• Korean Journal of Construction Engineering and Management
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• v.20 no.4
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• pp.22-33
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• 2019

The government has steadily revised and developed laws and systems for activating remodeling of apartments in response to the problems of aged apartments. However, despite such efforts, remodeling has yet to be activated. For many reasons, this study noted that there were no tools for reasonable profitability judgements and decision making in the preliminary stages of the remodeling project. Thus, the feasibility evaluation model was developed. Generally, the profitability judgements are made after the conceptual design. However, decisions to drive remodeling projects are made at the preliminary stage. So a feasibility evaluation model is required at the preliminary stage. Accordingly, In this study, a feasibility evaluation model was developed for determining preliminary stage profitability. Construction costs, business expenses, financial expenses, and generally sales revenue were calculated using the initial available information and remodeling variables derived through the existing cases. Through this process, we developed an algorithm that can give an overview of the return on investment. In addition, the preliminary stage feasibility evaluation model developed was applied to three cases to verify the applicability of the model. Although applied in three cases, the difference between the model's forecast and actual case values is less than 5%, which is considered highly applicable. If cases are expanded in the future, it will be a useful tool that can be used in actual work. The feasibility evaluation model developed in this study will support decision making by union members, and if the model is applied in different regions, it will be expected to help local governments to understand the size of possible remodeling projects.

• Journal of Environmental Impact Assessment
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• v.30 no.2
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• pp.105-116
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• 2021

In the city of high population density crowded with buildings, Urban Heat Island (UHI) is intensified, and the city is vulnerable to thermal comfort. The maintenance of vacant land in downtown is treated as a factor that undermines the residential environment, spoils the urban landscape, and decreases the economic vitality of the whole region. Therefore, this study compared the effects on microclimate in the surrounding area according to the development scenarios targeting the vacant land in Songhyeon-dong, Jongno-gu, Seoul. The status quo, green oriented, building oriented and green-building mediation scenarios were established and ENVI-met was used to compare and analyze the impact of changes in wind speed, air temperature and mean radiant temperature (MRT) within 1 km of the target and the target site. The result of inside and 1 km radius the targeted area showed that the seasonal average temperature decreased and the wind speed increased when the green oriented scenario was compared with the current state one. It was expected that the temperature lowered to -0.73 ℃ or increased to 1.5 ℃ in summer, and the wind speed was affected up to 210 meters depending on the scenario. And it was revealed that green area inside the site generally affects inside area, but the layout and size of the buildings affect either internal and external area. This study is expected to help as a decision-making support tool for developing Songhyeon-dong area and to be used to reflect the part related to microclimate on the future environmental effects evaluation system.

• Journal of Technology Innovation
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• v.30 no.3
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• pp.1-34
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• 2022

This study examines the effect of CEO age on the value relevance of R&D which is the relationship between R&D expenditures and firm value. The value relevance of R&D expenditures is higher in companies with current older CEOs, while the relationship in companies with younger CEOs is lower than that of other companies. These results suggest that older CEOs tend to be conservative and make prudent R&D investment decisions. Because they make systematic investment decisions with rich experience, they are expected to have higher investment performance in the market. On the other hand, young CEOs choose risky investments in order to have their abilities highly evaluated in the labor market. The market places a high degree of risk on the R&D decision-making of young CEOs. Next, we analyze whether the age of the replaced CEOs affects the relationship between R&D expenditures and firm value. The result shows that the change of management increases the effect of R&D expenditure on firm value. However, in the case of being replaced by a younger CEO, this positive relationship becomes lower than that of other companies, showing results consistent with the case of the current younger CEO. The samples are analyzed by dividing them into conglomerates and non-conglomerates. In conglomerates, the age of the replaced CEOs does not affect the value relevance of R&D expenditures. Only non-conglomerates showed a negative (-) effect on the replaced younger CEOs. These results suggest that conglomerates maintain the stability of R&D management and performance so that the performance of R&D expenditures is not significantly affected by the age of the replaced CEOs. The reason is that mutual checks and support are coordinated within the group through decentralization of work and systematization of decision-making. This study shows evidence that the relationship between R&D expenditure and firm value according to the age of the replaced CEO is a phenomenon that only occurs in non-conglomerates. This phenomenon suggests that conglomerates are stably managing their R&D performance regardless of the change of CEOs or the characteristics of the CEOs.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• 한국사회정책
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• v.25 no.2
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• pp.367-402
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• 2018

