• Journal of the Korean Institute of Landscape Architecture
• /
• v.45 no.6
• /
• pp.10-27
• /
• 2017

Recently, the importance of recognizing the natural environment and the need for its conservation are increasing due to rapid urbanization. Suncheon Bay, designated as Scenic Site No. 41 and one of the World's Five Greatest Coastal Wetlands, is the only tideland among the tidal flats in Korea, which has salt marsh reserves. It has high conservation value from the ecological aspect. In addition to the Suncheon Bay National Garden, it provides various benefits not only to visitors but to local residents as well in terms of economics, environmental issues, and history and cultural aspects. Two million tourists visit the site annually, which has constantly highlighted the limits of ecological capacity. The valuation of the Suncheon Bay wetland is more important for the sustainability of the Suncheon Bay wetland than for its value as a tourism resource for the activation of the local economy. This study used the Logit model, which is commonly used among probabilistic choice models, to evaluate the economic value of Suncheon Bay wetland with the contingent valuation method(CVM). Applying the conservation value of the Suncheon Bay wetland to the benefit of KRW 8,200 for 1 person and 1 day, the benefit from exploration is KRW 2,050, the management and conservation value is KRW 3,034, and the heritage value is KRW 3,116. The results of this study are that benefit from the annual exploration of Suncheon Bay wetland was KRW 44.3 in billion, the management and conservation value was KRW 6.55 in billion, and the heritage value was KRW 6.73 in billion. When converted to the number of paying visitors per year, the conservation value is about KRW 177.1 billion. This study was conducted to evaluate the use and conservation aspects of the economic value of Suncheon Bay wetland. Based on the latent value of the Suncheon Bay wetland, it provides basic data about the efficient management and policy establishment of Suncheon Bay wetland. The study is significant in that the ecological sustainability of the Suncheon bay wetland and the value of non-marketable were evaluated based on the recognition of 'benefit through exploration', 'management and conservation value' and 'value of heritage'. It can be used as policy decision data on the integrated collection of the admission fee of the Suncheon Bay wetland and Suncheon Bay National Garden.

• Journal of the Korean association of regional geographers
• /
• v.20 no.2
• /
• pp.163-175
• /
• 2014

This study is to explain social capital characteristics of Filipino immigrant women at the level of neighborhood. This research targeted Filipino immigrant women in the metropolis, small town and rural area in Korea to find out the relevance of individual property and characteristics of the community and social capital of neighboring communities- school community, cathedral community, etc- through measurement of the participants' recognition. This study reveals that differences exist in the relationship between length of residence and social capital in the school community and the catholic church community. There is a significant positive relationship between length of residence and political factors in the catholic church community, thereby having a better relationship with longer period of stay, while length of residence and confidence show a negative trend in the school community, leading to less confidence. The catholic church community holds a dominant position in homogeneity, cohesion, and the amount of social capital. According to the findings, social capital 'relation' is more closely related to homogeneity of the community, 'norms' to cohesion. 'Relation and norms' and 'confidence and politics' factors are recognized similarly in both communities, thus resulting in the recognition that decision making within the community, the share of value, and observance of social norms approximate a friendly relationship among members, and satisfaction level, emotional support, and confidence among members approach politics that members can talk about their personal matters. It is noted in the research process that the symbolism of the cathedral community as a transnational circuit behavior occurs where collective culture and personal desires of Filipino immigrant women were combined with production of social capital. Filipino immigrant women's awareness of community and social capital appearing in the cathedral community show that not only residence, along with the cultural identity of Filipino immigrant women, but also collective social and cultural characteristics, such as 'family reunion' can not be overlooked. In particular, at this time when discussion and debate on the interculturalism over multiculturalism is heating up, communal spirit and social capital based on the ethnic identity are important in that they can be a crucial path to the cross-cultural interaction with our society, therefore, a study on the social capital of the ethnic community needs to be encouraged and extended to more diverse communities, to the space of the multilayered scale.

• Archives of design research
• /
• v.18 no.3 s.61
• /
• pp.85-94
• /
• 2005

Most of product designers use 3D CAD system as a inevitable design tool nowadays and many new products are developed through a concurrent engineering process. However, it is very difficult for novice designers to get the sense of reality from modeling objects shown in the computer screens. Such a intangibility problem comes from the lack of haptic interactions and contextual information about the real space because designers tend to do 3D modeling works only in a virtual space of 3D CAD system. To address this problem, this research investigate the possibility of a interactive quantified structure simulation for product design using AR(augmented reality) which can register a 3D CAD modeling object on the real space. We built a quantified structure simulation system based on AR and conducted a series of experiments to measure how accurately human perceive and adjust the size of virtual objects under varied experimental conditions in the AR environment. The experiment participants adjusted a virtual cube to a reference real cube within 1.3% relative error(5.3% relative StDev). The results gave the strong evidence that the participants can perceive the size of a virtual object very accurately. Furthermore, we found that it is easier to perceive the size of a virtual object in the condition of presenting plenty of real reference objects than few reference objects, and using LCD panel than HMD. We tried to apply the simulation system to identify preference characteristics for the appearance design of a home-service robot as a case study which explores the potential application of the system. There were significant variances in participants' preferred characteristics about robot appearance and that was supposed to come from the lack of typicality of robot image. Then, several characteristic groups were segmented by duster analysis. On the other hand, it was interesting finding that participants have significantly different preference characteristics between robot with arm and armless robot and there was a very strong correlation between the height of robot and arm length as a human body.

