• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.190-201
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• 2024

This study examined the effect of the distance Infection Control Education Program (ICEP), developed based on the ADDIE model, on infection control knowledge, attitude, and performance among care workers in long-term care facilities nationwide. The program, developed based on the ADDIE model, was applied to 173 care workers directly responsible for nursing care of elderly residents in lomg-term care facilities. The distance ICEP for care workers was conducted through the website and lasted 30 minutes for each of the eight topics. To determine the effectiveness of the education, infection control knowledge, attitude, performance, and satisfaction were surveyed before and four weeks after the program. Differences in infection control knowledge, attitude, and performance before and after the distance ICEP were assessed by a t-test. A significant difference was observed in knowledge and infection control performance after the distance ICEP was administered to care workers. In the sub-domains of infection control performance, overall understanding of infection, regular infection control education, infection control by special pathogen (multidrug-resistant bacteria, tuberculosis, tick-borne infectious diseases), and detailed infection control education by infection site (pressure ulcers and urinary tract infections) were significantly improved. Infection control knowledge and performance improved through the distance ICEP applied to care workers. Satisfaction also displayed high scores on most items and indicated that it was helpful for infection control in facilities, confirming the effectiveness of infection control education. Based on the survey of care workers nationwide, the infection education program can be effectively used for care workers in the future.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.916-921
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• 2011

Mobile robot localization is the task of estimating the robot pose in a given environment. This research deals with outdoor localization based on an elevation map. Since outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose. This paper proposes a Hausdorff distance-based map matching method. The Hausdorff distance is exploited to measure the similarity between extracted features obtained from the robot and elevation map. The experiments and simulations show that the proposed Hausdorff distance-based map matching is useful for robust outdoor localization using an elevation map. Also, it can be easily applied to other probabilistic approaches such as a Markov localization method.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.57-67
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• 2019

Income distribution is a major concern in economic theory. In regional economics, it is often of interest to compare income distributions in different regions. Traditional methods often compare the income inequality of different regions by assuming parametric forms of the income distributions, or using summary statistics like the Gini coefficient. In this paper, we propose a nonparametric procedure to test for heterogeneity in income distributions among different regions, and a K-means clustering procedure for clustering income distributions based on energy distance. In simulation studies, it is shown that the energy distance based method has competitive results with other common methods in hypothesis testing, and the energy distance based clustering method performs well in the clustering problem. The proposed approaches are applied in analyzing data from China Health and Nutrition Survey 2011. The results indicate that there are significant differences among income distributions of the 12 provinces in the dataset. After applying a 4-means clustering algorithm, we obtained the clustering results of the income distributions in the 12 provinces.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.67-80
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• 2007

Recently, many studies on query processing algorithms has been done for spatial networks, such as roads and railways, instead of Euclidean spaces, in order to efficiently support LBS(location-based service) and Telematics applications. However, both a closest pairs query and an e-distance join query require a very high cost in query processing because they can be answered by processing a set of POIs, instead of a single POI. Nevertheless, the query processing cost for closest pairs and e-distance join queries is rapidly increased as the number of k (or the length of radius) is increased. Therefore, we propose both a closest pairs query processing algorithm and an e-distance join query processing algorithm using a POI-based materialization technique so that we can process closest pairs and e-distance join queries in an efficient way. In addition, we show the retrieval efficiency of the proposed algorithms by making a performance comparison of the conventional algorithms.

• The Journal of Information Systems
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• v.11 no.1
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• pp.51-74
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• 2002

The purpose of this study is to empirically investigate critical success factors for effective implementation of web-based distance learning system. First of all, four critical success factors are theoretically derived from reviewing previous research. They are: (1) learner-related factor including the variables such as teaming ability, learning attitude, and attending motivation, (2) environmental factor including the variables of physical and mental support for learners, (3) instructional design factor represented by one variable, the degree of appropriateness of learning contents, and (4) the factor concerning the level of self-directed learning readiness embracing the variables such as curiosity for learning, openness towards challenge of learning and affection for learning. Subsequently, the relationships between these four critical success factors and the degree of learning satisfaction are empirically investigated. The data for empirical analysis of the research are collected from 1,020 respondents who have already passed the web-based distance learning courses which have been implemented in Information and Communication Officials Training Institute. Out of 1,020 responded questionnaires, 875 data were available for statistical analyses. The main results of this study are as follows. Firstly, the most important factor for successful implementation of the web-based distance learning system is shown to be the instructional design factor, and in the next place, the self-directed learning readiness factor, the environmental factor and the learner-related one in sequence. Secondly, additional analysis of the variables included in the instructional design factor shows that availability of practical information and knowledge is the most influencing variable, and next, interesting composition of contents, reasonable learning amount, optimal level of instruction, and understandable explanation are significantly important in the descending order. Lastly, among learning motivators, strong intention of acquiring business knowledges and skills is found to be the most important satisfier in the web-based distance learning. The theoretical contribution of this study is to derive a comprehensive model of critical success factors for implementing the web-based distance learning system. And, the practical implication of this study is to propose efficient and effective guidelines for developing and operating the web-based distance learning system in the various kinds of organizations.

• ETRI Journal
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• v.25 no.6
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• pp.527-530
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• 2003

In this study, we consider distance-based registration (DBR) and propose a DBR with implicit registration (DBIR) in order to improve the performance of the DBR. With analytical models based on a 2-dimensional random walk in a hexagonal cell configuration, we analyzed the performance of the DBR and DBIR. Our results showed that the DBIR always outperforms the DBR.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.36-42
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• 1996

In this paper, we have proposed the robust estimation of HMM parameters which is based on CDDN(codeword dependent distance normalization)and codeword weighting by distance. The proposed method has used a distance normalization based on the characteristics of a codeword dependent distribution and have computed fuzzy contributions of codeword to a input vector with a fuzzy objective function. From experimental results, we have shown the effectiveness of the proposed method in that the correction rate of the proposed method is improved 4.5% over the conventional FVQ based method. Especially, the application of distance weighting to smoothing of output probability is improved the performance of 2.5% compared to distance based codeword weighting.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.522-529
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• 2010

Distance relay plays an important role in the protection of transmission lines. The application of flexible AC transmission systems (FACTS) devices, such as the static synchronous compensator (STATCOM), could affect the performance of the distance relay because of compensation effect. This paper analyzes the application of distance relay on the protection of a transmission line containing STATCOM. New setting principles for different protection zones are proposed based on this analysis. A typical 500 kV transmission system employing STATCOM is modeled using Matlab/Simulink. The impact of STATCOM on distance protection scheme is studied for different fault types, fault locations, and system configurations. Based on simulation results, the performance of distance relay is evaluated. The setting principle can be verified for the transmission line with STATCOM.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.71-76
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• 2023

In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.