• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2001.11a
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• pp.59-59
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• 2001

전보에서 발표한 바와 같이, 대상 라이너지 제조공장의 습부 운전조건이 지극히 악화되어 있으며 초지 시스템이 지종교체 등의 충격에 전혀 완충작용을 못하는 이유로 는 용수를 포함한 지료의 전하특성을 조절해주지 못하기 때문인 것으로 판단되었다. 특 히 양이온성 고분자로서 유일하게 사용하고 있는 보류향상제가 적절히 작용하지 못하 기 때문에 보류도가 저하되고, 제품내에 보류되지 못한 다량의 미세분이 백수 중에 존 재함으로서 결과적으로 지료의 전기적 특성을 더욱 악화시키는 악순환이 되풀이 되는 것으로 판단되었다. 이와 같이 강하게 음으로 하전된 지료의 전기적 특성을 조절하기 위해서는 양이온성 고분자의 사용량을 증가시키거나 고분자의 전하밀도 또는 분자량을 변화시켜 보는 것이 일반적인 습부첨가제 사용방법이라고 할 수 있다. 따라서 대상 습부공정의 조업조건을 호전시키기 위해서는 적절한 보류향상시스 템의 적용이 가장 시급한 현안이라고 판단되어 선규 보류제의 현장적용시험을 수행한 결과, 백수의 COD와 미세분이 격감하고 탈수성이 향상되어 습부공정의 운전조건이 호 전됨을 관측할 수 있었다. 그러나 2달 이상에 걸친 보류제 현장적용시험 기간 중에 생 산된 라이너지의 제반 물성들은 별다른 변화를 관측할 수 없었다. 이는 적용된 보류제 의 상당 부분이 계내의 미세분과 작용하여 소모되기 때문으로 판단되었다. 본 연구에서는 보류제의 투입 이전에 보류제와는 상대적으로 저분자량과 고 전 하밀도를 가진 고분자 전해질 4종을 사용하여 라이너지 지료의 전하를 중화시키고자 하였으며, 이러한 공정으로 생산된 라이너지의 물성변화를 관측하였다. 물성으로는 파 열강도, 압축강도, 습윤인장강도 및 염료 고착능력 등을 살펴보았다.시아노에틸화한 PYA가 안정된 분자구조를 유지하고 있음을 확인할 수 있었다. 시아노에틸화한 PYA용액의 점탄성 평가를 위하여 storage modulus와 loss modulus 를 분석하였다. 일반적 유변특성 평가 결과 PYA용액은 shear-thinning, pseudoplastic 한 특성을 나타내어 표면사이즈 공정에서의 적용 가능성을 확인할 수 있었다. 사용하는 통계기법 중의 하나인 주성분회귀분석을 실시하였다. 주성분 분석은 여러 개의 반응변수에 대하여 얻어진 다변량 자료의 다차원적인 변 수들을 축소, 요약하는 차원의 단순화와 더불어 서로 상관되어있는 반응변수들 상호간 의 복잡한 구조를 분석하는 기법이다. 본 발표에서는 공정 자료를 활용하여 인공신경망 과 주성분분석을 통해 공정 트러블의 발생에 영향 하는 인자들을 보다 현실적으로 추 정하고, 그 대책을 모색함으로써 이를 최소화할 수 있는 방안을 소개하고자 한다.금 빛 용사 둥과 같은 표면처리를 할 경우임의 소재 표면에 도금 및 용 사에 용이한 재료를 오버레이용접시킨 후 표면처리를 함으로써 보다 고품질의 표면층을 얻기위한 시도가 이루어지고 있다. 따라서 국내, 외의 오버레이 용접기술의 적용현황 및 대표적인 적용사례, 오버레이 용접기술 및 용접재료의 개발현황 둥을 중심으로 살펴봄으로서 아직 국내에서는 널리 알려지지 않은 본 기 술의 활용을 넓이고자 한다. within minimum time from beginning of the shutdown.및 12.36%, $101{\sim}200$일의 경우 12.78% 및 12.44%, 201일 이상의 경우 13.17% 및 11.30%로 201일 이상의 유기의 경우에만 대조구와 삭

