• Journal of Korean Elementary Science Education
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• v.22 no.2
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• pp.149-155
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• 2003

The objective of this research was to study the impact of metacognitive lesson models on the formation of magnetic field concepts. The subjects of this research was eighty students from two sixth grade classes. One class of forty of these students was the experimental group, which the metacognitive strategic lesson model was applied, and the other class of forty students was the control group which the traditional lessons were conducted. As the result of the experiment, the experimental group and the control group, which previously did not show difference in terms of achievement of conceptualizing magnetic field, displayed a significant difference. According to the comparison between the pre-experiment test on the students' previous concepts and the achievement of the students after the experiment, the middle group showed difference between the experimental group and the control group in a small degree. The lower group showed a notable difference between the two groups and the higher group showed no difference. In terms of achievements shown in different questions asked, there was little difference in the questions that were stated in the textbook while there was a significant difference in the questions that applied the contents of the textbook. The higher academic group, according to the test on the previous concepts, did not show much difference between the experimental group and the control group when they were asked about the concepts of the textbook. In terms of the comparison in the metacognitive levels, both the higher and lower metacognitive experimental groups' average grade was higher than the control groups' and showed an important statistical difference.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• Korean journal of food and cookery science
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• v.19 no.3
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• pp.308-317
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• 2003

The purpose of this study was to identify combinations of factors, with regard to the use of restaurants by tourists, and to establish the relative importance of these factors in terms of their contribution to the total usage. Of 250 questionnaires, 209 were utilized for analysis in this study. Crosstabs, conjoint analysis, paired-samples t-test, k-means cluster analysis, one-way ANOVA analysis, and the Friedman test were used for the statistical analysis. The findings from this study were as follows: First, the Pearson's R and Kendall's tau statistics show that the model fits the data well. Second, it was found that 209 tourists most preferred restaurants that provided excellent quality traditional food, with a high quality of service, at a cheap price for the suburb. The 81 tourists of the first cluster most preferred restaurant that provided excellent quality fusion food, at a cheap price for the suburb. The 65 tourists of tile second cluster most preferred restaurant that provided average quality national food, at an expensive price for the suburb. The 63 tourists of the third cluster most preferred restaurant that provided excellent quality traditional food, at a reasonable price for the suburb. Third, it wis found that all tourists and the three clusters groups regarded both the type of food and its price to be very important factors. Finally, the results used in this study have provided some insight into the types of marketing strategies and tourism policies that may be successfully used by the operators and policymakers managing a location, the quality, price and type of food, and quality of service required by tourists dining at restaurants.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.4
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• pp.148-158
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• 2011

This study examines visitors' image and behavior characteristics of Wolseong palace in Gyeongju. This area has been a royal palace during Silla periods. So many scholars dedicate to the protection of this historical-cultural heritage. The research process consists of two main steps, such as on-site field investigation and survey research. The data were collected in summer and autumn. Collected data is classified into three groups to describe visitors' behavior, time, space, and then processed by statistical methods. The results are as follows: First, there is a shortage of programs and facilities. The result shows most visitors consider Wolseong palace as a pathway for walking. Hence better functions should be developed to attract more visitors but with least effect to historical remains. The founding is that increasing programs for history exploration, enhancing lighting installation, facilities, plant arrangement, road condition and so forth would be suggested.

• Journal of the Korean Physical Society
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• v.73 no.12
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• pp.1821-1826
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• 2018

Uniformity is a key feature of state-of-the-art infrared focal planed array (IRFPA) and infrared imaging system. Unlike traditional infrared telescope facility, a ground-based infrared radiant characteristics measurement system with an IRFPA not only provides a series of high signal-to-noise ratio (SNR) infrared image but also ensures the validity of radiant measurement data. Normally, a long integration time tends to produce a high SNR infrared image for infrared radiant characteristics radiometry system. In view of the variability of and uncertainty in the measured target's energy, the operation of switching the integration time and attenuators usually guarantees the guality of the infrared radiation measurement data obtainted during the infrared radiant characteristics radiometry process. Non-uniformity correction (NUC) coefficients in a given integration time are often applied to a specified integration time. If the integration time is switched, the SNR for the infrared imaging will degenerate rapidly. Considering the effect of the SNR for the infrared image and the infrared radiant characteristics radiometry above, we propose a-wide-dynamic-range NUC algorithm. In addition, this essasy derives and establishes the mathematical modal of the algorithm in detail. Then, we conduct verification experiments by using a ground-based MWIR(Mid-wave Infared) radiant characteristics radiometry system with an Ø400 mm aperture. The experimental results obtained using the proposed algorithm and the traditional algorithm for different integration time are compared. The statistical data shows that the average non-uniformity for the proposed algorithm decreased from 0.77% to 0.21% at 2.5 ms and from 1.33% to 0.26% at 5.5 ms. The testing results demonstrate that the usage of suggested algorithm can improve infrared imaging quality and radiation measurement accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.160-160
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• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.125-138
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• 2022

