• Korean Journal of Social Welfare
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• v.39
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• pp.350-373
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• 1999

The patient population of U. S. state mental hospitals has changed drastically since the 1960s, when the deintstitutionalization movement began. This paper is designed to look at what happened to the number of inpatients of state hospitals in California during the last 150 years and, from this, to explore implications for the future of the mental health system in Korea, especially for the viability of mental hospitals. The data had been collected by field research(visits to state hospitals and State Department of Mental Health, and interviews with mental health administrators) and accessing statistical publications and various reports. Since the first state hospital opened in 1851 the statewide inpatient population of individuals who were mentally disabled has grown and peaked at 37,489 in 1959. The number of patients in state hospitals, however, began declining in the early 1960s and was reduced to 10,874 by 1971, and to 4,973 by 1986. As of 1997, there were only 4, 263 inpatients remaining in the state hospital system. This dramatic decrease slowed down somewhat in 1980s and 1990s, but this trend seems irreversible except for the inpatients referred by the court. Now the beds in state hospitals are filled with more and more forensic patients, which constitutes nearly 70% of the total inpatient population. Based on these findings, it is well expected that the number of inpatients of mental hospitals in Korea will also be reduced in a significant way as the community-based mental health care system is gradually replacing the traditional one. Mental hospitals need to introduce more diversified programs for the care of the mentally ill, and concurrently more vigorous aftercare programs are required in the community.

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

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.43-65
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• 2010

Facing a complex environment driven by a decade, many companies are adopting new strategic frameworks such as Customer Relationship Management system to achieve sustainable profitability as well as overcome serious competition for survival. In many business areas, CRM system advanced a great deal in a matter of continuous compensating the defect and overall integration. However, pharmaceutical companies in Korea were slow to accept them for usesince they still have a tendency of holding fast to traditional way of sales and marketing based on individual networks of sales representatives. In the circumstance, this article tried to empirically address current status of CRM system as well as the effects of the system on the performance of pharmaceutical companies by applying BSC method's four perspectives, from financial, customer, learning and growth and internal process. Survey by e-mail and post to employers and employees who were working in pharma firms were undergone for the purpose. Total 113 cases among collected 140 ones were used for the statistical analysis by SPSS ver. 15 package. Reliability, Factor analysis, regression were done. This study revealed that CRM system had a significant effect on improving financial and non-financial performance of pharmaceutical companies as expected. Proposed regression model fits well and among them, CRM marketing information system shed the light on substantial impact on companies' outcome given profitability, growth and investment. Useful analytical information by CRM marketing information system appears to enable pharmaceutical firms to set up effective marketing and sales strategies, these result in favorable financial performance by enhancing values for stakeholderseventually, not to mention short-term profit and/or mid-term potential to growth. CRM system depicted its influence on not only financial performance, but also non-financial fruit of pharmaceutical companies. Further analysis for each component showed that CRM marketing information system were able to demonstrate statistically significant effect on the performance like the result of financial outcome. CRM system is believed to provide the companies with efficient way of customers managing by valuable standardized business process prompt coping with specific customers' needs. It consequently induces customer satisfaction and retentionto improve performance for long period. That is, there is a virtuous circle for creating value as the cornerstone for sustainable growth. However, the research failed to put forward to evidence to support hypothesis regarding favorable influence of CRM sales representative's records assessment system and CRM customer analysis system on the management performance. The analysis is regarded to reflect the lack of understanding of sales people and respondents between actual work duties and far-sighted goal in strategic analysis framework. Ordinary salesmen seem to dedicate short-term goal for the purpose of meeting sales target, receiving incentive bonus in a manner-of-fact style, as such, they tend to avail themselves of personal network and sales and promotional expense rather than CRM system. The study finding proposed a link between CRM information system and performance. It empirically indicated that pharmaceutical companies had been implementing CRM system as an effective strategic business framework in order for more balanced achievements based on the grounded understanding of both CRM system and integrated performance. It suggests a positive impact of supportive CRM system on firm performance, especially for pharmaceutical industry through the initial empirical evidence. Also, it brings out unmet needs for more practical system design, improvement of employees' awareness, increase of system utilization in the field. On the basis of the insight from this exploratory study, confirmatory research by more appropriate measurement tool and increased sample size should be further examined.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.73-91
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• 2013

