• Journal of the Korea Convergence Society
• /
• v.11 no.11
• /
• pp.321-335
• /
• 2020

Given that with the development of the 4th industry, interest and desire for smart cities are gradually increasing and related technologies are developed as a way to strengthen urban competitiveness by utilizing big data, information and communication technology, IoT, M2M, and AI, the purpose of this study is to find out how to achieve this goal on the premise of the idea of smart well fair city. In other words, the purpose is to devise a smart well-fair city in the care area, such as health care, medical care, and welfare, and see if it is feasible. With this recognition, the paper aimed to review the concept and scope of smart city, the discussions that have been made so far and the issues or limitations on its connection to social security and social welfare, and based on it, come up with the concept of welfare city. As a method of realizing the smart welfare city, the paper reviewed characteristics and features of a social security platform as well as the applicability of smart city, especially care services. Furthermore, the paper developed discussions on the standardization of the city in terms of political and institutional improvements, utilization of personal information and public data as well as ways of institutional improvement centering on social security information system. This paper highlights the importance of implementing the digitally based community care and smart welfare city that our society is seeking to achieve. With regard to the social security platform based on behavioral design and the 7 principles(6W1H method), the present paper has the limitation of dealing only with smart cities in the fields of healthcare, medicine, and welfare. Therefore, further studies are needed to investigate the effects of smart cities in other fields and to consider the application and utilization of technologies in various aspects and the corresponding impact on our society. It is expected that this paper will suggest the future course and vision not only for smart cities but also for the social security and welfare system and thereby make some contribution to improving the quality of people's lives through the requisite adjustments made in each relevant field.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.45 no.4
• /
• pp.11-22
• /
• 2017

A plaza represents the identity of a city, and that reveals a plaza's importance. Gwanghwamun Plaza and Seoul Plaza are two representative plazas where the citizens can freely express their opinions. Many major plazas in the center of Seoul were built under the Japanese occupation. Among these, the plaza of Joseon Bank has different characteristics than Gwanghwamun Plaza and Seoul Plaza. Even though this plaza was built in the center of the commercial, administrational, and cultural district during the Japanese colonial period, the research on this plaza has been limited. This study was conducted to verify the features of this plaza by analyzing its construction and transformation during the Japanese colonial period. The study's results outline how the plaza was constructed by the Japanese administration. The intention of the government is shown by the fact that it purchased land parcels and held a design competition. In the 1910s, the government purchased seven parcels of land during the expansion of roads as the place for the plaza. During the late 1930s, the government accepted a traffic circle to regulate the traffic and eliminate the conflict between crossing movements. In 1939, a fountain was built in the plaza's center, and its design was selected through a design competition. It was planned as a square, but gradually turned into a rotary. Furthermore, the plaza was a landmark and symbol of the power and modernity of Japan. As the main modal point of public transportation, the plaza became surrounded with largescale Western-style buildings, commercial advertising, and neon signs. The plaza became a place where people could experience the modern city. These spectacular displays showed that Japanese imperialism was perceived as a strange and peculiar landscape to the majority of Korean citizens. This study investigates the history and characteristics of the plaza, focusing on its beginning as well as the transformation of its form. As to the limitations of the study, it does not consider political and economic contexts within the transformation of Seoul and in relation to this plaza. Instead, that research remains for a future study.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.47 no.6
• /
• pp.1-11
• /
• 2019

This study aims to understand the tendencies and characteristics when constructing landscape architectural archives in the United States, and to find implications for creating Korean landscape architectural archives. Focusing on seven American landscape architectural archives operated by public institutes, universities, and research institutes, this study examines the background, mission, scope, subject, acquisition, arrangement, and application of the programs in the archives. The results show that the characteristics of the American landscape architectural archives are as follows: first, the recognition of the value and importance of the landscape plays a major role in the establishment of archives; second, the purpose of the archives is to educate and encourage people to find diversity or significance of landscapes; third, the subject and scope of the archives vary according to the purpose of each operating institution; fourth, the main acquisitional method is to receive a donation and to purchase materials or documents; fifth, the classification systems of each archive differs depending on the subject and scope; and sixth, the archives were built on the basis of participation and collaboration with various experts and organizations with stable and sustainable financial resources. These characteristics offer three implications for constructing landscape architectural archives in Korea. First, a long-term vision for landscape architectural archives and the establishment of differentiated themes are required. Second, appropriate methods of material collection and systems of classification, recording, and digitization are necessary and therefore research and pilot archives are required. Third, it is necessary to secure the sustainability of archives by developing ways to utilize the archives in connection with academic and educational programs, thereby securing financial resources and allowing for the establishment of appropriate policies. As basic research, this study is significant as it provides a basis for further research concerning the development of landscape architectural archives in Korea.

