• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.43-50
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• 2015

Google, Yahoo, Daum and Naver has the POI(point of interest) service. And POI on the map is expending to social commerce, SNS, social game and social shopping. At the same time the uses's position on the map is the starting point of the Humanities Story. That means our current position is the place for stories of tales, children's song, fictional characters, the film background, lyrics and the birth of great people. This study points out that service has the limited to cafe, restaurant and hospital, and suggests the Humanities fusion Map Service which is combined with the POI information.

• Women's Health Nursing
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• v.8 no.4
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• pp.608-618
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• 2002

The purpose of this study is to analyze the type of ego states and stress coping style on female college students who are in the course of nursing study. This study is performed in the view of Transactional Analysis and designed to scrutinize descriptive correlations between the type of ego states and stress coping style. The subject is consists of 144 freshmen and sophomore, 138 junior and senior students group, who are students of K nursing college located in Seoul. The sampling investigation period is on Sept. 14, 2002 to Oct. 26, 2002. The measuring instrument used for Transactional Analysis ego state is 50 items Ego-gram research paper devised by Dusay(1997). For studying coping style, Folkman & Lazarus's measurement(1984) was adopted, which is translated and modified by Han, and Oh,(1990). Statistic average and standard deviation were generated by using SPSS PC+, t-test and Pearson correlation. The results were as follows: 1) In the type of ego states on both groups(lower group : freshmen, sophomore upper group : junior, senior) indicated the arithmetic apex NP(maximum value), then the point A was high and the data made a down slope to point AC. In the comparison to type of ego states between two groups, only at point CP, the data value of upper year students represented higher than that of lower year ones by C(t=2.28, p=.023). In the psychological energy level of ego states, both groups indicated average level.2) Stress coping style of whole students were highly and affirmatively dedicated to research. Consecutive consequences follow like this(high to low) : the central point of problem, search for social support, hopeful aspect and indifference. Especially hopeful aspect(t=.67, p=.05), relaxation of tension(t=-2.16, p=.03) made significant difference each other in the view of arithmetic calculation 3) While verifying coping style in terms of ego states level between lower and upper students group, In type CP, high level ego states group indicated significant difference on stress coping style area than low leveled group and made such sequences as the central point of problem, hopeful aspect, search for social support, positive interest and relaxation of tension. In type NP, sequences such as the central point of problem, search for social support, positive interest and relaxation of tension were emerged with little differences. In type A, the central point of problem, positive interest and relaxation of tension. In type FC, hopeful aspect, search for social support, positive interest and relaxation of tension. In type AC, hopeful aspect and indifference were derived significantly different(p<.05). 4) In the aspect of relation between ego states and coping style, type CP presented the central point of problem and relaxation of tension, type NP presented positive interest, search for social support and the central point of problem, type A showed the central point of problem, positive interest and relaxation of tension, type FC showed relaxation of tension, positive interest, search for social support, indifference and the central point of problem, type AC showed hopeful aspect, indifference and the central point of problem. All the sequence shown above had high-to-low procedure and represented static relations each other(p<.05).

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.37-39
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• 2000

The purpose of this paper is to present a neural network model based on change-point detection for the prediction of the U.S. Treasury Securities. Interest rates have been studied by a number of researchers since they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points due to the monetary policy of the U.S. government. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5344-5356
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• 2018

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.609-618
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• 2016

Since demand for location-based services increases and the relevant service becomes more diverse, the use of POI (Point of Interest) is being required in various fields. Various roles of POI for display, search and inquiry exist, but the implementation and expression of such roles are partially limited. Therefore, the data model for implementation is suggested in this paper to enable practical implementation, expression and inquiry of POI data. The data model was developed based on 3 roles of POI including search, expression and linkage, and especially, the spatial relationship between POI objects which was not suggested in previous data models is considered and time series scheme is suggested to enable various expressions and inquiries in application services.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.359-366
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• 2019

Indoor spatial data has great importance as the demand for representing the complex urban environment in the context of providing LBS (Location-based Services) is increasing. IndoorGML (Indoor Geographic Markup Language) has been established as the data standard for spatial data in providing indoor navigation, but its definitions and relationships must be expanded to increase its applications and to successfully delivering information to users. In this study, we propose an approach to integrate IndoorGML with Indoor POI (Points of Interest) data by extending the IndoorGML notion of space and topological relationships. We consider two cases of representing Indoor POI, by 3D geometry and by point primitive representation. Using the concepts of the NRS (node-relation structure) and multi-layered space representation of IndoorGML, we define layers to separate features that represent the spaces and the Indoor POI into separate, but related layers. The proposed methodology was implemented with real datasets to evaluate its effectiveness for performing indoor spatial analysis.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.1
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• pp.9-17
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• 2021

This aim of this study examined the relationship among the interest in anti-aging, health promotion behaviors and the need for anti-aging services by their 20s. Survey was conducted among adults from their 20s in the Changwon City. 228 responses were used for analysis. The overall average score of the interest and effort of anti-aging were 2.97 point and 2.62 point (out of 5), respectively. And those were both higher in female than men (p<0.01~p<0.001). The overall average score of need for anti-aging service was 3.50 point(total score is 5). In The demand for each area of anti-aging service were ≥3.5 point in all 5 areas, and stress management (4.00 point) was the highest, while the beauty management (3.60 point) was the lowest. There were significant differences in all five areas by sex (p<0.01~p<0.001). The overall score of the performance of health promotion behaviors was 2.44 point(total score is 4), and the interpersonal relationship score (2.85 point) was the highest, while the health responsibility score (2.08 point) was the lowest. The interest in anti-aging and performance of health promotion behaviors showed positive relationship to anti-aging services, and their explanation powers were 34.6% (p<0.001). The results of this study suggest be used as data to establish strategies revitalizing various anti-aging service in the twenties.