• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.31 no.3
• /
• pp.25-37
• /
• 2013

The purpose of this study is to draw reasonable management plans to reinforce essence of Ohyundan(五賢壇: Five sprit tablets), a sacred site and monument of Jeju, by investigating and analyzing current status and problems of cultural landscape elements(e.g. architectural structures, installation, letters carved on the rocks, actual vegetation, etc.) while grasping placeness contained in Ohyundan through consideration of its history and transition process of Ohyundan a future being and shrine of Gyulrim Seowon(橘林書院) in Jeju. Results derived from research are summarized as follows. Ohyundan is noted due to its placeness in that it was a place for Gyulrim Seowon, Jeju's one and only Saaek Seowon(賜額書 院) and it was a symbolic space of exile culture in Jeju. As it is inferred from Gyulrim Seowon, which is dangho(堂號: clan name) of Seowon, orchards surrounding all over places are a signature landscape element that shows placeness of the past Ohyundan. Joduseok(俎豆石: altar stone), representing a core installation of Ohyundan and ancestral tablet of five spirits, created a refined place by putting up common stones around altar and founding blocked stones to wall. This refinement and thrift served basic mind of Neo-Confucianism, and led to of Jeju's Jonyang mind(spend-thrift mind). In conclusion, a practice plan is a prerequisite to restore essence of Ohyundan by actively excluding installations not suitable for placeness or overly designed such as Jeju Hyangrodang(a center for the elderly) and numerous monument houses. On the other hand, together with Joduseok, as letters carved on the rocks such as 'Jeungjoo Byukrip(曾朱壁立)' and 'Gwangpoongdae(光風臺)' and Yoocheonseok serve as a signature landscape that well shows mind of five spirits and teaching of Neo-Confucianism, and also a trace from a confucian viewpoint deeply rooted in Jeju, they are judged as a cultural landscape corresponding to the essence of place in Ohyundan which requires proactive preservation and plans for public relations. Together with this, although many different old big trees such as Pinus densiflora , Pinus thunbergii, Quercus variabilis, Celtis sinenis, Zelkova serrata and Rhus succedanea are a landscape element that increases sacred Ohyundan and commemorative value, now required is thorough entity tree management by assigning serial number on them as many of them were dead or removed resulting from transition process of land use. Further, to reinforce quality of site location belonging to Gyulrim Seowon, a prerequsite is to review plans that create Gyulrim at reinstalled site of building and raw land.

• Journal of Global Scholars of Marketing Science
• /
• v.18 no.3
• /
• pp.27-57
• /
• 2008