• Asia pacific journal of information systems
• /
• v.19 no.2
• /
• pp.157-178
• /
• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Clinical and Experimental Pediatrics
• /
• v.49 no.2
• /
• pp.150-156
• /
• 2006

Purpose : The aims of this study included assessment of molecular-epidemiologic features during an outbreak of colonization of extended spectrum ${\beta}$-lactamase producing Klebsiella pneumoniae(ESBL-KPN) and re-evaluation of their colonized status one year later. Methods : Rectal swab cultures for ESBL-KPN from all hospitalized infants and newly admitted infants were obtained during the outbreak of colonization from July to December, 2000. The pattern of XbaI-digested chromosomal DNA of isolates were analyzed by pulsed-field gel electrophoresis. Weekly rectal swab cultures were obtained during the outbreak until patients were either discharged or decolonized. Patients discharged after being colonized had follow up stool cultures a year later. Results : A total of 80 patients(28.5 percent) were colonized. Of those, 53 whose pulsed-field gel electrophoresis(PFGE) was possible only once, were ESBL-KPN grouped into six cluster clones and 10 single clones : 28 patients(52.8 percent) were colonized with type A, the most common clone, followed by type B in 11 patients(20.8 percent). Of those 12 patients in whom serial PFGE was done more than twice, type A was predominant. Narrowed-down in strains occurred from types A, B, C, D and three single clones at initiation of the study into types A and type B after three months of strict infection control. Among 75 patients(93.7 percent) who were sent home after being colonized, 30 patients were re-called for stool cultures a year later : All of them were decolonized. Conclusion : This study demonstrates the importance of infection control as the diversity of ESBL-KPN strains could be narrowed into fewer strains. Colonization of ESBL-KPN could be reversed upon return to the community.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.37 no.3
• /
• pp.1-12
• /
• 2019

A bridge is a structure constructed on water or in the air for convenient passage. Compared to other buildings, the building materials and structures of bridge required unique functions to cross the space. It depends on the productivity of the building, the level of science and technology, and the ecological environment of the building site. Also, it has important relationship with functions such as politics, military, economy, and life. Most of the academic research on bridges is focused on research in the field of bridge-building technology, so the study on the landscape aesthetics and history of bridges is lacking. Against this backdrop, the study will be valuable as a accumulation of both countries' understanding of bridge types, history and culture, as well as technical and aesthetic data, by analyzing the bridges located within the palaces of Korea and China. The research method is to analyze the bridge through field survey and literature analysis.. First, the bridges of royal palace of Korea and China are to be classified quantitatively as physical shapes, landscapes, and decorations by comparing the materials, forms, landscapes, and decorative culture of bridges. Second, characteristics, common points, and differences are extracted by classifying bridges of both countries. Also, the results are discussed based on the physical environment or cultural background. This would be worth cross-referencing in the building technology and aesthetics of the two countries. For the first important characteristics of result, main materials of Korean and Chinese palaces are stone. However, the bridge in China's royal palaces is also focused on wood. Second, in terms of form, the bridges in the royal gardens of Korea and China are all based on the beam bridge. However, the specific form, ratio, style of the beam bridge, and airspace of arched bridge are very different. Third, most of the connection methods are focused on the over bridge. It values the convergence with the surrounding landscape. Due to the difference in the area and location of water, the bridge in the Korean palace is more focused on the convergence of the surrounding buildings and plants, while the bridge in the Chinese palace is more concerned about the harmony of hydration. Fourth, the decoration places importance on the artistry and aesthetics of both the bridges in Korea and China. There is a difference in style in the same type of decoration due to culture.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.199-230
• /
• 2021

