• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.341-353
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• 2016

Highway is mainly in charge of middle-long distance of vehicular travel. Trip length has shown a growing trend due to increased commute distances by the relocation of public agencies. For this reason, the proportion of driver-driven accidents, caused by their fatigue or sleepiness, are very high on highways. However, existing studies related to accident prediction have mainly considered external factors, such as road conditions, environmental factors and vehicle factors, without driving behavior. In this study, we suggested an accident index (FDR, Fatigued Driving Rate) based on traffic behavior using large-scale Car Navigation path data, and exlpored the relationship between FDR and traffic accidents. As a result, FDR and traffic accidents showed a high correlation. This confirmed the need for a paradigm shift (from facilities to travel behavior) in traffic accident prediction studies. FDR proposed in this study will be utilized in a variety of fields. For example, in providing information to prevent traffic accidents (sleepiness, reckless driving, etc) in advance, utilization of core technologies in highway safety diagnostics, selection of priority location of rest areas and shelter, and selection of attraction methods (rumble strips, grooving) for attention for fatigued sections.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3260-3269
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• 2014

The construction industry has strong ties with other industries, and so construction company insolvency also has a strong influence on other industries. Prediction models addressing the insolvency of construction company have been well studied. Although factors contributing to insolvency must precede those of predictions of insolvency, studies on these contributing factors are limited. The purpose of this study is to analyze the influence of changes in the housing market on construction company insolvency by using the Vector Error Correction Model. Construction companies were divided into two groups, and the expected default frequency(EDF), which indicates insolvency of each company was measured through the KMV model. The results verified that 10 largest construction companies were in a better financial condition compared to relatively smaller construction companies. As a result of conducting impulse response analysis, the EDF of large companies was found to be more sensitive to housing market change than that of small- and medium-sized construction companies.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.197-215
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• 2021

This paper analyzes the machine learning predictions of the economic effects of Busan's strategic industries on the employment and income using the Ridge Regression and Lasso Regression models with regulation terms. According to the Ridge estimation and Lasso estimation models of employment, the intelligence information service industry such as the service platform, contents, and smart finance industries and the global tourism industry such as MICE and specialized tourism are predicted to influence on the employment in order. However, the Ridge and Lasso regression model show that the future transportation machine industry does not significantly increase the employment and income since it is the primitive investment industry. The Ridge estimation models of the income show that the intelligence information service industry and global tourism industry are also predicted to influence on the income in order. According to the Lasso estimation models of income, four strategic industries such as the life care, smart maritime, the intelligence machine, and clean tech industry do not influence the income. Furthermore, the future transportation machine industry may influence the income negatively since it is the primitive investment industry. Thus, we have to select the appropriate economic objectives and priorities of industrial policies.

• Journal of Hospice and Palliative Care
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• v.5 no.2
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• pp.111-124
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• 2002

Purpose : Although the average life expectancy has increased due to advances in medicine, mortality due to cancer is on an increasing trend. Consequently, the number of terminally ill cancer patients is also on the rise. Predicting the survival period is an important issue in the treatment of terminally ill cancer patients since the choice of treatment would vary significantly by the patents, their families, and physicians according to the expected survival. Therefore, we investigated the prognostic factors for increased mortality risk in terminally ill cancer patients to help treat these patients by predicting the survival period. Methods : We investigated 31 clinical parameters in 157 terminally ill cancer patients admitted to in the Department of Family Medicine, National Health Insurance Corporation Ilsan Hospital between July 1, 2000 and August 31, 2001. We confirmed the patients' survival as of October 31, 2001 based on medical records and personal data. The survival rates and median survival times were estimated by the Kaplan-Meier method and Log-rank test was used to compare the differences between the survival rates according to each clinical parameter. Cox's proportional hazard model was used to determine the most predictive subset from the prognostic factors among many clinical parameters which affect the risk of death. We predicted the mean, median, the first quartile value and third quartile value of the expected lifetimes by Weibull proportional hazard regression model. Results : Out of 157 patients, 79 were male (50.3%). The mean age was $65.1{\pm}13.0$ years in males and was $64.3{\pm}13.7$ years in females. The most prevalent cancer was gastric cancer (36 patients, 22.9%), followed by lung cancer (27, 17.2%), and cervical cancer (20, 12.7%). The survival time decreased with to the following factors; mental change, anorexia, hypotension, poor performance status, leukocytosis, neutrophilia, elevated serum creatinine level, hypoalbuminemia, hyperbilirubinemia, elevated SGPT, prolonged prothrombin time (PT), prolonged activated partial thromboplastin time (aPTT), hyponatremia, and hyperkalemia. Among these factors, poor performance status, neutrophilia, prolonged PT and aPTT were significant prognostic factors of death risk in these patients according to the results of Cox's proportional hazard model. We predicted that the median life expectancy was 3.0 days when all of the above 4 factors were present, $5.7{\sim}8.2$ days when 3 of these 4 factors were present, $11.4{\sim}20.0$ days when 2 of the 4 were present, and $27.9{\sim}40.0$ when 1 of the 4 was present, and 77 days when none of these 4 factors were present. Conclusions : In terminally ill cancer patients, we found that the prognostic factors related to reduced survival time were poor performance status, neutrophilia, prolonged PT and prolonged am. The four prognostic factors enabled the prediction of life expectancy in terminally ill cancer patients.