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.809-813
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• 2004

강우시 유역 내에서 발생하는 수문특성을 구명하고자 하는 연구는 지속적으로 진행되고 있다. 특히 최근 몇년간 집중호우로 인한 홍수피해가 매우 실각한 수준으로 발생하였고, 이에 지방 소하천을 포함한 전국의 하천정비사업이 새로운 설계홍수빈도를 토대로 진행되고 있다. 우리나라의 강우특성은 여름철에 편중되는 특성을 지니고 있어 홍수시의 홍수방어 대책 등 치수에 많은 어려움이 있는 것이 현실이다. 집중호우로 인한 피해는 전 세계적인 문제로 제기되고 있으며, 이에 강우-유출 관계를 규명하고자 하는 노력이 지속적으로 이루어지고 있다. 강우-유출과정은 시간적, 공간적 다변성을 지닌 수문학적 인자에 의해 좌우되기 때문에 이러한 문제를 해결하기 위해 다년간의 강우-유출 자료를 바탕으로 알고리즘을 생성하고, 이를 바탕으로 정확한 모의가 가능한 수문 모형 및 시스템들을 개발하는데 노력을 기울이고 있다(심순보 등, 1998, 신사철 등, 2002). 그러나 이러한 모형들은 많은 매개변수와 다양한 정보들을 필요로 하게 되어 이들을 처리하는데 많은 어려움이 따른다. 따라서 최근에는 GIS(Geographical Information System)를 활용하여 유역과 분수계를 결정하고 하천형태학적인 특성인자를 추출하는 자동화된 유역정보 추출기술 개발에 대한 관심이 집중되고 있다(Bhaskar, 1992, Francisco, 1995, Yeon, 1999). 이에 본 연구에서는 GIS기법을 이용하여 지형자료로부터 하천연장, 배수면적, 지체시간, 도달시간 등 유역내의 분포형 수문매개변수를 추출하였고 추출된 매개변수를 통해 강우-유출식을 적용하여 분포형 유출량을 산정하는데 활용하고자 한다.ansverse Mercatro) 지구좌표계의 DEM 자료로 변환하였다. 또한 유역의 고도차를 이용한 흐름특성 분석을 위해 수치고도자료를 이용하여 유역흐름특성을 분석할 수 있는 TOPAZ(Topographic PArameteri-Zation) 프로그램을 이용하였다. TOPAZ 프로그램을 통해 분석된 각 격자별 분포형 수문 매개변수는 적합한 관계식을 통해 분포형 유출량을 모의하는데 적용된다.다 정확한 유입량 예측이 가능할 것으로 사료된다.이 작은 오차를 발생하였으며, 전체적으로 퍼프 모형이 입자모형보다는 훨씬 적은 수의 계산을 통해서도 작은 오차를 나타낼 수 있다는 것을 알 수 있었다. 그러나 Gaussian 분포를 갖는 퍼프모형은 전단흐름에서의 긴 유선형 농도분포를 모의할 수 없었고, 이에 관한 오차는 전단계수가 증가함에 따라 비선형적으로 증가하였다. 향후, 보다 다양한 흐름영역에서 장${\cdot}$단점 분석 및 오차해석을 수행한 후에 각각의 Lagrangian 모형의 장점만을 갖는 모형결합 방법을 제시할 수 있을 것으로 판단된다.mm/$m^{2}$로 감소한 소견을 보였다. 승모판 성형술은 전 승모판엽 탈출증이 있는 두 환아에서 동시에 시행하였다. 수술 후 1년 내 시행한 심초음파에서 모든 환아에서 단지 경등도 이하의 승모판 폐쇄 부전 소견을 보였다. 수술 후 조기 사망은 없었으며, 합병증으로는 유미흉이 한 명에서 있었다. 술 후 10개월째 허혈성 확장성 심근증이 호전되지 않아 Dor 술식을 시행한 후 사망한 예를 제외한 나머지 6명은 특이 증상 없이 정상 생활 중이다 결론: 좌관상동맥 페동맥이상 기시증은 드물기는 하나, 영유