Many consumers enjoy the challenge of purchasing a brand that matches well with their own values and personalities (for example, Ko et al., 2008; Ko et al., 2006). Therefore, the personalities of consumers can impact on the final selection of a brand and its brand personality in two ways: first, the consumers may incline to purchase a brand or a product that reflects their own personalities; second, consumers tend to choose a company that has similar brand personalities to those brands that are being promoted. Therefore, the objectives of this study are following: 1. Is there any empirical relationship between a consumer's personality and the personality of a brand that he or she chooses? 2. Can a corporate brand be differentiated by the brand personality? In short, consumers are more likely to hold favorable attitudes towards those brands that match their own personality and will most probably purchase those brands matching well with their personality. For example, Matzler et al. (2006) found that extraversion and openness were positively related to hedonic product value; and that the personality traits directly (openness) and indirectly (extraversion, via hedonic value) influenced brand effects, which in turn droved attitudinal and purchase loyalty. Based on the above discussion, the following hypotheses are proposed: Hypothesis 1: the personality of a consumer is related to the brand personality of a product/corporate that he/she purchases. Kuksov (2007) and Wernerfelt (1990) argued that brands as a symbolic language allowed consumers to communicate their types to each other and postulated that consumers had a certain value of communicating their types to each other. Therefore, how brand meanings are established, and how a firm communicate with consumers about the meanings of the brand are interesting topics for research (for example, Escalas and Bettman, 2005; McCracken, 1989; Moon, 2007). Hence, the following hypothesis is proposed: Hypothesis 2: A corporate brand identity is differentiated by the brand personality. And there are significant differences among companies. A questionnaire was developed for collecting empirical measures of the Big-Five personality traits and brand personality variables. A survey was conducted to the online access panel members through the Internet during December 2007 in Korea. In total, 500 respondents completed the questionnaire, and considered as useable. Personality constructs were measured using the Five-factor Inventory (NEO-FFI) scale and a total of 30 items were actually utilized. Brand personality was measured using the five-dimension scale developed by Aaker (1997). A total of 17 items were actually utilized. The seven-point Likert-type scale was the format of responses, for example, from 1 indicating strongly disagreed to 7 for strongly agreed. The Analysis of Moment Structures (AMOS) was used for an empirical testing of the model, and the Maximum Likelihood Estimation (MLE) was applied to estimate numerical values for the components in the model. To diagnose the presence of distribution problems in the data and to gauge their effects on the parameter estimates, bootstapping method was used. The results of the hypothesis-1 test empirically show that there exit certain causality relationship between a consumer's personality and the brand personality of the consumer's choice. Thus, the consumer's personality has an impact on consumer's final selection of a brand that has a brand personality matches well with their own personalities. In other words, the consumers are inclined to purchase a brand that reflects their own personalities and tend to choose a company that has similar brand personalities to those of the brand being promoted. The results of this study further suggest that certain dimensions of the brand personality cause consumers to have preference to certain (corporate) brands. For example, the conscientiousness, neuroticism, and extraversion of the consumer personality have positively related to a selection of "ruggedness" characteristics of the brand personality. Consumers who possess that personality dimension seek for matching with certain brand personality dimensions. Results of the hypothesis-2 test show that the average "ruggedness" attributes of the brand personality differ significantly among Korean automobile manufacturers. However, the result of ANOVA also indicates that there are no significant differences in the mean values among manufacturers for the "sophistication," "excitement," "competence" and "sincerity" attributes of the corporate brand personality. The tight link between what a firm is and its corporate brand means that there is far less room for marketing communications than there is with products and brands. Consequently, successful corporate brand strategies must position the organization within the boundaries of what is acceptable, while at the same time differentiating the organization from its competitors.

