• Horticultural Science & Technology
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• v.33 no.1
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• pp.39-46
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• 2015

Objective of this research was to investigate the influence of compositions and concentrations of fertilizer solutions on the vegetative growth and nutrient uptake of 'Seolhyang' strawberry. To achieve this, the solutions of acid fertilizer (AF), neutral fertilizer (NF), and basic fertilizer (BF) were prepared at concentrations of 100 or $200mg{\cdot}L^{-1}$ based on N and applied during the 100 days after transplanting. The changes in chemical properties of the soil solution were analysed every two weeks, and crop growth measurements as well as tissue analyses for mineral contents were conducted 100 days after fertilization. The growth was the highest in the treatments with BF, followed by those with NF and AF. The heaviest fresh and dry weights among treatments were 151.3 and 37.8 g, respectively, with BF $200mg{\cdot}L^{-1}$. In terms of tissue nutrient contents, the highest N, P and Na contents, of 3.08, 0.54, and 0.10%, respectively, were observed in the treatment with NF $200mg{\cdot}L^{-1}$. The highest K content was 2.83%, in the treatment with AF $200mg{\cdot}L^{-1}$, while the highest Ca and Mg were 0.98 and 0.42%, respectively, in BF $100mg{\cdot}L^{-1}$. The AF treatments had higher tissue Fe, Mn, Zn, and Cu contents compared to those of NF or BF when fertilizer concentrations were controlled to equal. During the 100 days after fertilization, the highest and lowest pH in soil solution of root media among all treatments tested were 6.67 in BF $100mg{\cdot}L^{-1}$ and 4.69 in AF $200mg{\cdot}L^{-1}$, respectively. The highest and lowest ECs were $5.132dS{\cdot}m^{-1}$ in BF $200mg{\cdot}L^{-1}$ and $1.448dS{\cdot}m^{-1}$ in BF $100mg{\cdot}L^{-1}$, respectively. For the concentrations of macronutrients in the soil solution of root media, the AF $200mg{\cdot}L^{-1}$ treatment gave the highest $NH_4$ concentrations followed by NF $200mg{\cdot}L^{-1}$ and AF $100mg{\cdot}L^{-1}$. The K concentrations in all treatments rose gradually after day 42 in all treatments. When fertilizer concentrations were controlled to equal, the highest Ca and Mg concentrations were observed in AF followed by NF and BF until day 84 in fertilization. The BF treatments produced the highest $NO_3$ concentrations, followed by NF and AF. The trends in the change of $PO_4$ concentration were similar in all treatments. The $SO_4$ concentrations were higher in treatments with AF than those with NF or BF until day 70 in fertilization. These results indicate that compositions of fertilizer solution should to be modified to contain more alkali nutrients when 'Seolhyang' strawberry is cultivated through inert media and nutri-culture systems.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.3
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• pp.25-34
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• 2020

This study examined the definition and major values, the designated procedures and types, and the designation trend in Korean scenic sites and national parks in the United States. Based on this, the analysis of the characteristics of the designation of the two natural heritages. The results are as follows; First, Scenic Sites has characteristics of complex heritage that includes academic, historical, and humanities values on the basis of landscape. As a natural heritage based on public nature, the U.S. National Park aims to contribute to the people's natural heritage and satisfy both ecological and historical values through the protection of the landscape. Second, the designation of a scenic sites are decided through deliberation by the Cultural Heritage Committee after the request of the owner, manager, or local government or by the authority of the head of the Cultural Heritage Administration. The designated survey is divided into basic resource surveys and resource surveys by type. Since the initial designation of the Sogeumgang Mountain in Cheonghakdong, Myeongju in 1970, the number of designated scenic sites was low until the 2000s, but the number of designated scenic sites has increased rapidly since 2006 due to the policy to promote the scenic site, and the proportion of natural and historical and cultural scenic sites has been balanced. The designation of the U.S. national park is decided by the Congress or the president, and the National Park Service makes a series of decisions on whether to conduct a special resource study of provisional resources through a preliminary inspection survey, whether to satisfy the criteria for designation of national parks based on the results of special resource research, and to prioritize them. The U.S. National Parks have been expanded not only by Congress but also by the president's empowerment to designate them as national monuments. With the integrated operation of the National Park Service, the number of designated cases increased as the national park included the heritage sites under the control of various ministries. In addition, a number of historical areas were designated by the enactment of the Historical Site Act, and recreational areas were designated to provide leisure space and classified and managed in a total of 18 units. Third, the comparison of the designation characteristics of the two heritage properties confirmed that the designation of natural heritage with complex value, the classification of types according to complementary designation system and resource characteristics, the establishment of the competent ministry and the balancing of the heritage according to the designation policy. The two heritages had the characteristics of complex natural heritages that met ecological, historical and academic values at the same time based on landscape and public nature. In addition, both countries have identified a system for deliberating the designation of heritage through a basic resource survey and an in-depth designation survey, and classified each type according to the characteristics of the resource. In addition, the policies for promoting scenic sites in Korea and the integrated operation of the National Park Service in the U.S. influenced the designated aspects of the two heritage sites, balancing natural heritage with historical and cultural heritage. Fourth, the resource types and conservation management methods of Scenic site and National Park were largely related. The natural areas of the U.S. National Park include types of natural monuments in Korea as major resources, and have characteristics similar to natural scenic sites. In addition, historical resources were similar to the criteria for designation of historical and cultural scenic sites in terms of landscape, and the aspects of war and celebrity-related relics were related to the types of historic sites. In terms of conservation management, the natural area of the U.S. national park has a way of keeping the original ecosystem intact, but the Korean natural heritage protection system is likely to be useful for focusing on the resource of viscosity. Meanwhile, historical resources include historical sites and historical and cultural scenic sites in the traditional era, but historical relics in the U.S. National Parks have set a time limit to modern times for war history and celebrity-related relics, and the active provision of entertainment programs based on existing resources was derived as a difference.

