• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.16-27
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• 2020

This study presented a plan for the establishment of conservation areas and the ecological management of those areas in the stream based on the Marxan with Zones Program for a Jungrangcheon Stream in downtown Seoul. The application of the Marxan with Zones Program included the stage of planning unit setting, application of mapping indices, numerical correction for repetitive analysis, creation of scenario-specific optimizations through analysis, analysis of sensitivity by scenario, review, and the selection of optimal plans among the scenarios considered. As a result of the establishment of a conservation area near Jungrangcheon Stream, which has several watershed areas, including an upper-middle-class wildlife protection zone, which was previously designated and managed as a conservation area, and the migratory protection zone downstream of Jungrangcheon Stream were designated as key conservation areas. A number of wild birds were observed in the upper reaches of Jungrangcheon Stream, adjacent to the forests of Suraksan Mountain and Dobongsan Mountain. The downstream area is a habitat for migratory birds that travel along the stream and the adjacent river ecosystem, including the Hangang River confluence and Cheonggyecheon Stream confluence. Therefore, the upper and lower reaches of Jungrangcheon Stream are connected to forest ecosystems such as Dobongsan Mountain, Suraksan Mountain, and Eungbongsan Mountain, as well as urban green area and river ecosystems in the basin area, which influence the establishment of conservation areas. This study verified the establishment and evaluation of existing conservation areas through the Marxan with Zones Program during the verification of the conservation areas and was presented as in-stream management and basin management method to manage the basin areas derived from core conservation areas determined through the program.

• Fisheries and Aquatic Sciences
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• v.22 no.12
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• pp.31.1-31.12
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• 2019

Background: The black-chinned tilapia, Sarotherodon melanotheron, is the most abundant fish species in the Nakwa (an open lagoon) and Brenu (a closed lagoon) in the Central Region of Ghana. Aspects of the life history characteristics and the ecology of the fish populations in both lagoons were studied to assess the bio-ecological status of this important resource. Methods: Fish samples were obtained from fishermen that fish on the Nakwa and Brenu lagoons using cast, drag and gill nets. The age of the fish was assessed from otoliths analysis and its growth modelled following the von Bertalanffy growth function. Morphometric characteristics of the fish populations were analysed using power regression and ANOVA for parameters comparisons, and Student's t test to determine whether species grew isometrically. The percentage occurrence method was used to analyse the stomach contents of the fish. Results: A total of 382 fish samples from both lagoons were measured, comprising 209 from Nakwa lagoon and 176 from Brenu lagoon. The size and weight of fish samples ranged between 3.9-11.5 cm total length and 1.0-27.3 g for Nakwa Lagoon and 5.6-12.8 cm total length and 3.2-29.8 g for the Brenu Lagoon. The estimated von Bertalanffy growth parameters were L∞ = 12.04 cm and K = 2.76/year for the Nakwa Lagoon samples and L∞ = 13.44 cm and K = 3.27/year for Brenu Lagoon samples. Daily otolith incremental rate ranged from 0.01-0.03 mm per day to 0.01-0.02 mm per day for Nakwa and Brenu lagoons, respectively. Stomach content analysis of the fish samples revealed that the species are planktivorous and the range of food varied between the lagoons. Green algae were the most prevalent food item in the stomachs of the fish samples from Nakwa with the frequency of 69% whilst diatoms (80.5%) were most prevalent phytoplanktonic food item for the fish in Brenu lagoon. Conclusions: The estimates of asymptotic length for the species in both lagoons are close to known values of the species length at first sexual maturity and points to intensive fishing pressure. As a consequence, a comprehensive sample-based survey is required in both lagoons to derive estimates of management reference points. The results of the stomach content analysis are beneficial to the construction of diet matrix for ecosystem models of the two systems.

• Journal of Nutrition and Health
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• v.41 no.5
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• pp.428-438
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• 2008

Choline is important for normal membrane function, acetylcholine synthesis and methyl group metabolism. In this study, 185 food items customarily eaten by Koreans were selected from the data of the 2001 Korean National Health and Nutrition Survey and analyzed on the total choline content of the foods using enzymatic method of choline oxidase. Foods with high choline concentration (mg/100 g) were listed in sequence of quail egg (476.04 mg), dried squid (452.42 mg), beef liver (427.16 mg), pork liver (424.92 mg), tuna canned in oil (414.44 mg), boiled and dried anchovy (381.30 mg), dried Alaskan pollack (378.88 mg), chicken egg (309.88 mg), chicken liver (259.38 mg), soybean (238.62 mg), French bread with garlic (193.18 mg) and barley (183.73 mg). From this result, it is shown that dried fishes, prepared fishes, livers, eggs, pulses and cereals might be categorized as high choline food. Citron tea and green tea showed low choline content below 1 mg. Vegetables and fruits were also categorized into low choline food. No choline was detected in red pepper powder, beer, soju, soybean oil and corn oil out of foods analyzed in this study. Further study is required for analytic procedure of the foods of which results are inconsistent with USDA's data such as rice and wheat flour.