Purpose: Failure Mode and Effect Analysis (FMEA) is a widely utilized technique to measure product reliability by identifying potential failure modes. Even though FMEA techniques have been studied, the form of Risk Priority Number (RPN) used to evaluate risk priority in FMEA is still questionable because of its shortcomings. In this study, we suggest common RPN(cRPN) to resolve shortcomings of the traditional RPN and show the extensibility of cRPN. Methods: We suggest cRPN which is based on Cobb-Douglas production function, and represent the various application on weighting risk factors, weighted RPN in a mathematical way, and show the possibility of statistical approach. We also conduct numerical study to examine the difference of the traditional RPN and cRPN as well as the potential application from the analysis on marginal effects of each risk factor. Results: cRPN successfully integrates previously suggested approaches especially on the relative importance of risk factors and weighting RPN. Moreover, we analyze the effect of corrective actions in terms of econometric analysis using cRPN. Since cRPN is rely on the reliable mathematical model, there would be numerous applications using cRPN such as smart factory based on A.I. techniques. Conclusion: We propose a reliable mathematical model of RPN based on Cobb-Douglas production function. Our suggested model, cRPN, resolves various shortcomings such as consideration of the relative importance, the effect of combinations among risk factors. In addition, by adopting a reliable mathematical model, quantitative approaches are expected to be applied using cRPN. We find that cRPN can be utilized to the field of industry because it is able to be applied without modifying the entire systems or the conventional actions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• The Korean Journal of Emergency Medical Services
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• v.28 no.1
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• pp.113-126
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• 2024

Purpose: Education for children in South Korea is similar to that for adults, necessitating additional enhancements. Therefore, this study aimed to examine the effectiveness of fairy-tale books in cardiopulmonary resuscitation (CPR) education among kindergarten children. Methods: The study involved 64 kindergarten children enrolled in an affiliated kindergarten program were included. The participants were divided into an experimental group of 32 who received CPR education using picture books, and a control group of 32 who received education through traditional methods. Participant characteristics such as sex, age, height, weight, cardiac arrest awareness, and ability to request assistance were measured, and the collected data were statistically analyzed. Results: Following education, the experimental group showed significantly higher scores than the control group across all measures, including cardiac arrest recognition (2.25 vs. 0.34, p<.001) and consciousness assessment (1.81 vs. 0.09, p<.001). Additionally, in requesting assistance, the experimental group exhibited statistical superiority in phone usage (1.75 vs. 0.28, p<.001), situational explanation post-call (2.25 vs. 0.34, p<.001), and self-location explanation (0.84 vs. 0.00, p=.001). Conclusion: The use of fairy-tale books in CPR education enhanced cardiac arrest recognition and the ability to request assistance (EMS Activation) more effectively than the traditional educational methods among kindergarten children.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.216-224
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• 2024

This paper presents a method for analyzing the reliability of hardware electronic equipment, taking into account failures caused by radiation. Traditional reliability analysis primarily focuses on the wear out failure rate and often neglects the impact of radiation failure rates. We calculate the wear out failure rate through physics of failure analysis, while the radiation failure rate is semi-empirically estimated using the Verilog Fault Injection tool. Our approach aims to ensure reliability early in the development process, potentially reducing development time and costs by identifying circuit vulnerabilities in advance. As an illustrative example, we conducted a reliability analysis on the ISCAS85 circuit. Our results demonstrate the effectiveness of our method compared to traditional reliability analysis tools. This thorough analysis is crucial for ensuring the reliability of FPGAs in environments with high radiation exposure, such as in aviation and space applications.