• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.50-59
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• 2015

This study aimed at suggesting Willingness To Pay (WTP) for introduction of the Traffic Accident emergency Call (TAC) system by using Contingent Valuation Method (CVM) which is a general valuation method. As the method, this study suggested a WTP estimation method of the TAC system with the double-bound dichotomous choice model. In previous studies, the data are processed differently according to the type of questions and analysis models used for the calculation of willingness to pay. Therefore, we re-organized the model by the cases using the truncated data sets, and showed the difference in WTPs. The dataset was developed by more than 500 questionnaire obtained from online and offline survey with the consideration of composition ratio by age group referring housing census in 2010 to mitigate regional bias of samples. At last, this study applied various statistical methods, survival analysis, multiple regression, and Tobit model for better interpretation of the questionnaires.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.174-179
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• 2015

Failure Mode and Effects Analysis (FMEA) has been used by Dynamic Positioning (DP) system for risk and reliability analysis. However, there are limitations associated with its implementation in offshore project. 1) since the failure data measured from the SCADA system is missing or unreliable, assessments of Severity, Occurrence, Detection are based on expert's knowledge; 2) it is not easy for experts to precisely evaluate the three risk factors. The risk factors are often expressed in a linguistic way. 3) the relative importance among three risk factors are rarely even considered. To solve these problems and improve the effectiveness of the traditional FMEA, we suggest a Fuzzy-FMEA method for risk and failure mode analysis in Dynamic Positioning System of offshore. The information gathered from DP FMEA report and DP FMEA Proving Trials is expressed using fuzzy linguistic terms. The proposed method is applied to an offshore Dynamic Positioning system, and the results are compared with traditional FMEA.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.63-72
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• 2014

With the rapid development in the economy and other fields as well, the standard of living in South Korea has been improved, and consequently, the demand of automobiles has quickly increased. It leads to various traffic issues such as traffic congestion, traffic accident, and parking problem. In particular, this illegal parking caused by the increase in the number of automobiles has been considered one of the main reasons to bring about traffic congestion as intensifying any dispute between neighbors in relation to a parking space, which has been also coming to the fore as a social issue. Therefore, this study looked into Daejeon Metropolitan City, the city that is understood to have the highest automobile sharing rate in South Korea but with relatively few cases of illegal parking crackdowns. In order to investigate the theoretical problems of the illegal parking, this study conducted a decision-making tree model-based Exhaustive CHAID analysis to figure out not only what makes drivers park illegally when they try to park vehicles but also those factors that would tempt the drivers into the illegal parking. The study, then, comes up with solutions to the problem. According to the analysis, in terms of the influential factors that encourage the drivers to park at some illegal areas, it was learned that these factors, the distance, a driver's experience of getting caught, the occupation and the use time in order, have an effect on the drivers' deciding to park illegally. After working on the prediction model, four nodes were finally extracted. Given the analysis result, as a solution to the illegal parking, it is necessary to establish public parking lots additionally and first secure the parking space for the vehicles used for living and working, and to activate the campaign for enhancing illegal parking crackdown and encouraging civic consciousness.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.1-19
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• 2023

Bikes have recently emerged as an alternative to carbon neutrality. To understand the demand for public bikes, we endeavored to estimate travel frequency of public bike by considering the intermediate stops. Using the GPS trajectory data of 'Ttareungyi', a public bike service in Seoul, we identified a stay point and estimated travel frequency reflecting population, land use, and physical characteristics. Application of map matching and a stay point detection algorithm revealed that stay point appeared in about 12.1% of the total trips. Compared to a trip without stay point, the trip with stay point has a longer average travel distance and travel time and a higher occurrence rate during off-peak hours. According to visualization analysis, the stay points are mainly found in parks, leisure facilities, and business facilities. To consider the stay point, the unit of analysis was set as a hexagonal grid rather than the existing rental station base. Travel frequency considering the stay point were analyzed using the Zero-Inflated Negative Binomial (ZINB) model. Results of our analysis revealed that the travel frequency were higher in bike infrastructure where the safety of bike users was secured, such as 'Bikepath' and 'Bike and pedestrian path'. Also, public bikes play a role as first & last mile means of access to public transportation. The measure of travel frequency was also observed to increase in life and employment centers. Considering the results of this analysis, securing safety facilities and space for users should be given priority when planning any additional expansion of bike infrastructure. Moreover, there is a necessity to establish a plan to supply bike infrastructure facilities linked to public transportation, especially the subway.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.17-33
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• 2005