This study analyzed the relationship between the act on the guarantee of rights of and support for persons with developmental disabilities(Act for PWDD) and the similar ordinance of the local governments based on this law and focused on the correspondence(the rate of reflection) and the autonomy(differentiation). As of October 2017, 63 local government regulations and Act for PWDD were analyzed in this study. The results of the analysis are as follows: First, the rate of reflection in the ordinance of Act for PWDD was different according to the clause. In the aspect of emphasizing welfare support, the agreement between local ordinance and rate was high. While the Act for PWDD emphasized the rights of persons with developmental disabilities, there was little information about their right in the ordinance of local governments. This is evidence that current ordinance is based on the protective point of view for people with developmental disabilities. In the future, policy measures will be needed to ensure that respect for decision-making by persons with developmental disabilities and rights guarantees are included in the bylaws. Second, there is a provision that the rate of ordinance reflection is 0%, which may be guaranteed by other laws in the area, so it does not mean the absence of related system in the region, but there is possibility of institutional blind spot. In the future, consideration should be given to the complementarity of other legal systems in the area with developmental disabilities, so that persons with developmental disabilities should not be placed in institutional blind spots. Third, the autonomy(differentiation) of local ordinance was examined from the contents aspect and the administrative aspect to help practical implementation. The differentiation between the ordinances vary. Emphasizing the responsibilities of the head of the organization, emphasizing the fact-finding survey, setting up the welfare committee, or adding local needs were included to the ordinance. Local governments considering the enactment of ordinances in the future should refer to these cases and establish enactable local ordinances that take advantage of the characteristics of local autonomy.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Venture Innovation
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• v.6 no.2
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• pp.41-65
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• 2023

This study was conducted to empirically analyse the factors that influence SMEs' intention to adopt ESG. For this purpose, we first derived the variables of usefulness of ESG and ease of adoption. In addition, we adopted CEO's will because of the importance of CEO's role in decision-making in SMEs. In addition, we added customer's request, government support, and credit evaluation reflection as institutional factors for ESG management. To examine the mediating role of attitudes and employees' innovation resistance in these relationships and how they affect ESG adoption, we set up a research model. These factors were used in the empirical analysis with 368 valid responses from the survey. Hierarchical regression analysis method using SPSS 24.0 was used for statistical analysis, and Process Macro 4.0 based on SPSS 24 was used for mediation and moderation effects. The results of the empirical analysis of this study showed that the usefulness of ESG adoption, ease of adoption, CEO's will, customer's request, government support, and credit evaluation reflection all had a positive and significant effect on the intention to adopt ESG management. In particular, among the variables affecting ESG adoption, CEO's will was found to be the most influential. Attitudes were also found to play a mediating role between the influencing factors and intention to adopt ESG management, as well as the mediating effect of employee' innovation resistance. The academic implications of this study include the identification and empirical testing of each of the influencing variables of ESG management adoption in the scarce literature on ESG in SMEs, and the prioritisation of the influence of these factors on adoption intention, which can be used to promote the adoption of ESG management. In terms of practical implications, it is important for SMEs to have a win-win relationship with large corporations, an ecosystem such as government support, in order to improve CEO awareness and motivate the CEO's will, and for smooth introduction of ESG management, it is necessary to find ways to reduce resistance through sufficient communication with organizational members to make them aware of the need.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.82-98
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• 2009

This study applied SWAT model to analyze suspended sediment load that is influence on the high density turbid water in Donghyang and Cheoncheon basin, which are located in the upstream of Yongdam Dam. GIS data such as DEM, land cover map and soil map, and meteorological data were used as the input data of SWAT model. And the rating curve equation and Q-SS equation of Donghyang and Cheoncheon gauge station were applied as the measured values of them. As the result of flowout, the coefficient of determination ($R^2$) and the Nash-Sutcliffe coefficient of efficiency (EI) of model calibration showed high as 0.87 and 0.87 at Donghyang gauge station, and the $R^2$ and EI of model validation were high as 0.95 at Cheoncheon gauge station. Also, as the result of suspended sediment load, the $R^2$ and EI of model calibration were high as 0.77 and 0.76 at Donghyang gauge station, and the $R^2$ and EI of model validation marked high as 0.867 and 0.80 at Cheoncheon gauge station. It is considered that the suspended sediment load of 2003 showed the highest due to rainfall amounts and rainfall intensity in using SWAT model. The results of suspended sediment modeled in this study can be applied to the decision-making support data for the evaluation of soil erosion possibility and turbid water potential in the management of reservoir.

• Journal of Food Hygiene and Safety
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• v.28 no.1
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• pp.83-88
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• 2013

Foreign matters in foods are important food safety issue of consumers, retailers, food manufacturers and food safety authorities in Korea. In order to provide information for development of risk management options and detection technology for foreign matters, multifactorial risk of foreign matters in foods was estimated based on various factors including detection rate, health adverse effect, economic and social aspects. For the each of five food items and foreign matters which were selected from previous study, factors including detection rates, health adverse effects, annual production amounts and willingness to additional pay to reduce foreign matters in foods were quantitatively estimated. The highest risk score was estimated for metal-noodle combination followed by insect-noodle and metal-beverage combinations. The multifactorial risk assessment on foreign matters in food could provide useful information to support risk managers and scientist in complex decision making when various factors should be concerned and different food-foreign matter combinations are compared.