• Journal of Industrial Convergence
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• v.21 no.2
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• pp.33-42
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• 2023

This study was conducted to investigate the relationship between political interest, economic interest, sociocultural interest and intention of maintaining the nursing job against 291 nursing students from a university in C city from August to September 2022. The collected data were analyzed by t-test, ANOVA, and Pearson's correlations. The study showed political interest 3.35(maximum 5 points), economic interest 3.76(maximum 5 points), sociocultural interest 4.15(maximum 5 points), and intention of maintaining the nursing job were 3.40(maximum 5 points). In the correlation between variables, the correlation coefficient between political interest and sociocultural skill(r=.385, p<.01) was high, and economic interest and overall sociocultural interest were the most correlated(r=.534, p<.01). For the correlation with intention of maintaining the nursing job, sociocultural behavioral intention(r=.158, p<.01) and attitude(r=.131, p<.01) were statistically significant. Based on the results, repeated studies including political, economic, and sociocultural interest as variables influencing intention of maintaining the nursing job and future research on systematic and diverse educational programs including sociocultural attitudes and behavioral intention are suggested so the nursing job can be maintained for longer time as a profession.

• 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.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.407-414
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• 2015

Interest points are generally located at the pixels where height changes occur. So, interest points can be the significant pixels for DSM generation, and these have the important role to generate accurate and reliable matching results. Manual operation is widely used to extract the interest points and to match stereo satellite images using these for generating height information, but it causes economic and time consuming problems. Thus, a tie point extraction method using Harris-affine technique and SIFT(Scale Invariant Feature Transform) descriptors was suggested to analyze height information of buildings in this study. Interest points on buildings were extracted by Harris-affine technique, and tie points were collected efficiently by SIFT descriptors, which is invariant for scale. Searching window for each interest points was used, and direction of tie points pairs were considered for more efficient tie point extraction method. Tie point pairs estimated by proposed method was used to analyze height information of buildings. The result had RMSE values less than 2m comparing to the height information estimated by manual method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.205-211
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• 2012

In this paper, we proposed a context-aware augmented reality platform for mobile augmented reality to support user-oriented virtual world information for smartphone user. We designed the platform architecture and 6 subsystems which are derived from the analysis of existing augmented reality applications and platforms. The proposed architecture includes a context reasoning service subsystem for the context-aware information filtering, and separates the inner platform from the outer virtual world network containing virtual information to resolve interoperability issue of POI(Points of Interest) data.

• Journal of Information Technology Services
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• v.10 no.4
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• pp.269-280
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• 2011

Mobile Augmented Reality (mobile AR), as one of the most prospective mobile applications, intends to provide richer experiences by annotating tags or virtual objects over the scene observed through camera embedded in a handheld device like smartphone or pad. In this paper, we analyzed the current status of the art of mobile AR and proposed a novel Points of Interest (POIs) data model based on ontology to provide context-aware information retrievals on lots of POIs data. Proposed ontology was expanded from the standard POIs data model of W3C POIs Working Group and established using OWL (Web Ontology Language) and Protege. We also proposed a context-aware mobile AR platform which can resolve three distinguished issues in current platforms : interoperability problem of POI tags, POIs data retrieval issue, and context-aware service issue.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1467-1469
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• 2003

Image matching is fundamental process to identify conjugate points on the stereo images. However, standard methods or general solutions for matching problem have not been found yet, in spite of long history. Quality of the matching basically depends on uniqueness of the matching entity and robustness of the algorithm. In this study, conjugate points were extracted by implementing interest operator, then area based matching method was applied to the topographical characteristics of the gray value as the matching entities. The matching entities were utilized based on the concept of the intrinsic image.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.362-371
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• 2003

In this paper, we propose an optimal interest point extraction method to support shape-base image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of the shape contour. The threshold is determined dynamically by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For n points of the contour, the proposed algorithm has O(nlogn) computational cost on an average to extract the number of m optimal interest points. Experiments were performed on the 70 synthetic shapes of 7 different contour types and 1100 fish shapes. It shows the average optimization ratio up to 0.92 and has 14% improvement, compared to the fixed threshold method. The shape features extracted from our proposed method can be used for shape-based image classification, indexing, and similarity search via normalization.

• The Korean Journal of Emergency Medical Services
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• v.22 no.2
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• pp.93-102
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• 2018

Purpose: The study aims to establish an effective training strategy and methods by comparing the effects of educational interest, satisfaction, and achievements of virtual reality and videos education before simulation training. Methods: The randomized control study was implemented on May 31, 2018, by randomly selecting 36 participants to compare educational virtual reality and videos. Statistical analyses were performed using SPSS 20.0. Results: The participants were divided into an intervention group of 17(47.2%) and a control group of 19(52.8%). Regarding the levels of satisfaction, a significant difference (p= .010) was noted between the control (3.88 points) and the intervention groups (4.45 points). A significant difference (p= .001) was also noted between the intervention (80.3 points) and control (63.3 points) in terms of total simulation practical skills. Conclusion: Educational virtual reality can be an alternative training method to achieve the standard educational objectives by raising levels of educational interest and of achievement with practical skills.

• Journal of Korean Dental Hygiene Science
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• v.5 no.2
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• pp.35-44
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• 2022

Background: This study was aimed at investigating the perception of social network service (SNS) users regarding dental office visits and determining the proportion of dental SNS users among general SNS users. Methods: We surveyed 177 adults using SNSs. Dental SNS characteristics were classified into information provision, interaction, recency, reliability, and interest, and the recognition level of each area was surveyed on a 5-point scale. The total number of items was 17, including three information provision, three interaction, four recency, four reliability, and three interest items. Results: Among the five domains, the recognition level was the highest for reliability (3.51 points) and the lowest for interest (2.94 points). Among the 17 items, the recognition level was the highest for "Educational information provided by dental SNS is valuable" at 3.60 points, "Dental SNS educational video is useful for information sharing and dental knowledge improvement" at 3.53 points, and "The perceived educational information of dental SNS is reliable" at 3.51 points. Participants in their 20s and 30s had higher scores for being up-to-date (3.33 and 2.88 points, respectively) and reliability (3.59 and 3.09 points, respectively) than those in their 40s or older. The recognition level of all areas of dental SNS characteristics was significantly higher for experienced dental SNS users than for nonexperienced ones. Conclusions: The results of this study suggested that dental institutions should consider ways to utilize SNS for patient management and education and that dental SNS-related contents should contain educational and reliable information to help SNS users manage their oral health.