• Journal of Information Management
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• v.37 no.1
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• pp.1-16
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• 2006

Due to the dramatic development of the Internet, the ICT market has changed from a voice based services to data based services. Substitution and complementary dynamism has emerged from communication technology services such as the Internet, mobile phone, and telephone. This paper analyses diffusion patterns of communication technologies such as the Internet, cellular phones, and telephones in different country groups. We estimate modified logistic growth model using time series data for the years 1975-2002. As a result, it is possible to categorize country groups according to the patterns of diffusions. This research creates essential information to forecast demand for new services based on incumbent services as well as provide information on strategies for entering the network industry.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.801-805
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• 2009

하천의 골재는 도로, 항만, 주택건설 등 다양한 건설재료로 이용되어 왔으며 급속한 산업화와 더불어 국가건설사업, 지방자치단체의 수익사업으로 인해 골재 수요가 급격히 증가해 왔다. 그러나 체계적인 조사 및 연구가 없이 무분별하게 행해진 하천준설은 하상저하, 하천의 불안정, 하상의 장갑화 등 물리적 영향뿐만 아니라 주변 하천의 식생, 저서생물의 서식처 등 수서생태계의 파괴, 하천정화능력 저하, 교각의 노출 등 생물적 경제적인 측면에서 악영향을 미치고 있다. 본 연구에서는 하천준설사업이 꾸준히 진행 중에 있는 낙동강 유역의 감천을 연구대상지역으로 선정하여, 2차원 수치모형인 RMA-2를 이용하여 준설에 따른 동수역학적 흐름특성의 변화를 모의하고, SED2D를 이용하여 단기하상변동을 예측하였다. 지형자료는 수치지도 및 실측자료를 토대로 하여 구축하였으며, 준설 전 후의 하상변동을 모의해 비교 분석한 결과, 하도중심에서의 흐름특성은 준설전에 비해 수위가 평균 0.85 m 저하되었고, 하상은 평균 0.56 m 저하되었다. 대상유역의 수위와 유속은 준설전에 비해 준설후에 안정적인 특성을 나타냈다. 또한, 준설전 후 전반적으로 하상상승이 발생하였으며 이는 상류에서의 유사유입량이 많아 퇴적이 많이 일어난 것으로 판단되며, 준설후는 준설전에 비해 하류에서의 퇴적량이 적게 나타났으며 이는 준설구간에서의 퇴적으로 인해 하류 유사이송이 적게 일어난 것으로 판단된다. 본 연구의 결과는 하천준설에 따른 하천환경 및 물리적 영향을 최소화하고, 교란하천의 복원 및 관리 등에도 활용될 수 있을 것이다.

• Korean Journal of Social Welfare Studies
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• no.39
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• pp.5-28
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• 2008

The purpose of this study is two-fold: 1) to identify different adolescent smoking trajectories in Korea; and 2) to examine predictors of those smoking trajectories within a social developmental frame. Data were from the Korea Youth Panel Survey(KYPS), a longitudinal study of 3,449 youths followed since 2003. Using semi-parametric group-based modeling, four smoking trajectories were identified: non initiators, late onsetters, experimenters, and escalators. Multinomial logistic regressions were then used to identify risk and protective factors that distinguish the trajectory groups from one another. Among non smokers at age 13, late onsetters were distinguished from non initiators by a variety of factors in every ecological domain. Among youths who already smoked at age 13, escalators who increased their smoking were distinguished from experimenters who almost desisted from smoking by age 17 by self-esteem and academic achievement. Finally, implications for youth welfare practice from this study were discussed.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.37-51
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• 2009

One of the critical issues in the management of manufacturing companies is the efficient process of planning and operating service resources such as human, parts, and facilities, and it begins with the accurate service demand forecasting. In this research, service and sales data from the LCD monitor manufacturer is considered for an empirical study on Product Life Cycle (PLC) based service demand forecasting. The proposed PLC forecasting approach consists of four steps : understanding the basic statistics of data, clustering models using a self-organizing map, developing respective forecasting models for each segment, comparing the accuracy performance. Empirical experiments show that the PLC approach outperformed the traditional approaches in terms of root mean square error and mean absolute percentage error.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.10a
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• pp.147-148
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• 1999