Advertising market has been facing new challenges due to dramatic change in advertising channels and the advent of innovative media such as mobile devices. Recent research related to mobile devices is mainly focused on the fact that mobile devices could identify users'physical location in real-time, and this sheds light on how location-based technology is utilized to achieve competitive advantage in advertising market. With the introduction of smartphone, the functionality of smartphone has become much more diverse and context awareness is one of the areas that require further study. This work analyses the influence of context awareness value resulted from the transformation of advertising channel in mobile communication market, and our research result reflects recent trend in advertising market environment which is not considered in previous studies. Many constructs has intensively been studied in the context of advertising channel in traditional marketing environment, and entertainment, irritation and information are considered to be the most widely accepted variables that has positive relationship with advertising value. Also, in smartphone advertisement, four main dimensions of context awareness value are recognized: identification, activity, timing and location. In this study, we assume that these four constructs has positive relationship with context awareness value. Finally, we propose that advertising value and context awareness value positively influence smartphone advertising attitude. Partial Least Squares (PLS) structural model is used in our theoretical research model to test proposed hypotheses. A well designed survey is conducted for college students in Korea, and reliability, convergent validity and discriminant validity of constructs and measurement indicators are carefully evaluated and the results show that reliability and validity are confirmed according to predefined statistical criteria. Goodness-of-fit of our research model is also supported. In summary, the results collectively suggest good measurement properties for the proposed research model. The research outcomes are as follows. First, information has positive impact on advertising value while entertainment and irritation have no significant impact. Information, entertainment and irritation together account for 38.8% of advertising value. Second, along with the change in advertising market due to the advent of smartphone, activity, timing and location have positive impact on context awareness value while identification has no significant impact. In addition, identification, activity, location and time together account for 46.3% of context awareness value. Third, advertising value and context awareness value both positively influence smartphone advertising attitude, and these two constructs explain 31.7% of the variability of smartphone advertising attitude. The theoretical implication of our research is as follows. First, the influence of entertainment and irritation is reduced which are known to be crucial factors according to previous studies related to advertising value, while the influence of information is increased. It indicates that smartphone users are not likely interested in entertaining effect of smartphone advertisement, and are insensitive to the inconvenience due to smartphone advertisement. Second, in today' ubiquitous computing environment, it is effective to provide differentiated advertising service by utilizing smartphone users'context awareness values such as identification, activity, timing and location in order to achieve competitive business advantage in advertising market. For practical implications, enterprises should provide valuable and useful information that might attract smartphone users by adopting differentiation strategy as smartphone users are sensitive to the information provided via smartphone. Also enterprises not only provide useful information but also recognize and utilize smarphone users' unique characteristics and behaviors by increasing context awareness values. In summary, our result implies that smartphone advertisement should be optimized by considering the needed information of smartphone users in order to maximize advertisement effect.

• Restorative Dentistry and Endodontics
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• v.34 no.3
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• pp.177-183
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• 2009

The aim of this study was to evaluate the marginal and internal gaps in CEREC3 CAD/CAM inlays of three different preparation designs. CEREC3 Inlays of three different preparation designs (n=10) were fabricated according to Group I-conventional functional cusp capping/shoulder preparation, Group II-horizontal reduction of cusps and Group III-complete reduction of cusps/shoulder preparation. After cementation of inlays. the bucco-lingual cross section was performed through the center of tooth. Cross section images of 20 magnifications were obtained through the stereomicroscope. The gaps were measured using the Leica application suite software at each reference point. Statistical analysis was performed using one-way ANOVA and Tukey's test (${\alpha}<0.05$). The marginal gaps ranged from 80.0 to $97.8{\mu}m$ for Group I, 42.0 to $194.8{\mu}m$ for Group II, 51.0 to $80.2{\mu}m$ for Group III. The internal gaps ranged from 90.5 to $304.1{\mu}m$ for Group I, 80.0 to $274.8{\mu}m$ for Group II, 79.7 to $296.7{\mu}m$ for Group III. The gaps of each group were the smallest on the margin and the largest on the horizontal wall. For the CEREC3 CAD/CAM inlays, the simplified designs (groups II and III) did not demonstrate superior results compared to the traditional cusp capping design (group I).

