• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.99-118
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• 2015

Object-oriented programming languages have been widely selected for developing modern information systems. The use of concepts relating to object-oriented (OO, in short) programming has reduced efforts of reusing pre-existing codes, and the OO concepts have been proved to be a useful in interpreting system requirements. In line with this, we have witnessed that a modern conceptual modeling approach supports features of object-oriented programming. Unified Modeling Language or UML becomes one of de-facto standards for information system designers since the language provides a set of visual diagrams, comprehensive frameworks and flexible expressions. In a modeling process, UML users need to consider relationships between classes. Based on an explicit and clear representation of classes, the conceptual model from UML garners necessarily attributes and methods for guiding software engineers. Especially, identifying an association between a class of part and a class of whole is included in the standard grammar of UML. The representation of part-whole relationship is natural in a real world domain since many physical objects are perceived as part-whole relationship. In addition, even abstract concepts such as roles are easily identified by part-whole perception. It seems that a representation of part-whole in UML is reasonable and useful. However, it should be admitted that the use of UML is limited due to the lack of practical guidelines on how to identify a part-whole relationship and how to classify it into an aggregate- or a composite-association. Research efforts on developing the procedure knowledge is meaningful and timely in that misleading perception to part-whole relationship is hard to be filtered out in an initial conceptual modeling thus resulting in deterioration of system usability. The current method on identifying and classifying part-whole relationships is mainly counting on linguistic expression. This simple approach is rooted in the idea that a phrase of representing has-a constructs a par-whole perception between objects. If the relationship is strong, the association is classified as a composite association of part-whole relationship. In other cases, the relationship is an aggregate association. Admittedly, linguistic expressions contain clues for part-whole relationships; therefore, the approach is reasonable and cost-effective in general. Nevertheless, it does not cover concerns on accuracy and theoretical legitimacy. Research efforts on developing guidelines for part-whole identification and classification has not been accumulated sufficient achievements to solve this issue. The purpose of this study is to provide step-by-step guidelines for identifying and classifying part-whole relationships in the context of UML use. Based on the theoretical work on Meta-model Formalization, self-check forms that help conceptual modelers work on part-whole classes are developed. To evaluate the performance of suggested idea, an experiment approach was adopted. The findings show that UML users obtain better results with the guidelines based on Meta-model Formalization compared to a natural language classification scheme conventionally recommended by UML theorists. This study contributed to the stream of research effort about part-whole relationships by extending applicability of Meta-model Formalization. Compared to traditional approaches that target to establish criterion for evaluating a result of conceptual modeling, this study expands the scope to a process of modeling. Traditional theories on evaluation of part-whole relationship in the context of conceptual modeling aim to rule out incomplete or wrong representations. It is posed that qualification is still important; but, the lack of consideration on providing a practical alternative may reduce appropriateness of posterior inspection for modelers who want to reduce errors or misperceptions about part-whole identification and classification. The findings of this study can be further developed by introducing more comprehensive variables and real-world settings. In addition, it is highly recommended to replicate and extend the suggested idea of utilizing Meta-model formalization by creating different alternative forms of guidelines including plugins for integrated development environments.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.93-100
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• 2010

Trip generation is the first step in the conventional four-step model and has great effects on overall demand forecasting, so accuracy really matters at this stage. A linear regression model is widely used as a current trip generation model for such plans as urban transportation and SOC facilities, assuming that the relationship between each socio-economic index and trip generation stays linear. But when rapid urban development or an urban planning structure has changed, socio-economic index data for trip estimation may be lacking to bring many errors in estimated trip. Hence, instead of assuming that a socio-economic index widely used for a general purpose, this study aims to develop a new trip generation model by type based on the market separation for the variables to reflect the characteristics of various zones. The study considered the various characteristics (land use, socio-economic) of zones to enhance the forecasting accuracy of a trip generation model, the first-step in forecasting transportation demands. For a market separation methodology to improve forecasting accuracy, data mining (CART) on the basis of trip generation was used along with a regression analysis. Findings of the study indicated as follows : First, the analysis of zone characteristics using the CART analysis showed that trip production was under the influence of socio-economic factors (men-women relative proportion, age group (22 to 29)), while trip attraction was affected by land use factors (the relative proportion of business facilities) and the socio-economic factor (the relative proportion of third industry workers). Second, model development by type showed as a result that trip generation coefficients revealed 0.977 to 0.987 (trip/person) for "production" 0.692 to 3.256 (trip/person) for "attraction", which brought the necessity for type classifications. Third, a measured verification was conducted, where "production" and "attraction" showed a higher suitability than the existing model. The trip generation model by type developed in this study, therefore, turned out to be superior to the existing one.