• Korean Journal of Heritage: History & Science
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• v.39
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• pp.351-378
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• 2006

With the development of media, the methods for the documentation of intangible cultural heritage have been also developed and diversified. As well as the previous analogue ways of documentation, the have been recently applying new multi-media technologies focusing on digital pictures, sound sources, movies, etc. Among the new technologies, the documentation of intangible cultural heritage using the method of 'Motion Capture' has proved itself prominent especially in the fields that require three-dimensional documentation such as dances and performances. Motion Capture refers to the documentation technology which records the signals of the time varing positions derived from the sensors equipped on the surface of an object. It converts the signals from the sensors into digital data which can be plotted as points on the virtual coordinates of the computer and records the movement of the points during a certain period of time, as the object moves. It produces scientific data for the preservation of intangible cultural heritage, by displaying digital data which represents the virtual motion of a holder of an intangible cultural heritage. National Research Institute of Cultural Properties (NRICP) has been working on for the development of new documentation method for the Important Intangible Cultural Heritage designated by Korean government. This is to be done using 'motion capture' equipments which are also widely used for the computer graphics in movie or game industries. This project is designed to apply the motion capture technology for 3 years- from 2005 to 2007 - for 11 performances from 7 traditional dances of which body gestures have considerable values among the Important Intangible Cultural Heritage performances. This is to be supported by lottery funds. In 2005, the first year of the project, accumulated were data of single dances, such as Seungmu (monk's dance), Salpuri(a solo dance for spiritual cleansing dance), Taepyeongmu (dance of peace), which are relatively easy in terms of performing skills. In 2006, group dances, such as Jinju Geommu (Jinju sword dance), Seungjeonmu (dance for victory), Cheoyongmu (dance of Lord Cheoyong), etc., will be documented. In the last year of the project, 2007, education programme for comparative studies, analysis and transmission of intangible cultural heritage and three-dimensional contents for public service will be devised, based on the accumulated data, as well as the documentation of Hakyeonhwadae Habseolmu (crane dance combined with the lotus blossom dance). By describing the processes and results of motion capture documentation of Salpuri dance (Lee Mae-bang), Taepyeongmu (Kang seon-young) and Seungmu (Lee Mae-bang, Lee Ae-ju and Jung Jae-man) conducted in 2005, this report introduces a new approach for the documentation of intangible cultural heritage. During the first year of the project, two questions have been raised. First, how can we capture motions of a holder (dancer) without cutoffs during quite a long performance? After many times of tests, the motion capture system proved itself stable with continuous results. Second, how can we reproduce the accurate motion without the re-targeting process? The project re-created the most accurate motion of the dancer's gestures, applying the new technology to drew out the shape of the dancers's body digital data before the motion capture process for the first time in Korea. The accurate three-dimensional body models for four holders obtained by the body scanning enhanced the accuracy of the motion capture of the dance.

• 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.

• Trans-
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• v.12
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• pp.51-79
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• 2022

