• Journal of Korean Society of Rural Planning
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• v.17 no.1
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• pp.13-27
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• 2011

This study analyzed the budget investment plans for the unit-project items(UPI) of 176 project districts for the rural village comprehensive development projects (RVCDP). This study classified the master plan reports of 176 project districts into 88 unit project items in aspect of project management, in order to analyze characteristics of distribution of budget in each project item. Most of all unit project items have similar types of uniform distribution with plus skewness in frequency pattern analysis except the total budget of the project district. This study analyzed the characteristics of budget distribution per province, year, and geographical types of region. Furthermore this paper also analyzed ratio of budget in unit project items to find out distribution pattern of each budget between project items over time. The hierarchical system for UPI of RVCDP consisted of three steps, which are 4 items of the first step on Strength of Rural-urban Exchange & Regional Capability (RURC), Green-income Infrastructure & Facility (GIF), Culture- health-welfare Facility, and Eco-environment & Landscape facility (ELF), 13 items for the second one, and 52 items for the third project items. From the results of the budget investment analysis for 5 years from 2004 to 2008, the budget investment ratios of RURC and ELF have steady state for every year, while GIF in decreasing and ELF in increasing over time. The ratios of UPI on infrastructure were decreased, whereas those on culture, health, and welfare were increased. Portion of tow project items among 52 items, which are community centers for village residents and rural experimental study facility, has 30% of total budget investment. Futhermore, the budget ratios of seven project items showed 50% of total budget. Average value of project budgets for five years was optimized as a type of exponential function in the case of decent array for ranking order.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.691-700
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• 2000

The standard volume ray-tracing, optimized with octree, needs to repeatedly traverse hierarchical structures for each ray that often leads to redundant computations. It also employs the expensive 3D interpolation for producing high quality images. In this paper, we present a new ray-casting method that efficiently computes shaded colors and opacities at resampling points by traversing octree only once. This method traverses volume data in object-order, finds resampling points on slices incrementally, and performs resampling based on 2D interpolation. While the early ray-termination, which is one of the most effective optimization techniques, is not easily combined with object-order methods, we solved this problem using a dynamic data structure in image space. Considering that our new method is easy to implement, and need little additional memory, it will be used as very effective volume method that fills the performance gap between ray-casting and shear-warping.

• Journal of Engineering Education Research
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• v.18 no.1
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• pp.61-73
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• 2015

The purpose of this study is to suggest some direction to support learning of students in college of engineering. It results from the assumption that engineering education accreditation should come with assessment of the educational process. To analyze the learning process, this study analyzed 5 categories - involvement in and out of instruction, faculty-student interaction, teaching-learning outcomes, and the system of student support. The Research method was questionnaire, and T-test and hierarchical linear model were used. The major findings are as follows. Major-level of satisfaction in teaching-learning and optional-level of satisfaction in teaching-learning are good. But the degree of self-directed learning activities and student-faculty interaction is low, and writing attitude and learning outcomes are not good. Student-faculty interaction, high-order thinking activities and active involvement have a good influence on learning outcomes. So this study suggests to enhance active involvement in instruction, high-order thinking activities, writing skills, and interaction with faculty for the improvement of quality of higher education.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.13-21
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• 2011

In mobile environment supporting mobility technologies, user requirements have been increased with respect to utilization of location information. In particular, moving object DBMS has consistently posed in order to efficiently maintain traffic information related to location of vehicle which tents to tremendously change over time. Despite the fact that these sorts of researches must be taken into consideration, empirical studies on moving object in terms of map database for lbs service, spatial attribute of which is continuously changed over time, have rarely performed. Therefore, aim of this paper is to suggest efficient spatial index scheme, which is capable of supporting query processing algorithm and location of moving object over time, by developing new empirical model. As a result, we can come to the conclusion that moving object model based on multi-fixed grid index makes it possible to cut down on the number of entity for retrieving. What's more, this model enables hierarchical data to be accessed through efficient spatial filtering on large-scale lbs data and constraints in accordance with level in order to display map.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.68-77
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• 2017

