• Journal of the Korean Society of Propulsion Engineers
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• v.24 no.4
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• pp.12-24
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• 2020

The dual-throat nozzle is an extremely effective method in the thrust vectoring control field, utilizing another convergent section to connect with the divergent part of the conventional convergent-divergent nozzle. In the present research, the numerical simulation is conducted to investigate the effects of the injection angle on thrust vectoring performance in a 3D supersonic nozzle. Five injection angles are discussed and core performance variations are analyzed, including the deflection angle, injected mass flow ratio, system resultant thrust ratio, efficiency, Mach number contour and streamline on the symmetry plane, and Mach number contours at different slices. Meaningful conclusions are offered for fighter jet designers.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.300-300
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• 2022

가뭄 취약성은 다양한 평가 요소가 반영되는 다기준 구성으로 개념화될 수 있으며 관련하여 수반되는 영향을 집계하여 측정해야 하므로 여러 변수가 제공하는 정보를 통합해야 한다. 따라서 가뭄 취약성 평가의 일반적인 절차에는 (1) 고려할 변수 선택, (2) 가중치 체계 정의 및 (3) 변수 집계가 포함된다. 여기서 가중치 산정은 평가결과에 막대한 영향을 미칠 수 있는 중요한 과정이다. 각 평가 요소는 내재된 의미가 다르기 때문에 모두 동일한 가중치를 가지고 있다고 가정 할 수 없다. 따라서 각 평가 요소별로 영향력을 가늠하는 가중치를 찾는 것이 다기준 평가에서 주요한 연구 분야이다. 본 연구에서는 밭 가뭄 취약성 평가를 위한 평가 요소의 자료로부터 각 요소를 통계적 기법으로 분석하여 평가 결과에 반영함으로써 주관적인 가중치를 적용하는 평가기법에 따른 편향 가능성을 해소하고자 한다. 객관적 가중치 산정기법인 Entropy, PCA 기법을 적용하였다. 평가 결과는 가중치 산정기법에 따라 차이가 발생하였으며 특히 Entropy 가중치의 경우, 다른 방법에 비하여 차이가 많이 나타났으며 이 같은 차이는 Entropy 가중치 산정기법상 정보의 변화량이 많은 평가인자에 과도한 가중치가 반영된 결과로 판단된다. 본 연구에서 제시한 밭 가뭄과 연관되는 지표를 적용하여 가뭄취약성을 평가하는 방안은 각 지역에 내재된 밭 가뭄취약정도를 파악하여 사전에 대응하기 위한 정책 수립 등에 기여할 수 있다.

• Journal of the Korean Society of Earth Science Education
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• v.11 no.1
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• pp.55-62
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• 2018

The purpose of this study was to examine the pre-service elementary teachers' views on the necessity of science learning. The eighty five student teachers in their second year of studies were participated in this study. The participants freely wrote their thoughts on a question'Why should students learn science?' The results of the study are as follows: (1) The participants' responses were very diverse, so their responses contained almost all kind of values or arguments about the science learning suggested by researchers, and there was no difference in their response ratio between views of focusing on intrinsic values and views of focusing on extrinsic values; (2) About 30% of the participants had the biased conceptions on the necessity of science learning and they would be likely to explain their biased conceptions to their future students. The educational implications and the suggestions for further studies are also presented in this paper.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.889-898
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• 2019

The problem of missing value imputation for variables in surveys that include item missing becomes complicated if outliers and logical boundary conditions between other survey items cannot be ignored. If there are outliers and boundaries in a variable including missing values, imputed values based on previous regression-based imputation methods are likely to be biased and not meet boundary conditions. In this paper, we approach these difficulties in imputation by combining various robust regression models and multiple imputation methods. Through a simulation study on various scenarios of outliers and boundaries, we find and discuss the optimal combination of robust regression and multiple imputation method.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.347-355
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• 2020

An Isolation-with-Migration (IM) model is used to estimate extant population sizes, the splitting time of populations split away from their common ancestral populations, and migration rates between the extant populations. An evolutionary model such as IM models is estimated by analyzing DNA sequences sampled from the extant populations in the model. When a true model includes an unsampled 'ghost' population without data, the unsampled population is often ignored from the evolutionary model to infer. In this paper, we conduct a simulation study to investigate the effect of an unsampled population on the estimation of the size of the sampled population. When there exists an unsampled population that shares migrations with the sampled population, the size estimation of the sampled population was biased. However, the size estimation was improved if an evolutionary model, including the unsampled population, was estimated.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.315-326
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• 2013

