• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1996.06a
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• pp.79-109
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• 1996

In order to transform the underground utility data into the 3-D spatial objects, this thesis defined the type of the 3-D spatial objects and the storage structure of objects, and developed the spatial data transformation system that transforms the 2-D underground utility data into the pre-defined 3-D spatial objects through the Booch Method, an object-oriented development method. For this sake, the relational data model of ARC/INFO and the storage structure are analyzed, as well as the pre-requisites, required for the 3-D visualization of the underground utilities. Also, the 2-D underground utility data, saved in ARC/INFO, were transformed into the ASCII files through the internal pre-processing procedure, then used as the input data of the transformation system. In addition, to develop the transformation system, the object-oriented development methods are studied first and, then, applied to the system analysis and the design procedure with the Booch Method as a development method. Finally, the results of analysis and design procesure are implemented through AML, a ARC/INFO macro language, and C++, an object-oriented programing language. As a result of this study, the 3-D spatial objects that can visualize the 2-D utilities in 3-D, are acquired, and the adaptation of object-oriented development method to the system development procedure enabled an effective development prodedure.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.326-332
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• 2012

In this paper, error analyses on the calculation of offshore wind speed have been conducted using HeMOSU-1 data to develop offshore wind energy in Yeonggwang sea of Korea and onshore observed wind data in Buan, Gochang and Yeonggwang for 2011. Offshore wind speed data at 98.69 m height above M.S.L is estimated using relational expression induced by linear regression analysis between onshore and offshore wind data. In addition, estimated offshore wind speed data is set at 87.65 m above M.S.L using power law wind profile model with power law exponent(0.115) and its results are compared with the observed data. As a result, the spatial adjustment error are 1.6~2.2 m/s and the altitude adjustment error is approximately 0.1 m/s. This study shows that the altitude adjustment error is about 5% of the spatial adjustment error. Thus, long term observed data are needed when offshore wind speed was estimated by onshore wind speed data. because the conversion of onshore wind data lead to large error.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.141-167
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• 2021

Authenticity is the opposite of hypocrisy or deceitful living in philosophy. While various positive factors that humans experience in life based on authenticity have been studied abroad, most of the studies in Korea that tried to measure authenticity did not take into account the characteristics of Korean culture or were developed only for the purpose of use in a limited domain or specific purpose. In this study, based on the specificity of Korean culture, we developed a measure of authenticity that researchers can use universally. To this end, the items constituting the existing authenticity scale and the items reflecting the cultural value of Korean society, which value social relationships, are integrated. The results of exploratory factor analysis and confirmatory factor analysis indicated that authenticity consists of three factors: self-awareness, behavioral authenticity, and relationship authenticity. In addition, criterion validity was verified based on correlations with life satisfaction, mindfulness, self-esteem, HEXACO, social desirability, self-regulation focus, and emotional diversity. These results suggest that the authenticity scale of this study is a reliable and valid measure, and is expected to be an important tool for empirical individual differences research on authenticity in everyday life in Korean population.

• Journal of Food Hygiene and Safety
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• v.35 no.6
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• pp.607-617
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• 2020

Human bio-monitoring (HBM) data is a very important resource for tracking total exposure and concentrations of a parent chemical or its metabolites in human biomarkers. However, until now, it was difficult to execute the integration of different types of HBM data due to incompatibility problems caused by gaps in study design, chemical description and coding system between different sources in Korea. In this study, we presented a standardized code system and HBM knowledge model (KM) based on relational database modeling methodology. For this purpose, we used 11 raw datasets collected from the Ministry of Food and Drug Safety (MFDS) between 2006 and 2018. We then constructed the HBM database (DB) using a total of 205,491 concentration-related data points for 18,870 participants and 86 chemicals. In addition, we developed a summary report-type statistical analysis program to verify the inputted HBM datasets. This study will contribute to promoting the sustainable creation and versatile utilization of big-data for HBM results at the MFDS.

• Journal of Korea Port Economic Association
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• v.38 no.1
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• pp.143-158
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• 2022

The shipping industry has experienced significantly fluctuated while decline in long-term and boom in short-term period due to the continuous recession of the shipping industry since the 2010s and during the recent COVID-19 pandemic. This study aims to suggest implications for securing manpower and personnel policy by analyzing the effects of job environmental factors of a liner shipping company on job satisfaction and turnover intention and differences between land and marine workers. For the analysis, the job environmental factors of a liner shipping company were divided into work character, relational factors, personnel characters, and remuneration factors, and then structural equation model and t-test were conducted to test the relationship between job satisfaction and turnover intention. As a result of the study, it was found that job environmental factors, such as work, relationship, personnel, and remuneration, had a positive (+) effect on job satisfaction, and job satisfaction had a negative (-) effect on turnover intention. In a comparative analysis between groups such as land and sea workers, it was found that the maritime workers group had higher job satisfaction and lower turnover intention than the land workers.

• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.63-109
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• 2008

This article examines psychological factors that contribute to educational achievement of Korean adolescents. By reviewing empirical research, three core areas are outlined. First, positive and negative roles that Korean society play on academic achievement are analyzed. Compared to other countries, Korean society places a high premium, pressure an investment on educational achievement. This has contributed to the rapid economic growth and development, but at the same time it has created numerous social problems. Second, psychological and relational dynamics of investing in and achieving success in education are delineated. Through indigenous psychological analysis, the role parents play in motivating and regulating their children to succeed academically is reviewed. In addition, the role of teachers and friends and the coordinated efforts of Korean society are outlined. Third, future directions and transformations in education that are needed in Korean society are discussed. Although Korean adolescents are high achievers in high school, this is not the case at the university level. Since Korean students are interested in entering a prestigious university, they have developed skills in doing well in standardized tests. Educational reforms need to take into consideration individuals' personal interests, skills and creativity to ensure that the knowledge that they acquired can be used to further their career and improve their subjective well-being. Educational transformation does not imply simply adopting Western models, but developing indigenous models that can maximize human and cultural potential and stimulate curiosity, diversity and creativity that are necessary in the global era.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.199-208
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• 2023

Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation(max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.

• Korean Journal of Culture and Social Issue
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• v.10 no.spc
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• pp.177-209
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• 2004

This paper provides a theoretical and conceptual framework for understanding adolescent culture and educational achievement in Korea. In the first part of the paper, the authors outline a research paradigm in cultural psychology and adolescent culture. In the second section, the traditional family structure, the role of parents, and how they have been changed by modernization are outlined. In the third section, socialization practices and parent-child relationship are reviewed. In the fourth section, Western theories that have been developed to explain educational achievement and their limitations are examined. In the fifth section, factors that contribute to educational success of Korean students are presented. In the final section, the impact of centralized, standardized, and rigid educational system that is imposed on adolescents is discussed. The highly regulated and centralized bureaucracy restricts educational and career opportunities for adolescents and it is responsible for the high rate of violence, delinquency, and bullying in Korea. The need for encouraging civil society that allows for diversity of ideas and skills and at the same time maintaining strong relational bonds are discussed.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.