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Land and Housing Review
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• v.12 no.2
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• pp.47-59
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• 2021

This study aims to review the trends of the population outflows in the metropolitan area of Busan and to investigate the factors that affect population out-migration to the Seoul metropolitan area. The following variables are considered for analysis: traditional population movement variables and quality of life variables, such as population, society, employment, housing, culture, safety, medical care, greenery, education, and childcare. The 'domestic population movement data', provided by the MDIS of the National Statistical Office, was used for this research. Out of the total of 57 million population movement data in the period 2012 - 2017, population outmigration from Busan to the Seoul metropolitan area was extracted. Independent variables were drawn from public data sources in accordance with the temporal and spatial settings of the study. The multiple linear regression model was specified based on the dataset, and the fit of the model was measured by the p-value, and the values of Adjusted R2, Durbin-Watson analysis, and F-statistics. The results of the analysis showed that the variables that have a significant effect on population movement from Busan to the Seoul metropolitan area were as follows: 'single-person households', 'the elderly population', 'the total birth rate', 'the number of companies', 'the number of employees', 'the housing sales price index', 'cultural facilities', and 'the number of students per teacher'. More positive (+) influences of the population out-movement were observed in areas with higher numbers of single-person households, lowers proportions of the elderly, lower numbers of businesses, higher numbers of employees, higher numbers of housing sales, lower numbers of cultural facilities, and lower numbers of students. The findings suggest that policies should enhance the environments such as quality jobs, culture, and welfare that can retain young people within Busan. Improvements in the quality of life and job creation are critical factors that can mitigate the outflows of the Busan residents to the Seoul metropolitan area.

• Korean Journal of Heritage: History & Science
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• v.52 no.3
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• pp.38-55
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• 2019

The purpose of this study is to clarify the historical flow of baekdong alloy's usage according to the alloying materials mentioned in document records. For this purpose, we first overviewed the use of copper as a base material for white copper alloys and other types of copper alloys. Baekdong is an alloy of copper and other metals and is currently defined as an alloy of copper and nickel. However, depending on the research subjects and time of the scholars, baekdong may be defined as a metal with over a certain percentage of tin added to copper, or as an alloy of tin, zinc, and lead with copper. There is disagreement regarding the interpretation of this term. Baekdong, which started to appear in the literature of the Three Kingdoms Period, has been steadily seen through the Goryeo and Chosun Dynasties to the modern period. It has been used in various ways, according to each age and culture, from the symbol of the office to trading goods, daily life goods, and money. In the literature, baekdong's alloying material is not only copper and nickel, which are currently defined as alloys, but it is the same in that copper is used as the base metal of the alloy, although it varies slightly from generation to generation. In addition to copper, tin, zeolite, and emerald, zinc and lead also appeared. It was found that baekdong, which means alloy, and baekdong, which means white metal, were mixed. Nickel, which is the alloy material of baekdong as it is currently defined, is a metal with a relatively high discovery time and is widely used as a material for modern industrial fields. Nickel was introduced into Korea at the end of the Joseon Dynasty, but its use is not known in detail. In this study, we examined the acceptance and use of nickel-based baekdong in articles of modern newspapers and in statistical data. Based on the experience of craftsmen, we estimated the period when nickel-based alloys were used in crafts. Material is a direct factor in the development and deterioration of technology, and the development of technology is the basis for the changing of civilizations and cultures. In this context, this study was to investigate baekdong with the material of alloys as a starting point.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.226-231
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• 2011

Ulmus davidiana var. japonica is a deciduous tree species used for traditional medicine. This study was conducted to investigate the variation of samara, seed, germination and growth characteristics among populations and among individuals within five natural populations of U. davidiana var. japonica distributed in Korea. The ten characteristics of samara and seed, the three germination behaviors as well as the two growth traits were studied in samaras collected from total 32 trees. Statistical analysis of all characteristics showed that there were significant differences among populations as well as among individuals within populations. In this study, the mean characteristics of this species were 13.0 mm in samara length, 9.7 mm in samara width, 1.37 in samara index, 0.015 g in samara weight, 3.07 mm in samara stalk length, 3.85 seed length, 2.66 mm in seed width, 1.46 in seed index, 1.29 mm seed thickness, 0.0062 g in seed weigh, 34.8% in germination percentage, 8.6 days in mean germination time, 3.5 ea./day in gemination rate, 37.7 cm in height and 4.90 mm in root collar diameter. Especially, coefficients of variations in samara weight, germination percentage, germination rate, height and root collar diameter were relatively high (${\geq}30.0%$) compared to other traits. There was no significant relationship between population association and geographical distribution. The results of principal component analysis for 15 characteristics showed that primary four principal components (PC's) explained 100% of the total variation. The first PC accounted for 41.8% of the variability which correlated with morphological traits, the second PC accounted for 32.9% of the variability which correlated with germination behaviors and the third PC accounted for 16.3% of the variability which correlated with growth traits.