• Clean Technology
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• v.18 no.2
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• pp.162-169
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• 2012

Pt-Sn/${\theta}-Al_2O_3$ catalyst for n-butane dehydrogenation reaction was prepared by incipient wetness method. To confirm the physicochemical properties of Pt-Sn/${\theta}-Al_2O_3$ catalyst, the characterization was performed using X-ray diffraction (XRD), $N_2$ sorption analysis, temperature programmed desorption of $NH_3$ ($NH_3$-TPD), temperature programmed reduction of $H_2$ ($H_2$-TPR) techniques. Also, the catalytic activities of Pt-Sn/${\theta}-Al_2O_3$ for n-butane dehydrogenation was tested as a function of pretreatment temperature, pretreatment time, reaction temperature, and the partial pressure of n-butane and hydrogen. The sum of selectivities to n-butenes consisting of 1-butene, cis-2-butene, and trans-2-butene was almost constant 95% in the range of conversion of n-butane 5-55%. The activation energy calculated from Arrhenius equation was $82.4kJ\;mol^{-1}$ and the reaction orders of n-butane and hydrogen from Power's law were 0.70 and -0.20, respectively.

• Journal of Korean Ophthalmic Optics Society
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• v.20 no.2
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• pp.219-228
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• 2015

Purpose: The current study was performed to compare the difference in binocular visual function depending on variable background colors at near work. Method: Fifty four adults (18 males, 34 females) who consented to the present study and had no ocular disease, ocular surgery history, strabismus and amblyopia with normal binocular vision were participated into this study. The subjects were asked to read the novels with black letter printed on white, red, green and blue background for 15 min. Then, their heterophoria, AC/A ratio, near point of convergence, accommodation facility, relative accommodation and vergence were measured before and after reading. The difference of measurements were compared. Result: Overall heterophoria was tended to decrease with regardless of background color. AC/A ratio showed a tendency of increase after reading the novels with all backgrounds except white background. Near point of convergence was significantly increased compared to before reading at all background color. Accommodative facility of dominant and non-dominant eyes were also significantly increased after reading however, binocular accommodative facility showed a tendency of decrease. Negative relative accommodation also decreased at all background colors however, the change of positive relative accommodation was not significantly different. In case of vergence, there was significant difference in break point of far BO and recovery point of far BI by the wavelength of background color. Conclusions: From the results, it was known there is convergence change depending on the wavelength of light even though same amount of accommodation and convergence is required when doing near work for certain period. Thus, it can be suggested that the adjustment of the near working environment which perception of various color was required, should be conducted according to the main wavelength.

• Journal of the Korean Geotechnical Society
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• v.28 no.12
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• pp.5-15
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• 2012

In this study, a series of CFD (Computational Fluid Dynamics) numerical analyses were performed in order to evaluate the thermal performance of six full-scale closed-loop vertical ground heat exchangers constructed in a test bed located in Wonju. The circulation HDPE pipe, borehole and surrounding ground formation were modeled using FLUENT, a finite-volume method (FVM) program, for analyzing the heat transfer process of the system. Two user-defined functions (UDFs) accounting for the difference in the temperatures of the circulating inflow and outflow fluid and the variation of the surrounding ground temperature with depth were adopted in the FLUENT model. The relevant thermal properties of materials measured in laboratory were used in the numerical analyses to compare the thermal efficiency of various types of the heat exchangers installed in the test bed. The simulation results provide a verification for the in-situ thermal response test (TRT) data. The CFD numerical back-analysis with the ground thermal conductivity of 4 W/mK yielded better agreement with the in-situ thermal response tests than with the ground thermal conductivity of 3 W/mK.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.823-827
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• 2010

This study was conducted to assess the propriety of models for soil water characteristics estimation in anthropogenic soil through the measurement of soil water content and soil water matric potential. Soil profile was characterized with four different soil layers. Soil texture was loamy sand for the first soil layer (from soil surface to 30 cm soil depth), sand for the second (30~70 cm soil depth) and the third soil layers (70~120 cm soil depth), and sandy loam for the fourth soil layer (120 cm < soil depth). Soil water retention curve (SWRC), the relation between soil water content and soil water matric potential, took a similar trend between different layers except the layer of below 120 cm soil depth. The estimation of SWRC and air entry value was better in van Genuchten model by analytical method than in Brooks-Corey model with power function. Therefore, it could be concluded that van Genuchten model is more desirable than Brook-Corey model for estimating soil water characteristics of anthropogenic soil accumulated with saprolite.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1B
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• pp.89-100
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• 2010

The performance evaluation of a conventional Wave Resonator at the entrance of harbors against solitary wave has been performed using 3D numerical wave flume. A wave resonator has been designed for the attenuation of the transmitted wave energy by trapping the short periodic incident waves only. In this study, however, the controlled performance of the wave resonator by its various widths has been numerically investigated for solitary waves. Source distribution method based on the Green function and the 3D one-field Model for immiscible TWO-Phase flows (TWOPM-3D) using 3D numerical wave flume were used for the short-periodic waves and the solitary waves, respectively, and these models were verified through the comparisons with the previous experimental and numerical results by other researchers. It was confirmed that the wave resonator is effective enough to control the solitary waves as well as the periodic waves when it compares with the case of no resonance system. Further, it was found that there is the optimal width of a wave resonator to attenuate the target solitary waves.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.