• Journal of Chest Surgery
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• v.39 no.3 s.260
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• pp.177-183
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• 2006

Background: The bidirectional cavopulmonary shunt (BCPS) is one of the primary palliative procedures for complex congenital heart disease. It has many advantages, but it is known to have high risks in young infants. Material and Method: From 1995 to 2003, 48 infants under the age of one year underwent BCPS. All the patients were Fontan candidates due to functional univentricular heart physiology. There were no significant differences in preoperative variables, except in mean age (67.58$\pm$3.78 vs. 212.91$\pm$13.44 days), and mean body weight (4.51$\pm$0.29 vs. 6.62$\pm$0.27 kg), between group A (<3 months, n=12) and group B ($\ge$3 months, n=36). Result: In group A, the arterial oxygen saturations serially measured were significantly lower. Hospital mortality was $25\%$, and $19\%$, respectively. During follow up, there were 2 late mortalities in group A, and 5 in group B. Conclusion: This study showed that operative risk in young infants was comparable to that of older patients, and BCPS could be a good option as a primary palliative procedure, and may eliminate other repeated palliative procedures which could be the risk factors for Fontan candidates. However, in high-risk patients accompanying pulmonary hypertension, or heterotaxia syndrome, other palliative procedures should be considered.

• Radiation Oncology Journal
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• v.9 no.2
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• pp.277-284
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• 1991

Survival data, prognostic factors, and patterns of failure were retrospectively analyzed for a total of 76 patients with adenocarcinoma of the uterine cervix treated between January 1981 and December 1987, which represents $4.1\%$ of all primary cervical carcinomas treated, at Department of Radiation Oncology, Yensei Cancer Center, Yonsei University College of Medicine. The mean age of the patients was 49 years (range, $27\~79$ years) and the peak incidence was in the group 50 to 59 years of age. More half of the patients were postmenopausal (46/76= $60.5\%$). Most patients ($76\%$) had abnormal vaginal bleeding either alone or in combination with other symptoms. The proportion of stage IIb was $43.4\%$. There were 4 major histologic subtypes: pure adenocarcinoma (48/76=$63.2\%$), adenosquamous carcinoma (20/76=$26.3\%$), papillary (5/76=$6.6\%$) and clear cell carcinoma (3/76=$3.9\%$). Of the many clinicopathologic variables evaluated for prognosis, the most significant prognostic factors were stage of disease and the size of tumor. The overall 5-year survival rate was $68\%$, and the 5-year survival rates for stage Ib, II and III were $90\%,\;66\%\;and\;54\%$, respectively. Control of pelvic tumors was achieved in $93.8\%,\;90.2\%\;and\;50.0\%$ of cases of stage Ib, II and III disease, respectively. In present study, treatment modalities (radiation therapy alone/combined operative and radiation therapy) did not affect the local control of tumor and the survival.

• Journal of Preventive Medicine and Public Health
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• v.28 no.4 s.51
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• pp.795-807
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• 1995

This study was carried out from 1993 to 1994 in the small industries in Incheon. The objectives of this study was in order to estimate the quitting rate, to identify its relating factors and to propose effective quitting management policy in the small industries. The results were as follows ; 1. The quitting rate of 266 study workers was 42.1%(112 workers). 2. Age, working duration, position, marrital status were significant difference between the quitting group and the non - quitting group. In the quitting group, mean age was young, working duration was short, general employees and unmarried workers were many compared with the non - quitting group. 3. In the industry characteristics, total assets, total assets, sales per person, establishment duration and occupational health and safely status were significant difference between the quitting group and the non - quitting group. In the quitting group, total assets, total sales and sales per person were little, establishment duration of company was short and occupational health and safety status were poor compared with the non - quitting group. 4. In the quitting group, worker's response to employer's disposal about health and safety was more passive and the relation to employer with employee was significantly poor compared with the non - quitting group. 5. Multiple logistic regression analysis of quitting against family income per person, working duration, relation to employer with employee, occupational health and safety status in industry, worker's response to employer's disposal about health and safety and sales per person was done. Working duration, occupational health and safety status, worker's response to employer'1 disposal about health and safety were significant explainatory variables for quitting. Above results showed that the quitting rate was high and it was significant difference between the quitting group and non : quitting group according to characteristics of workers and of industries. Especially, it suggested that working duration, occupational health and safety status and worker's response to employer's disposal about health and safety were significant quitting factor. Therefore, it should be reflected in the quitting management and the policy of steady employment.