This study investigates the methods for deriving colors which can serve as a reference to users such as designers and or contents creators who search for online images from the web portal sites using specific words for color planning and more. Two experiments were conducted in order to accomplish this. Digital scenery photos within the geographic scope of Korea were downloaded from web portal sites, and those photos were studied to find out what colors were used to describe daytime and nighttime. Machine learning was used as the study methodology to classify colors in daytime and nighttime, and KSCA was used to derive the color frequency of daytime and nighttime photos and to compare and analyze the two results. The results of classifying the colors of daytime and nighttime photos using machine learning show that, when classifying the colors by 51~100%, the area of daytime colors was approximately 2.45 times greater than that of nighttime colors. The colors of the daytime class were distributed by brightness with white as its center, while that of the nighttime class was distributed with black as its center. Colors that accounted for over 70% of the daytime class were 647, those over 70% of the nighttime class were 252, and the rest (31-69%) were 101. The number of colors in the middle area was low, while other colors were classified relatively clearly into day and night. The resulting color distributions in the daytime and nighttime classes were able to provide the borderline color values of the two classes that are classified by brightness. As a result of analyzing the frequency of digital photos using KSCA, colors around yellow were expressed in generally bright daytime photos, while colors around blue value were expressed in dark night photos. For frequency of daytime photos, colors on the upper 40% had low chroma, almost being achromatic. Also, colors that are close to white and black showed the highest frequency, indicating a large difference in brightness. Meanwhile, for colors with frequency from top 5 to 10, yellow green was expressed darkly, and navy blue was expressed brightly, partially composing a complex harmony. When examining the color band, various colors, brightness, and chroma including light blue, achromatic colors, and warm colors were shown, failing to compose a generally harmonious arrangement of colors. For the frequency of nighttime photos, colors in approximately the upper 50% are dark colors with a brightness value of 2 (Munsell signal). In comparison, the brightness of middle frequency (50-80%) is relatively higher (brightness values of 3-4), and the brightness difference of various colors was large in the lower 20%. Colors that are not cool colors could be found intermittently in the lower 8% of frequency. When examining the color band, there was a general harmonious arrangement of colors centered on navy blue. As the results of conducting the experiment using two methods in this study, machine learning could classify colors into two or more classes, and could evaluate how close an image was with certain colors to a certain class. This method cannot be used if an image cannot be classified into a certain class. The result of such color distribution would serve as a reference when determining how close a certain color is to one of the two classes when the color is used as a dominant color in the base or background color of a certain design. Also, when dividing the analyzed images into several classes, even colors that have not been used in the analyzed image can be determined to find out how close they are to a certain class according to the color distribution properties of each class. Nevertheless, the results cannot be used to find out whether a specific color was used in the class and by how much it was used. To investigate such an issue, frequency analysis was conducted using KSCA. The color frequency could be measured within the range of images used in the experiment. The resulting values of color distribution and frequency from this study would serve as references for color planning of digital design regarding natural scenery in the geographic scope of Korea. Also, the two experiments are meaningful attempts for searching the methods for deriving colors that can be a useful reference among numerous images for content creator users of the relevant field.

• Economic and Environmental Geology
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• v.56 no.2
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• pp.125-138
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• 2023

Most of previous cesium (Cs) sorbents have limitations on the treatment in the large-scale water system having low Cs concentration and high ion strength. In this study, the new Cs sorbent that is eco-friendly and has a high Cs removal efficiency was developed by improving the coal mine drainage treated sludge (hereafter 'CMDS') with the addition of Na and S. The sludge produced through the treatment process for the mine drainage originating from the abandoned coal mine was used as the primary material for developing the new Cs sorbent because of its high Ca and Fe contents. The CMDS was improved by adding Na and S during the heat treatment process (hereafter 'Na-S-CMDS' for the developed sorbent in this study). Laboratory experiments and the sorption model studies were performed to evaluate the Cs sorption capacity and to understand the Cs sorption mechanisms of the Na-S-CMDS. The physicochemical and mineralogical properties of the Na-S-CMDS were also investigated through various analyses, such as XRF, XRD, SEM/EDS, XPS, etc. From results of batch sorption experiments, the Na-S-CMDS showed the fast sorption rate (in equilibrium within few hours) and the very high Cs removal efficiency (> 90.0%) even at the low Cs concentration in solution (< 0.5 mg/L). The experimental results were well fitted to the Langmuir isotherm model, suggesting the mostly monolayer coverage sorption of the Cs on the Na-S-CMDS. The Cs sorption kinetic model studies supported that the Cs sorption tendency of the Na-S-CMDS was similar to the pseudo-second-order model curve and more complicated chemical sorption process could occur rather than the simple physical adsorption. Results of XRF and XRD analyses for the Na-S-CMDS after the Cs sorption showed that the Na content clearly decreased in the Na-S-CMDS and the erdite (NaFeS₂·2(H₂O)) was disappeared, suggesting that the active ion exchange between Na⁺ and Cs⁺ occurred on the Na-S-CMDS during the Cs sorption process. From results of the XPS analysis, the strong interaction between Cs and S in Na-S-CMDS was investigated and the high Cs sorption capacity was resulted from the binding between Cs and S (or S-complex). Results from this study supported that the Na-S-CMDS has an outstanding potential to remove the Cs from radioactive contaminated water systems such as seawater and groundwater, which have high ion strength but low Cs concentration.

• Journal of Distribution Research
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• v.15 no.2
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• pp.1-34
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• 2010

Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as