• The Mathematical Education
• /
• v.62 no.3
• /
• pp.381-400
• /
• 2023

As the era of solving various and complex problems in the real world using artificial intelligence and big data appears, problem-solving competencies that can solve realistic problems through a mathematical approach are required. In fact, the 2015 revised mathematics curriculum and the 2022 revised mathematics curriculum emphasize mathematical modeling as an activity and competency to solve real-world problems. However, the real-world problems presented in domestic and international textbooks have a high proportion of artificial problems that rarely occur in real-world. Accordingly, domestic and international countries are paying attention to the reality of mathematical modeling tasks and suggesting the need for authentic tasks that reflect students' daily lives. However, not only did previous studies focus on theoretical proposals for reality, but studies analyzing teachers' perceptions of reality and their competency to reflect reality in the task are insufficient. Accordingly, this study aims to analyze in-service mathematics teachers' perception of reality among the characteristics of tasks for mathematical modeling and the in-service mathematics teachers' competency for designing the mathematical modeling tasks. First of all, five criteria for satisfying the reality were established by analyzing literatures. Afterward, teacher training was conducted under the theme of mathematical modeling. Pre- and post-surveys for 41 in-service mathematics teachers who participated in the teacher training was conducted to confirm changes in perception of reality. The pre- and post- surveys provided a task that did not reflect reality, and in-service mathematics teachers determined whether the task given in surveys reflected reality and selected one reason for the judgment among five criteria for reality. Afterwards, frequency analysis was conducted by coding the results of the survey answered by in-service mathematics teachers in the pre- and post- survey, and frequencies were compared to confirm in-service mathematics teachers' perception changes on reality. In addition, the mathematical modeling tasks designed by in-service teachers were evaluated with the criteria for reality to confirm the teachers' competency for designing mathematical modeling tasks reflecting the reality. As a result, it was shown that in-service mathematics teachers changed from insufficient perception that only considers fragmentary criterion for reality to perceptions that consider all the five criteria of reality. In particular, as a result of analyzing the basis for judgment among in-service mathematics teachers whose judgment on reality was reversed in the pre- and post-survey, changes in the perception of in-service mathematics teachers was confirmed, who did not consider certain criteria as a criterion for reality in the pre-survey, but considered them as a criterion for reality in the post-survey. In addition, as a result of evaluating the tasks designed by in-service mathematics teachers for mathematical modeling, in-service mathematics teachers showed the competency to reflect reality in their tasks. However, among the five criteria for reality, the criterion for "situations that can occur in students' daily lives," "need to solve the task," and "require conclusions in a real-world situation" were relatively less reflected. In addition, it was found that the proportion of teachers with low task development competencies was higher in the teacher group who could not make the right judgment than in the teacher group who could make the right judgment on the reality of the task. Based on the results of these studies, this study provides implications for teacher education to enable mathematics teachers to apply mathematical modeling lesson in their classes.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.83-102
• /
• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.5 no.1
• /
• pp.13-24
• /
• 2003

The purpose of this study is to design Jugori model compatible with the body types of the middle-aged women especially from 40 to 59 years old. The result is as follows: We decided five items as the necessary items for designing jugori model : the bust girth (the breast & shoulder width), the B.P length, the neck width, the armhole circumference, and Hwa-jang. The breast & shoulder width are the size that comes out if the bust is divided by the breast & shoulder width on the basis of the side line, and Hwa-jang is a length measured with arms stretched out to 0° direction. With each person's physical characteristics considered, the application of the size of each body types and body parts is as follows: 1. The breast & shoulder width (1/4 portion) : We decided B/4+2cm as a standard size and, we adjusted the extra room on the basis of the discrepancy between the breast width and the shoulder width to make it fit well to the each body type. For the breast width (1/2 portion), we bisected the difference between the breast width and the shoulder width of the bust, and moved Gut-sup to the center of the Sup and Sup-sun for An-sup. According to the body type, the movement of the Sup for the people with big breasts gets bigger because there should be a big difference between the breast width and the shoulder width for them, and for the people with small breasts the movement will be relatively smaller. For the shoulder width (1/2 portion), we curved the back center line after we shortened as much as the difference between the amount of the shoulder width/2+1cm and of B/4+2cm. The movement of back center line will be bigger for a person with leaned-backward body type. 2. The front & back length: We made the front length to B.P length+2.5cm to have Jugori cover the breast point fully around the bust line, which is a vogue nowadays. For an upright body type, we decided the back length as (AH/2.2)+5cm. And for a bent-forward and a leaned-backward body type, we adjusted the calculation formulae differently taking the physical characteristics into account. We decided the back length (A) as (A.H/2.2)+5cm, and the front length (B) as the back length+5cm. So, (A+B) is the sum of the front length and the back length. Going back to the original formula, the front length is B.P+2.5cm. So, we can decide the back length if we subtract B.P+2.5cm from the sum of the front length and the back length. To make well-fit Jugoris, the front & back length are areas that we should pay attention to if we take each person's physical characteristics into consideration. 3. Go-dae (1/2 portion) : We decided Go-dae as the neck width/2+0.5cm. For an upright body type, because the base line which went down vertically from the tragion was straight, we generally decided Go-dae Dalim line as 1.0cm. But we decided Go-dae Dalim line down to 1.5cm for bent-forward type and up to 0.2cm for leaned-backward type because the upper half of the body of them was bent forward or leaned backward from the base line. 4. The armhole : We decided the armhole circumference as A.H/2+2cm with the whole extra room of 4cm. 5. The side line length : We can calculate the side line length to (the back length-the armhole)/2, and, in terms of the trend, 2.5cm will be appropriate.