This paper presents the proposed a classifier of liver cirrhotic step using MR(magnetic resonance) imaging and hierarchical neural network. The data sets for classification of each stage, which were normal, 1type, 2type and 3type, were analysis in the number of data was 231. We extracted liver region and nodule region from T1-weight MR liver image. Then objective interpretation classifier of liver cirrhotic steps. Liver cirrhosis classifier implemented using hierarchical neural network which gray-level analysis and texture feature descriptors to distinguish normal liver and 3 types of liver cirrhosis. Then proposed Neural network classifier learned through error back-propagation algorithm. A classifying result shows that recognition rate of normal is $100\%$, 1type is $82.8\%$, 2type is $87.1\%$, 3type is $84.2\%$. The recognition ratio very high, when compared between the result of obtained quantified data to that of doctors decision data and neural network classifier value. If enough data is offered and other parameter is considered this paper according to we expected that neural network as well as human experts and could be useful as clinical decision support tool for liver cirrhosis patients.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.177-197
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• 2001

This paper is aimed at proposing a data mining-driven analysis to manage the customer defection rate in the bank. After 1997 IMF crisis, Korean banks were suffering from hard-pressed restructuring. At the heart of such restructuring effects, there was the need to manage the customer more effectively than ever. So far, many banks in Korea used to a poor management of customers without any highly-skillful techniques. In line with this argument, we propose several data mining techniques to determine more effective technique far managing customer deflection. We applied three data mining techniques such as logit model, neural network, and C5.0. Experiment data were collected from personal deposit account data of a specific bank in Korea. After experiments, we found that C5.0 showed more robust performance compared to other two techniques. On the basis of those experiment results, we proposed customer defection management policy.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.43-57
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• 2022

In this paper, we propose a method of analyzing research papers published by researchers belonging to university departments in the field of disaster & safety for the scientometric analysis of the research status in the field of disaster safety. In order to conduct analysis research, the dataset constructed in previous studies was newly improved and utilized. In detail, for research papers of authors belonging to the disaster prevention and safety engineering type department of domestic universities, institution identification, cited journal identification of references, department type classification, disaster safety type classification, researcher major information, KSIC(Korean Standard Industrial Classification) mapping information was reflected in the experimental data. The proposed method has a difference from previous studies in the field of disaster & safety and data set based on related keyword searches. As a result of the analysis, the type and regional distribution of organizations belonging to the department of disaster prevention and safety engineering, the composition of co-authored department types, the researchers' majors, the status of disaster safety types and standard industry classification, the status of citations in academic journals, and major keywords were identified in detail. In addition, various co-occurrence networks were created and visualized for each analysis unit to identify key connections. The research results will be used to identify and recommend major organizations and information by disaster type for the establishment of an intelligent crisis warning system. In order to provide comprehensive and constant analysis information in the future, it is necessary to expand the analysis scope and automate the identification and classification process for data set construction.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.35-49
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• 2001

Over the years, cluster analysis has become a popular tool for marketing and segmentation researchers. There are various methods for cluster analysis. Among them, K-means partitioning cluster analysis is the most popular segmentation method. However, because the cluster analysis is very sensitive to the initial configurations of the data set at hand, it becomes an important issue to select an appropriate starting configuration that is comparable with the clustering of the whole data so as to improve the reliability of the clustering results. Many programs for K-mean cluster analysis employ various methods to choose the initial seeds and compute the centroids of clusters. In this paper, we suggest a methodology to evaluate various clustering programs. Furthermore, to explore the usability of the methodology, we evaluate four clustering programs by using the methodology.

• Journal of Construction Engineering and Project Management
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• v.5 no.2
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• pp.44-52
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• 2015

Various approaches have been used in crack preparations and each of the approaches has advantages and disadvantages. Although the routing method has been widely used and seems to be the best approach among the approaches, it is not a complete solution for crack preparation. This paper compares and evaluates a pneumatic crack cleaning device (CCD) developed by Robotics and Intelligent Construction Automation group at Georgia Tech, over existing devices. Surveys were conducted to discover factors that affect the performance of crack/joint preparation work. Then, data for such information were collected via field tests for devices such as router, heat lancer, air blower and CCD. Performed field test results and follow-up interviews demonstrated that the utilization of CCD has potential to offer improvements in productivity, safety, and maintenance cost. An analytic hierarchical procedure (AHP) and economic analyses were conducted. The AHP analysis considered three factors including safety, quality and productivity while the economic analyses examined the alternatives in various ways. The results indicated that the CCD was ranked first and second for the AHP analysis and economic analysis, respectively. In conclusion, the field tests and results revealed that the utilization of CCD achieved satisfactorily in performance, quality, safety and control, and showed that it has high potential in crack cleaning practice.