Innovative new products and services are being launched through the convergence between heterogeneous industries, and social interest and investment in convergence industries such as AI, big data-based future cars, and robots are continuously increasing. However, in the process of commercialization of convergence new products and services, there are many cases where they do not conform to the existing regulatory and legal system, which causes many difficulties in companies launching their products and services into the market. In response to these industrial changes, the current government is promoting the improvement of existing regulatory mechanisms applied to the relevant industry along with the expansion of investment in new industries. This study, in these convergence industry trends, aimed to analysis the existing regulatory system that is an obstacle to market entry of innovative new products and services in order to preemptively predict regulatory issues that will arise in emerging industries. In addition, it was intended to establish a regulatory impact analysis system to evaluate adequacy and prepare improvement measures. The flow of this study is divided into three parts. In the first part, previous studies on regulatory impact analysis and evaluation systems are investigated. This was used as basic data for the development direction of the regulatory impact framework, indicators and items. In the second regulatory impact analysis framework development part, indicators and items are developed based on the previously investigated data, and these are applied to each stage of the framework. In the last part, a case study was presented to solve the regulatory issues faced by actual companies by applying the developed regulatory impact analysis framework. The case study included the autonomous/electric vehicle industry and the Internet of Things (IoT) industry, because it is one of the emerging industries that the Korean government is most interested in recently, and is judged to be most relevant to the realization of an intelligent information society. Specifically, the regulatory impact analysis framework proposed in this study consists of a total of five steps. The first step is to identify the industrial size of the target products and services, related policies, and regulatory issues. In the second stage, regulatory issues are discovered through review of regulatory improvement items for each stage of commercialization (planning, production, commercialization). In the next step, factors related to regulatory compliance costs are derived and costs incurred for existing regulatory compliance are calculated. In the fourth stage, an alternative is prepared by gathering opinions of the relevant industry and experts in the field, and the necessity, validity, and adequacy of the alternative are reviewed. Finally, in the final stage, the adopted alternatives are formulated so that they can be applied to the legislation, and the alternatives are reviewed by legal experts. The implications of this study are summarized as follows. From a theoretical point of view, it is meaningful in that it clearly presents a series of procedures for regulatory impact analysis as a framework. Although previous studies mainly discussed the importance and necessity of regulatory impact analysis, this study presented a systematic framework in consideration of the various factors required for regulatory impact analysis suggested by prior studies. From a practical point of view, this study has significance in that it was applied to actual regulatory issues based on the regulatory impact analysis framework proposed above. The results of this study show that proposals related to regulatory issues were submitted to government departments and finally the current law was revised, suggesting that the framework proposed in this study can be an effective way to resolve regulatory issues. It is expected that the regulatory impact analysis framework proposed in this study will be a meaningful guideline for technology policy researchers and policy makers in the future.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.4
• /
• pp.225-232
• /
• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.169-196
• /
• 2017

The fourth industrial revolution represents a revolutionary change in the business environment and its ecosystem, which is a fusion of Information Technology (IT) and other industries. In line with these recent changes, the Ministry of Employment and Labor of South Korea announced 'the Fourth Industrial Revolution Leader Training Program,' which includes five key support areas such as (1) smart manufacturing, (2) Internet of Things (IoT), (3) big data including Artificial Intelligence (AI), (4) information security, and (5) bio innovation. Based on this program, we can get a glimpse of the South Korean government's efforts and willingness to emit leading human resource with advanced IT knowledge in various fusion technology-related and newly emerging industries. On the other hand, in order to nurture excellent IT manpower in preparation for the fourth industrial revolution, the role of educational institutions capable of providing high quality IT education services is most of importance. However, these days, most IT educational institutions have had difficulties in providing customized IT education services that meet the needs of consumers (i.e., learners), without breaking away from the traditional framework of providing supplier-oriented education services. From previous studies, it has been found that the provision of customized education services centered on learners leads to high satisfaction of learners, and that higher satisfaction increases not only task performance and the possibility of business application but also learners' recommendation intention. However, since research has not yet been conducted in a comprehensive way that consider both antecedent and consequent factors of the learner's satisfaction, more empirical research on this is highly desirable. With the advent of the fourth industrial revolution, a rising interest in various convergence technologies utilizing information technology (IT) has brought with the growing realization of the important role played by IT-related education services. However, research on the role of IT education service quality in the context of IT education is relatively scarce in spite of the fact that research on general education service quality and satisfaction has been actively conducted in various contexts. In this study, therefore, the five dimensions of IT education service quality (i.e., tangibles, reliability, responsiveness, assurance, and empathy) are derived from the context of IT education, based on the SERVPERF model and related previous studies. In addition, the effects of these detailed IT education service quality factors on learners' educational satisfaction and their work application/recommendation intentions are examined. Furthermore, the moderating roles of learner position (i.e., practitioner group vs. manager group) and participation motivation (i.e., voluntary participation vs. involuntary participation) in relationships between IT education service quality factors and learners' educational satisfaction, work application intention, and recommendation intention are also investigated. In an analysis using the structural equation model (SEM) technique based on a questionnaire given to 203 participants of IT education programs in an 'M' IT educational institution in Seoul, South Korea, tangibles, reliability, and assurance were found to have a significant effect on educational satisfaction. This educational satisfaction was found to have a significant effect on both work application intention and recommendation intention. Moreover, it was discovered that learner position and participation motivation have a partial moderating impact on the relationship between IT education service quality factors and educational satisfaction. This study holds academic implications in that it is one of the first studies to apply the SERVPERF model (rather than the SERVQUAL model, which has been widely adopted by prior studies) is to demonstrate the influence of IT education service quality on learners' educational satisfaction, work application intention, and recommendation intention in an IT education environment. The results of this study are expected to provide practical guidance for IT education service providers who wish to enhance learners' educational satisfaction and service management efficiency.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.173-198
• /
• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.