• Tuberculosis and Respiratory Diseases
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• v.53 no.4
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• pp.379-388
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• 2002

Background : Although patients with stage IV non-small cell lung cancer are known to have a poor prognosis, the prognostic factors for survival have not been well evaluated. Such factors may be different from those for overall survival. This study was performed to analyze the prognostic factors for survuval and the variation of survival according to metastatic organ, in patients with stage IV non-small cell lung cancer. Materials and Methods : From January 1997 to December 2000, 151 patients with confirmed stage IV non-small cell lung cancer were enrolled into this study retrospectively. The clinical and laboratory data were analyzed using univareate Kaplan-Meied and Multivariate Cox regression models. Results : On univariate analysis, age, performance status, serum albumin level, weight loss, forced expiratory volume in one second (FEV1), systemic chemotherapy, the number of metastatic organs and serum lactate dehydrogenase (LDH) level were significant factors (p<0.05). In multivariate analysis, important factors for survival were ECOG performance (relative risk of death [RR]: 2.709), systemic chemotherapy (RR: 1.944), serum LDH level (RR: 1.819) and FEV1 (RR: 1.774) (p<0.05), Metastasis to the brain and liver was also a significant factor on univariate analysis). The presence of single lung metastasis was associated with better survival than that of other metastatic organs (p=0.000). Conclusion : We confirmed that performance status and systemic chemotherapy were independent prognostic factors, as has been recognized. The survival of stage IV non-small cell lung cancer patients was different according to the metastatic organs. Among the metastatic sites, only patients with metastasis to the lung showed bettrer survival than that of other sites, while metastasis of the brain or liver was associated with worse survival than that of other sites.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.157-176
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• 2016

For a long time, mobile phone had a sole function of communication. Recently however, abrupt innovations in technology allowed extension of the sphere in mobile phone activities. Development of technology enabled realization of almost computer-like environment even on a very small device. Such advancement yielded several forms of new high-tech devices such as smartphone and tablet PC, which quickly proliferated. Simultaneously with the diffusion of the mobile devices, mobile applications for those devices also prospered and soon became deeply penetrated in consumers' daily lives. Numerous mobile applications have been released in app stores yielding trillions of cumulative downloads. However, a big majority of the applications are disregarded from consumers. Even after the applications are purchased, they do not survive long in consumers' mobile devices and are soon abandoned. Nevertheless, it is imperative for both app developers and app-store operators to understand consumer behaviors and to develop marketing strategies aiming to make sustainable business by first increasing sales of mobile applications and by also designing surviving strategy for applications. Therefore, this research analyzes consumers' mobile application usage behavior in a frame of substitution/supplementary of application categories and several explanatory variables. Considering that consumers of mobile devices use multiple apps simultaneously, this research adopts multivariate probit models to explain mobile application usage behavior and to derive correlation between categories of applications for observing substitution/supplementary of application use. The research adopts several explanatory variables including sociodemographic data, user experiences of purchased applications that reflect future purchasing behavior of paid applications as well as consumer attitudes toward marketing efforts, variables representing consumer attitudes toward rating of the app and those representing consumer attitudes toward app-store promotion efforts (i.e., top developer badge and editor's choice badge). Results of this study can be explained in hedonic and utilitarian framework. Consumers who use hedonic applications, such as those of game and entertainment-related, are of young age with low education level. However, consumers who are old and have received higher education level prefer utilitarian application category such as life, information etc. There are disputable arguments over whether the users of SNS are hedonic or utilitarian. In our results, consumers who are younger and those with higher education level prefer using SNS category applications, which is in a middle of utilitarian and hedonic results. Also, applications that are directly related to tangible assets, such as banking, stock and mobile shopping, are only negatively related to experience of purchasing of paid app, meaning that consumers who put weights on tangible assets do not prefer buying paid application. Regarding categories, most correlations among categories are significantly positive. This is because someone who spend more time on mobile devices tends to use more applications. Game and entertainment category shows significant and positive correlation; however, there exists significantly negative correlation between game and information, as well as game and e-commerce categories of applications. Meanwhile, categories of game and SNS as well as game and finance have shown no significant correlations. This result clearly shows that mobile application usage behavior is quite clearly distinguishable - that the purpose of using mobile devices are polarized into utilitarian and hedonic purpose. This research proves several arguments that can only be explained by second-hand real data, not by survey data, and offers behavioral explanations of mobile application usage in consumers' perspectives. This research also shows substitution/supplementary patterns of consumer application usage, which then explain consumers' mobile application usage behaviors. However, this research has limitations in some points. Classification of categories itself is disputable, for classification is diverged among several studies. Therefore, there is a possibility of change in results depending on the classification. Lastly, although the data are collected in an individual application level, we reduce its observation into an individual level. Further research will be done to resolve these limitations.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.27-32
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• 1997