• Journal of Korean Society of Forest Science
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• v.83 no.2
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• pp.175-190
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• 1994

In order to get fundamental information for prediction of landslide hazard, both forest and site factors affecting slope stability were investigated in many areas of active landslides. Twelve descriptors were identified and quantified to develop the prediction model by multivariate statistical analysis. The main results obtained could be summarized as follows : The main factors influencing a large scale of landslide were shown in order of precipitation, age group of forest trees, altitude, soil texture, slope gradient, position of slope, vegetation, stream order, vertical slope, bed rock, soil depth and aspect. According to partial correlation coefficient, it was shown in order of age group of forest trees, precipitation, soil texture, bed rock, slope gradient, position of slope, altitude, vertical slope, stream order, vegetation, soil depth and aspect. The main factors influencing a landslide occurrence were shown in order of age group of forest trees, altitude, soil texture, slope gradient, precipitation, vertical slope, stream order, bed rock and soil depth. Two prediction models were developed by magnitude and frequency of landslide. Particularly, a prediction method by magnitude of landslide was changed the score for the convenience of use. If the total store of the various factors mark over 9.1636, it is evaluated as a very dangerous area. The mean score of landslide and non-landslide group was 0.1977 and -0.1977, and variance was 0.1100 and 0.1250, respectively. The boundary value between the two groups related to slope stability was -0.02, and its predicted rate of discrimination was 73%. In the score range of the degree of landslide hazard based on the boundary value of discrimination, class A was 0.3132 over, class B was 0.3132 to -0.1050, class C was -0.1050 to -0.4196, class D was -0.4195 below. The rank of landslide hazard could be divided into classes A, B, C and D by the boundary value. In the number of slope, class A was 68, class B was 115, class C was 65, and class D was 52. The rate of landslide occurrence in class A and class B was shown at the hige prediction of 83%. Therefore, dangerous areas selected by the prediction method of landslide could be mapped for land-use planning and criterion of disaster district. And also, it could be applied to an administration index for disaster prevention.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.109-141
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• 2006

The central factor in the pecking order theory of financial structure is the asymmetric distribution of information between managers and less-informed outside investors. Myers and Majluf (1984) show that this asymmetry leads managers to prefer internal funds to external funds. Funds are raised through equity issues only after the capacity to issue debt has been exhausted. In contrast, according to static tradeoff theory, an optimum financial structure exists by the tradeoff between tax saving by debt and bankruptcy costs. This study examines the recent changes of Korean firms' financial structure and financing behavior and the determinants of financial structure. The sample of firms comes from the period of $1996{\sim}2004$, and the number of firms is 32,003. The major findings are as follows. First, in contrast with previous studies using US firms as sample, Korean firms have been using debt financing as their major financing instrument. Especially, the firms in the fund deficit situation relies much more on $long{\sim}term$ and $short{\sim}term$ debts rather than on equity issues. Second, as is the case with previous studies using US firms sample indicates, the financing deficit variable can not explain perfectly the net debt issue. However, compared with net equity issue variable, net debt issue variable is more closely related to the financing deficit variable. Third, when financing deficit variable is added to the current list of explanatory variables of financial structure determinants model, it has a significant and positive explanatory power. In addition, the coefficients of determinants are much improved. Thus, it is concluded that although pecking order theory is not perfect, it appears to be more useful compared to static tradeoff theory, at least in explaining the recent financing behavior of Korean manufacturing firms.

• Journal of the Korean Geographical Society
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• v.28 no.4
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• pp.321-355
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• 1993

This paper focuses on the geographical pattern of financial activities in the Korean urban system during 1975-1990, based on the assumption that financial activities can reveal control points in Korea's urban economy. In terms of spatial evolution of financial insitutions, different locational characteristics are revealed among different types of financial institutions, implying the role of urban hierarchy. Financial resources are highly concentrated in the capital region, Seoul and Kyonggi Province. Both centralization trends into the large metropolitan cities and relative declines of medium and small cities within the Korean urban system, have been experienced over the study period. Financial activities sustain relatively stable hierarchical structure in the urban hierarchy. Regarding the financial flows, dominant flow zones centered on major metropolitan cities are identified, clearly showing a prominant role of Seoul in financial flows in the entire urban system.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.694-709
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• 2021

Purpose: In this study, earthquake-induced landslide risk assessment was conducted to provide basic data for efficient and preemptive damage prevention by selecting the erosion control work before the earthquake and the prediction and restoration priorities of the damaged area after the earthquake. Method: The study analyzed the previous studies abroad to examine the evaluation methodology and to derive the evaluation factors, and examine the utilization of the landslide hazard map currently used in Korea. In addition, the earthquake-induced landslide hazard map was also established on a pilot basis based on the fault zone and epicenter of Pohang using seismic attenuation. Result: The earthquake-induced landslide risk assessment study showed that China ranked 44%, Italy 16%, the U.S. 15%, Japan 10%, and Taiwan 8%. As for the evaluation method, the statistical model was the most common at 59%, and the physical model was found at 23%. The factors frequently used in the statistical model were altitude, distance from the fault, gradient, slope aspect, country rock, and topographic curvature. Since Korea's landslide hazard map reflects topography, geology, and forest floor conditions, it has been shown that it is reasonable to evaluate the risk of earthquake-induced landslides using it. As a result of evaluating the risk of landslides based on the fault zone and epicenter in the Pohang area, the risk grade was changed to reflect the impact of the earthquake. Conclusion: It is effective to use the landslide hazard map to evaluate the risk of earthquake-induced landslides at the regional scale. The risk map based on the fault zone is effective when used in the selection of a target site for preventive erosion control work to prevent damage from earthquake-induced landslides. In addition, the risk map based on the epicenter can be used for efficient follow-up management in order to prioritize damage prevention measures, such as to investigate the current status of landslide damage after an earthquake, or to restore the damaged area.