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

• Journal of Intelligence and Information Systems
• /
• v.18 no.1
• /
• pp.125-141
• /
• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.44 no.3
• /
• pp.1-12
• /
• 2016

This study aimed to interpret the fundamental cause of road-kill occurrences and analyzed spatial characteristics of the road-kill locations from Route 44 in Seorak National Park, Korea. Logistic regression analysis was utilized for backward elimination on variables. Seorak National Park Service has constructed GIS-data of 81 road-kill occurrences from 2008 to 2013 and these data were assigned as dependent variables in this study. Considered as independent variables from previous studies and field surveys, vegetation age-class, distance to streams, coverage of fences and retaining walls, and distance to building sites were assigned as road-kill impact factors. The coverage of fences and retaining walls(-1.0135) was shown as the most influential factor whereas vegetation age-class(0.0001) was the least influential among all of the significant factor estimates. Accordingly, the rate of road-kill occurrence can increase as the distance to building sites and stream becomes closer and vegetation age-class becomes higher. The predictive accuracy of road-kill occurrence was shown to be 72.2% as a result of analysis, assuming as partial causes of road-kill occurrences reflecting spatial characteristics. This study can be regarded as beneficial to provide objective basis for spatial decision making including road-kill occurrence mitigation policies and plans in the future.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.45 no.2
• /
• pp.40-50
• /
• 2017

Compared to the traditional format, the revised IPA is an effective method for selecting a management strategy as compared to the traditional IPA. Comparison between the traditional IPA and revised IPA with a management strategy has been, however, limited. Therefore, the difference between the traditional IPA and revised IPA was compared to select an effective management strategy in the Suncheon Bay Wetland Reserve. First of all, related papers were reviewed to select an appropriate revised IPA. It was found that Deng (2007)'s revised IPA was appropriate for quantifying service quality and a management strategy that affects the measurable satisfaction of visitors in the space. Second, the results of the traditional IPA were compared with the revised IPA in the Suncheon Bay Wetland Reserve and the management strategy of the revised IPA and the changes of management factors were discussed. It was found that some management factors deviated from the order of the quadrant "low priority for managers", "Concentrate management here", "Keep up the good work" were moved to the order of the quadrants "Concentrate management here", "low priority for managers" and "Possible overkill" in the revised IPA grid. The complexity as a management factor resulted in higher demand management than the traditional IPA, which moved from "low priority for managers" to "Concentrate management here". Management factors resulted in lower demand management than the traditional IPA moved from "Concentrate management here" to "low priority for managers"; these consisted of shade trees, exhibition exteriors, programs, and a guided tour. Also, management factors moved from "Keep up the good work" to "Possible overkill" consisted of relaxation facilities, glow of the setting sun, a hedge, and an exhibition interior. Over all, the revised IPA responded properly to changes in the measurable satisfaction of visitors to the Suncheon Bay Wetland Reserve. Therefore, a revised IPA should be provided for accurate and reliable guidelines when decision makers establish management strategies.

• International Journal of Highway Engineering
• /
• v.12 no.4
• /
• pp.93-100
• /
• 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
• /
• v.33 no.12
• /
• pp.941-947
• /
• 2000

Background: Central nervous system complication after coronary artery bypass grafting(CABG) is one of the major prognostic determinants and the use of the cardiopulmonary bypass(CPB) may increase the incidence of this devastating complication. In this study, the outcomes after off-pump CABG were studied and compared with those following the conventional CABG using CPB. Material and Method: Among the consecutive isolated CABG's performed in SNUH during Feb. 1995 and Jun. 1999, 338 coronary artery bypass grafting were divided into two groups. 223 patients underwent CABG using the CPB(Group I), and 115 patients underwent CABG without CPB(OPCAB)(Group II). All patients enrolled in this study received extensive preoperative examinations including thorough neurologic examination before and after surgery, transcranial doppler study, carotid duplex ultrasonography, and magnetic resonance angiography if necessary. Central nervous system(CNS) complications were defined as stroke, seizure, metabolic or hypoxic encephalopathy and transient delirium after surgery. Result: There were 61 cases(27.3%) who developed postoperative CNS complication in Group I, whereas 8 cases(7.0%) of CNS complications developed postoperatively in group II(p<0.05). Statistically significant predictors of postoperative CNS complications in group I were age and the use of cardiac assist devices perioperatively. Conclusion: This study suggested that omitting the use of CPB in CABG resulted in significant decrease of the postoperative CNS complications. OPCAB should be more widely applied especially to the elderly who have preexisting cerebrovascular disease.

• Proceedings of the Korean DIstribution Association Conference
• /
• 2005.11a
• /
• pp.29-51
• /
• 2005

Brands are important in the consumer market. They are the interface between consumers and the company, consumers may develop loyalty to brands. also, The late development of industrial marketing explains the near absence of research on Brand Equity in business to business. With recent change, industrial companies have shifted from a production focus to a customer focus. industrial brand is fast developing. The basic purpose of this study is to investigate industrial brand trust and loyalty affecting the Result of business relationship between industrial buyers and suppliers. Factors hypothesized to influence trust in a brand include a number of brand characteristics, company characteristics and consumer-brand characteristics. This research presented a comprehensive constructive model consisting of components of industrial brand trust and loyalty, and then propose the research model base on prior researches and studies about relationships among components of industrial brand loyalty. Data were gathered from respondents who work in industrial buying center. For this study, Data were analyzed by SPSS 10.0 and AMOS 4.0. The results of this research analysis were as fallow. Industrial brand trust and loyalty were positively related with a number of industrial brand characteristics, supplier characteristics and buyer-brand characteristics. relationship commitment. This research newly proposed the concept of 'industrial brand trust and loyalty affecting the Result of business relationship between industrial buyers and suppliers'