대기 화산 모형은 유독 화학 물질이나 방사능 물질 누출 사고시, 방재 대응에 매우 중요한 도구로 사용될 수 있다. 이런 목적을 위해 미국, 유럽 등에서는 1980년을 전후하여 모형 체계 개발에 착수하였고, 현재는 실용화되어 현업에서 운용되고 있다(Lee, et. al, 1997; J. Ehrhardt, 1998). 국내에서는 원자력 안전 기술원을 중심으로 원자력 발전소 주변 반경 십여 km지역에 위치한 기상청의 자동 종합 기상 측정 장치(AWS, Automatic Weather Station)의 실측 바람장을 기반으로 확산 예측을 수행할 수 있는 시스템을 운용하고 있다(원자력안전기술원, 1999).(중략)

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2007.05a
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• pp.49-51
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• 2007

본 연구에서는 NASA에서 제공하는 지표면 온도자료인 MODIS MOD11 위성영상을 이용하여, 대기역학모형을 이용하여 1963년, 2002년의 대구지방 토지이용에 따른 지표면 열환경의 변화를 살펴보았다. 수치실험의 경우 고도에 따른 온도벼화가 나타났으며 특히 실제 대구도시지 내의 온도가 높게 나타났다. 그리고 1963년과 2002년의 도시화 정도에 따른 온도변화도 나타났다. 위성에서 관측하 지표면 온도는 한반도를 지나는 시간이 오전 11임에도 불구하고 대구 중심으로 높게 나타나는 것으로 조사되었으며, 팔공산과 앞산으로는 낮은 지표면 온도가 산출되었다. 모형수치는 위성영상과 비교하여 다소 낮게 산출되었으나 전체적인 분포는 잘 일치하고 있다. 그러므로 위성자료에서 측정한 기온은 지표면 토지이용도 분석과 그에 따른 지표면 열변화를 예측 분석하는데 주요한 지표가 될 수 있다.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.375-376
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• 2017

본 연구는 은퇴 후 신체적 건강이 시간이 지남에 따라 어떤 변화양상을 나타내는지, 은퇴 후 신체적 건강에 영향을 주는 것으로 알려진 은퇴 특성(은퇴시점, 은퇴자발성 여부) 변인이 은퇴 후 신체적 건강의 궤적에 어떠한 영향을 미치는지를 분석하고자 하는 목적으로 실시되었다. 분석 자료는 국민노후보장패널 3차시(2009년), 4차시(2011년), 5차시(2013년) 종단자료이며, 2,857명을 대상으로 잠재성장모형 분석을 실시하였다. 연구결과, 첫째, 중 고령은퇴자의 신체적 건강은 시간의 흐름의 변화를 알아보기 위해 무조건부 모형을 통해 확인한 결과, 중 고령은퇴자들은 시간이 지남에 따라 더 높은 수준의 ADL 값을 갖는 것으로 나타났다. 둘째, 중 고령은퇴자의 신체적 건강변화와 관련이 있는 은퇴특성은 무엇인지 조건부 모형을 통해 알아본 결과, 은퇴 특성 변수로는 조기은퇴자일 때, 은퇴가 비자발적일 때, 건강상 이유로 은퇴했을 때 더 높은 ADL 초기값을 보였다. 또한 변화궤적 영향요인을 살펴본 결과, 은퇴특성에서는 어떤 변수도 유의성을 보이지 않았고, 인구사회학적 변수 중에서는 연령이 높을수록, 소득이 높을수록 ADL의 증가속도가 더욱 가파른 것으로 나타났다.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Korea Port Economic Association
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• v.30 no.3
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• pp.1-14
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• 2014

This paper aims at measuring how new information is incorporated into volatility estimates. Various GARCH models are compared and estimated with daily BDI(Baltic Dry Index) data. While most researchers agree that volatility is predictable, they differ on how this volatility predictability should be modelled. This study, hence, introduces the asymmetric or leverage volatility models, in which good news and bad news have different predictability for future. We provide the systematic comparison of volatility models focusing on the asymmetric effect of news on volatility. Specifically, three diagnostic tests are provided: the sign bias test, the negative size bias test, and the positive size bias test. From the Ljung-Box test statistic for twelfth-order serial correlation for the level we do not find any significant serial correlation in the unpredictable BDI. The coefficients of skewness and kurtosis both indicate that the unpredictable BDI has a distribution which is skewed to the left and significantly flat tailed. Furthermore, the Ljung-Box test statistic for twelfth-order serial correlations in the squares strongly suggests the presence of time-varying volatility. The sign bias test, the negative size bias test, and the positive size bias test strongly indicate that large positive(negative) BDI shocks cause more volatility than small ones. This paper, also, shows that three leverage models have problems in capturing the correct impact of news on volatility and that negative shocks do not cause higher volatility than positive shocks. Specifically, the GARCH model successfully reveals the shape of the news impact curve and is a useful approach to modeling conditional heteroscedasticity of daily BDI.