In order to implement the smart home environment, we need an intelligence service platform that learns the user's life style and behavioral patterns, and recommends appropriate services to the user. The intelligence service platform should embed a couple of effective and efficient data mining algorithms for learning from the data that is gathered from the smart home environment. In this study, we evaluate the suitability of data mining algorithms for smart home intelligent service platforms. In order to do this, we first develop an intelligent service scenario for smart home environment, which is utilized to derive functional and technical requirements for data mining algorithms that is equipped in the smart home intelligent service platform. We then evaluate the suitability of several data mining algorithms by employing the analytic hierarchy process technique. Applying the analytical hierarchy process technique, we first score the importance of functional and technical requirements through a hierarchical structure of pairwise comparisons made by experts, and then assess the suitability of data mining algorithms for each functional and technical requirements. There are several studies for smart home service and platforms, but most of the study have focused on a certain smart home service or a certain service platform implementation. In this study, we focus on the general requirements and suitability of data mining algorithms themselves that are equipped in smart home intelligent service platform. As a result, we provide a general guideline to choose appropriate data mining techniques when building a smart home intelligent service platform.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.9 s.339
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• pp.29-40
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• 2005

In this paper, we propose a Cluster-based Multicast Routing (CMR) suitable for mobile ad-hoc networks. The main features that our proposed method introduces are the following: a) mobility-based clustering and group based hierarchical structure in order to effectively support stability and scalability, b) group based mesh structure and forwarding tree concepts in order to support the robustness of the mesh topologies which provides limited redundancy and the efficiency of tree forwarding simultaneously, and c) combination of proactive and reactive concepts which provide low route acquisition delay and low overhead. The performance evaluation of the proposed protocol is achieved via modeling and simulation. The corresponding results demonstrate the Proposed multicast protocol's efficiency in terms of packet delivery ratio, scalability, control overhead, end-to-end delay, as a function of mobility, multicast group size, and number of senders.

• Journal of Convergence for Information Technology
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• v.10 no.1
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• pp.203-210
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• 2020

This study was conducted in order to be applied to basic data for enhancing dental hygiene students' psychological well-being. Data analysis was carried out T-test, ANOVA, correlation analysis and hierarchical regression analysis by using SPSS version 19.0. The order of factors that affect the psychological well-being from the most to the least was sufferance(β=.327) and strength(β=.294) of resilience, support from friends of social support(β=.238), optimistic of resilience(β=.109), major satisfaction(β=.090), academic year(β=.084), and deep sleep(β=.078). Based on the result, this study suggests a need for developing programme to increase students' psychological well-being and a follow-up study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.56-63
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• 2015

Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.650-661
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• 2005

As the Internet is growing, the use of XML which is a standard of semi-structured document is increasing. Therefore, there are on going works about integration and retrieval of XML documents. However, the basis of efficient integration and retrieval of documents is to cluster XML documents with similar structure. The conventional XML clustering approaches use the hierarchical clustering algorithm that produces the demanded number of clusters through repeated merge, but it have some problems that it is difficult to compute the similarity between XML documents and it costs much time to compare similarity repeatedly. In order to address this problem, we use clustering algorithm for transactional data that is scale for large size of data. In this paper we use common structures from XML documents that don't have DTD or schema. In order to use common structures of XML document, we extract representative structures by decomposing the structure from a tree model expressing the XML document, and we perform clustering with the extracted structure. Besides, we show efficiency of proposed method by comparing and analyzing with the previous method.

• Journal of Korean society of Dental Hygiene
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• v.17 no.5
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• pp.769-777
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• 2017

Objectives: The study was to promote patient safety by analyzing the effect of dental hygienist's perception of patient safety culture on infection control activities. Methods: The study is based on a survey of 377 dental hygienists in total working in dental settings. The questionnaire consisted of 119 questions, including 34 questions on perception of patient safety culture, and 85 questions on infection control activities. Hierarchical regression analysis was used to examine the relationship between the perception of patient safety culture and infection control activities. The data was analyzed using the SPSS version 20.0, and p<0.05 was adopted to decide on significance. Results: The longer dental hygienists have worked n the dental settings, the more active they become in infection control activities. Among the different types of dental care settings, general (university) hospitals had the largest number of infection control activities, followed by dental clinics, and network dental clinics, in descending order. The dental settings possessing a higher number of dental hygienists were found to conduct more infection control activities than other dental settings. In addition, it was found that when a dental setting adopts a patient safety policy across all the units in the hospital, more systems and procedures for patient safety tend to be established, and that stricter management response to error leads to improvement of infection control activities. Conclusions:In order to enhance infection control activities, infection control activity programs should develop and implement periodic reinforcement of infection control education. regular monitoring of infection control activities.