Continual monitoring of abnormal operating conditions i a key issue in maintaining high product quality and safe operation, since the undetected process abnormality may lead to the undesirable operations, finally producing low quality products, or breakdown of equipment. The statistical projection method recently highlighted has the advantage of easily building reference model with the historical measurement data in the statistically in-control state and not requiring any detailed mathematical model or knowledge-base of process. As the complexity of process increases, however, we have more measurement variables and recycle streams. This situation may not only result in the frequent occurrence of process Perturbation, but make it difficult to pinpoint trouble-making causes or at most assignable source unit due to the confusing candidates. Consequently, an ad hoc skill to monitor and diagnose in plat-wide scale is needed. In this paper, we propose a hierarchical plant-wide monitoring methodology based on hierarchical decomposition and principal component analysis for handling the complexity and interactions among process units. This have the effect of preventing special events in a specific sub-block from propagating to other sub-blocks or at least delaying the transfer of undesired state, and so make it possible to quickly detect and diagnose the process malfunctions. To prove the performance of the proposed methodology, we simulate the Tennessee Eastman benchmark process which is operated continuously with 41 measurement variables of five major units. Simulation results have shown that the proposed methodology offers a fast and reliable monitoring and diagnosis for a large scale chemical plant.

• Journal of the Society of Disaster Information
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• v.14 no.1
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• pp.51-58
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• 2018

The purpose of this study is to propose a method to construct the information space of library with the resilient space and community base to cope with social changes in response to various contents of library functions. As an institution that provides the places and contents necessary for education and research, it aims to expand and change from the collection center to the user center, from the specific user center to the library that shares resources with the community, And to find ways to contribute to the revitalization of education. A library plan for communities that can increase the value of local libraries and expand user-centered space utilization is as follows; First, in terms of communities in space and programs, the program will be supplemented through the activation of cultural, entertainment, and collaborative programs and the creation of communities. Second, in terms of smart support for operational and environmental issues, the establishment of information technology and smart management operating system to expand the research productivity by efficiently utilizing mutually available data with the local community.

• Management & Information Systems Review
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• v.34 no.3
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• pp.125-140
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• 2015

KOSDAQ market reorganized their division system from two types to four types of division departments such as blue chip, venture, medium, and technology development departments in 2011. However, under the current new division system, financially unhealthy firms attempting to take advantage of the classifying opportunity of blue chip department are likely to engage in pernicious earnings management. The objective of this study is to investigate the earnings management behavior surrounding the time of KOSDAQ firms entering the blue chip department via new division system. More specifically, we test whether the firms classified blue chip department tend to engage in upward earnings management using accruals and real activities before and after they achieve blue chip status. In this study, we analyzed 111 firms classified blue chip department in 2011 according to new division system in KOSDAQ market. Major test results indicate that firms entering the blue chip department according to current KOSDAQ division system in general, tend to inflate reported earnings by means both of accruals and real activities right before the entering year. This result suggests that the firms classified blue chip department engage in opportunistic earnings management with a view to uplifting their market values. Our study is expected to provide clues useful for searching policy directions which intend to ameliorate adverse side effects of the current KOSDAQ division system. In sum, the regulatory authorities and enforcement bodies need to exercise caution in deliberating more stringent review procedures so that financially healthy and promising candidates are properly segregated from their poor and risky counterparts, thus enhancing the beneficial effects, while mitigating adverse side effects of the system.