• Cartoon and Animation Studies
• /
• s.39
• /
• pp.321-348
• /
• 2015

The purpose of this study was to find a system and a progression principle in orchestral piece based on the outcome after comparatively analyzing the orchestral operation technique in "Battle for The Glory" out of the background music in the animation "Dragon Quest IV" by Koichi Sugiyama(1931~), who is a leading runner of Japan's animation music, based on functional parts daccord & Instrumentation Pattern, Rhythm Pattern, Voicing Pattern, and harmonic ratio by progression section devised by the writer. As a result, first, five themes in this music have specific instrumentation pattern, respectively. In a passage that is shown exposition, reprise, and representation in theme, the unity was emphasized by maintaining the same instrumentation pattern. On the contrary to this, a passage of being suggested new theme is being used the exchange method and addition & subtraction in musical instrument in order to strengthen diversity. Second, the voicing pattern is forming the vertical contrasting relationship of "thinness-thickness" on the whole. However, the diversity is being intensified that is changed into the structure of "thickness-thinness" in the third theme and of "thinness-thickness" that has two melodies in the fourth theme. Third, the rhythm pattern is forming the vertical contrasting relationship of "big-small" on the whole. However, the fifth theme is being given diversity with being changed into the structure of "small-big." Fourth, the harmony by progression section from the horizontal perspective is shown to be high in the proportion of unity in the section of being repeated and represented the theme and to be high in the proportion of diversity in the section of being suggested new theme. In this study, the balanced orchestral operation technique through the operation technique, which was used in this work, is what extracts the relationship of diverse proportions in the horizontal progression section based on the technique of vertical perspective. In this aspect, this analytical study is desired to be positioned as a new paradigm in establishing a theoretical system and an educational method in orchestration.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.179-200
• /
• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.103-128
• /
• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Digital Convergence
• /
• v.18 no.6
• /
• pp.523-529
• /
• 2020

The purpose of this study is to collect official world records for a total of five years from 2015 to 2019 from the modern pentathlon world competition database to reveal the impact of shooting types and characteristics on the record. To that end, the entire shooting spree was analyzed for all male and female athletes participating in the UIPM Level 1 World Cup and World Championships. According to the study, the number of round trips and the number of cars increased, the number of shooting accumulation deteriorated, the best record in the first round trip 3rd round, and the worst record in the fourth round trip 5th round. In addition, the deviation values are accumulated according to the fire recording or without success of the first step round trip by 9 percent in accordance with the growing number of the deviation is an increasing trend is, is that over time. Modern pentathlon at the success of the first step is more important and as fire can just hit first step in the event of great effect in reducing record. Based on these studies, the factors and characteristics that affect shooting accuracy are identified, and follow-up research linked to track records is necessary to match the characteristics of the combined competition.

• The Journal of Society for e-Business Studies
• /
• v.20 no.1
• /
• pp.45-59
• /
• 2015

As many companies have been interested in big data, they have invested a lot of resources to get more customer data. Some companies try to trade the data illegally. In order to collect more customer data, companies provide various incentive programs to customers. However, their results are normally much less than their expectations. This study focuses on exploring the relative importance of the factors which influence customer attitudes to providing his/her personal information. This study conducts a conjoint analysis to assess trade-offs among the five influential factors-monetary reward, concern for data collection, concern for secondary use, concern for unauthorized use, and concern for errors. This study finds that the customer attitude to providing personal information is most influenced by the concern for secondary use. Furthermore, it shows that there are some differences between the light internet user group and the heavy internet user group in the relative importances of these factors. The monetary rewards appeal to the heavy internet users, rather than the light internet users.