1999

Triply encoded HTIS(hadamard transform imaging spectrometer) is a system which applies the grill spectrometer to the HTIS. we consider a nonideality of mask transparent characteristic in estimating spectrum. Triply encoded system increases the SNR(signal to noise ration) by multiplexing effect. In this paper, we suggest an advanced $T^{-1}$ method for spectrum recovery. Then, we proved the superiority of the suggested method by comparing the average MSE(mean square error) of the other recovery methods.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.1017-1019
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• 1999

In this paper we present 2-dimensional image recovery method using Hadamard transform. Generally, the methods of Hadamard transform are more useful tools and much simplier than those of Fourier transform. The Hadamard transform can improve estimates when the detector is the source of noise. We take into account nonidealities in the system for the further improved image We also present the average mean square error(AMSE) associated with estimates with the results from computer simulations.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.601-603
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• 1998

In this paper, Hadamard transform imaging spectrometers utilizing Grill spectrometers are proposed. General Hadamard Transform Spectrometers (HTS) carry out one-encoding through input masks, but Grill spectrometers carry out double-encoding through entrance and exit masks. Thus Grill spectrometers increase the signal-to-noise ratio by double-encoding. we reconfigure the system by using the Grill spectrometers which use a left cyclic S-matrix instead of the conventional right cyclic one. Then, we model the system and apply the mask characteristics method, i.e. $T^{I}$ method, to complete fast algorithm. Through computer simulations, we want to prove the superiority of the proposed system by comparing with the conventional HTS. From Observations concerning the average mean square error(AMSE) associated with estimates from the $T^{I}$ spectrum-recovery method, the relative performances of the two systems are compared.