• Korean Journal of Ecology and Environment
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• v.42 no.1
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• pp.1-8
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• 2009

This study used a mesocosm which presumes movement of the nutrient (especially $PO_{4^-}P$) in the wetland. After setting up the mesocosm inside the wetland and adding the $PO_{4^-}P$, observed the movement of the $PO_{4^-}P$ every hour. We analyzed the variables which had the possibility of affecting $PO_{4^-}P$ concentration in the wetland-flora, absorbing rate of algae, settling rate, release rate. Immediately after adding $PO_{4^-}P$, the concentration of the TP in water column at each mesocosm was 0.48, 12.4, 20.4, $23.6\;mg\;L^{-1}$, after 21 days they were 0.6, 1.92, 6.97 and $6.94\;mg\;L^{-1}$ respectively. The concentration of the TP in water column at the mesocosm decreased on average 73.7%. The concentration of the $PO_{4^-}P$ inside reed, algae and sediment in the mesocosm was increased from $0.73mg\;gDW^{-1}$, $3.81mg\;gDW^{-1}$, $466.1mg\;kg^{-1}$ to $0.83mg\;gDW^{-1}$, $4.57mg\;gDW^{-1}$ and $813.3mg\;kg^{-1}$ respectively. Algae is more sensitive than reeds in absorption of the nutrient. TP removal by settling was highest. Budgeting of TP indicated that P moved from particulates in the water column to sediment and algae. Immediately after adding $PO_{4^-}P$, water column (24.2%) and sediment (49.0%) dominated TP storage, with algae (10.3%) and reed (16.4%) holding smaller proportions of TP. After 21 days, Sediment (59.0%) and algae (17.9%) dominated TP storage, with water column (7.1%) and reed (15.8%) holding smaller proportions of TP. Estimation of phosphate movement using mesocosms is an appropriate method because wetlands have many controlling factors. Analysed data can be compared to background data for wetland construction and management.

• Korean journal of food and cookery science
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• v.30 no.6
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• pp.670-678
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• 2014

The purpose of this study was to find the optimal swelling method and condition for seacucumber to improve its taste and texture to accomodate the rapid increase of consumption. Another purpose was to try to determine an easy way to soak dried sea-cucumber under different conditions, and identify the influence of swelling time on the texture of sea-cucumber, in order to reduce preparation time and provide basic data for easy handling. After boiling or steaming for six different periods including 5, 15, 30 and 60 minutes the texture of the sea-cucumbers were compared, For the additive test, the sea-cucumbers were boiling for 30 minutes period with 4 different additives and the textures were compared, Since the texture is an important characteristic of sea-cucumber, there are many variables that affect this property including the, drying and preservation methods. This study provides basic understanding of the influence of the heating method, time and temperature on the swelling of sea-cucumber for handy use at processing sites.

• 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.

• Korean Journal of Psychosomatic Medicine
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• v.23 no.1
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• pp.36-46
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• 2015

Objectives: To understand the risk factors of demographic data in geriatric depression scale, and suicidality among in elderly who live alone at one urban region. Methods:In 2009, 589 elderly who live alone(age${\geq}$65) were carried out a survey about several socio-demographic data, Korean version of the Geriatric Depression Scale(SGDS-K) and Suicidal Ideation Questionnaire (SIQ). Statistical analysis was performed for the collected data. Results: Mean age of elderly who live alone is 75.69(SD 6.17). 40.1% of participants uneducated, 31.4% graduate from elementary school, 12.9% graduate from high school, 11.7% graduate from middle school, 3.2% graduate from university. Religionless, having past history of depression or physical diseases, low subjective satisfaction of family situation, and not having any social group activity have significance to depressive symptoms of elderly who live alone. Having past history of depression, religionless, low subjective satisfaction of family situation have significance to suicidality. Especially, low subjective satisfaction of family situation and having past history of depression are powerful demographic factor both depressive symptoms and suicidality of elderly who live alone. Conclusions: When we take care elderly who live alone, we should consider many things, but especially the social support network such as family satisfaction and past history of depression for reducing or preventing their depression and suicide both elderly depression and suicide who live alone.