Users in social media post various types of opinions such as product reviews and movie reviews. It is a common trend that customers get assistance from the opinions in making their decisions. However, as opinion usage grows, distorted feedbacks also have increased. For example, exaggerated positive opinions are posted for promoting target products. So are negative opinions which are far from common evaluations. Finding these biased opinions becomes important to keep social media reliable. Techniques of opinion mining (or sentiment analysis) have been developed to determine sentiment polarity of opinionated documents. These techniques can be utilized for finding the biased opinions. However, the previous techniques have some drawback. They categorize the text into only positive and negative, and they also need a large amount of training data to build the classifier. In this paper, we propose methods for discovering the biased opinions which are skewed from the overall common opinions. The methods are based on angle based outlier detection and personalized PageRank, which can be applied without training data. We analyze the performance of the proposed techniques by presenting experimental results on a movie review dataset.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.329-337
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• 2019

It is true that many tales delivered in an 'Story-telling' auditorium or theater have not so much exquisite and refined forms as distorted and deteriorated ones. Furthermore, when false interpretations of tale-performers added into the category of the texts of tales, the problems can be made worse. In case of oral folk tales, there can be discordance between the standpoint of a tale-performer and the contents of a tale. This thesis is directly aimed at pointing out the 'Behavior Economics' problems concerned with the reading and interpretation of tales through investigating the missing parts of a text in reading tales. Man's rationality is meant to be confined to bounded rationality. Instead of making best choices, bounded rationality leads consumers to make a decision which they think suffices themselves to the point requiring no more consideration on the given item. It is the very Heuristic that does work in the process of this simplified decision making process. Heuristic utilizes established empirical notion and specific information, and that's why there can be cognitive 'Biases' sometimes leading to inaccurate judgment. As Oral Literature is basically based on heavy guesswork and perceptual biases of general public, it is imperative to contemplate oral literature in the framework of Heuristic of behavior economics. This thesis deals with thinking types and behavioral patterns of the general public in the perspective of heuristic by examining 'Story-tellings' on the basis of personal or public memory. In addition, heuristic involves how to deal with significant but intangible content such as the errors of oral story teller, the deviations of the story, and responses of the audience.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.375-384
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• 2021

Deep learning techniques have been proven to have high performance in image processing and are applied in various fields. The most widely used methods for validating a deep learning model include a holdout verification method, a k-fold cross verification method, and a bootstrap method. These legacy methods consider the balance of the ratio between classes in the process of dividing the data set, but do not consider the ratio of various features that exist within the same class. If these features are not considered, verification results may be biased toward some features. Therefore, we propose a deep learning model validation method based on data feature coverage for image classification by improving the legacy methods. The proposed technique proposes a data feature coverage that can be measured numerically how much the training data set for training and validation of the deep learning model and the evaluation data set reflects the features of the entire data set. In this method, the data set can be divided by ensuring coverage to include all features of the entire data set, and the evaluation result of the model can be analyzed in units of feature clusters. As a result, by providing feature cluster information for the evaluation result of the trained model, feature information of data that affects the trained model can be provided.

• Child Health Nursing Research
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• v.19 no.4
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• pp.262-269
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• 2013

Purpose: The aims of this study is to analysis the effects of obesity management programs for children and to measure the differences in the effects by type and dependent variables in order to analyze the structures of the programs. Methods: Sixty-one peer-reviewed journals including child obesity and intervention studies published between 2000 and 2010 were included for meta-analysis. Effect size and statistics of homogeneity were by STAT 10.0. Results: A total of 61 studies were used in the analysis, and the effect size of the independent studies was determined to be -0.23 (95% CI, -0.32 ~ -0.15). Serum Leptin and Insulin were the big effect size among the studies that used dependent variables. The theses used in the research did not display publishing bias. Conclusion: Obesity management programs that have been confirmed to be effective need to be developed into regional protocols. A continuous control of obese children and research for effective intervention program are in need.

• Journal of Gifted/Talented Education
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• v.25 no.4
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• pp.469-491
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• 2015

The present study investigated the inhibition mechanisms of gifted children, which is one of the main executive functions in human cognitive system. The inhibition process was subdivided into cognitive and emotion aspects in order to examine the interplay between these two aspects with respect to inhibition processing. In Experiment 1, word-color Stroop task was used to study the cognitive inhibition process of 100 gifted children(Gender: 62 males, 38 females; Academic grade: 46 Elementary school students, 54 Secondary school students). In addition, emotional Stroop task was utilized in Experiment 2 to examine the effect of emotional component during cognitive inhibition process. Results revealed a significant cognitive cost (i.e., word-color Stroop effect) when participants had to withhold automatic response during cognitive inhibition task in Experiment 1. Such cognitive cost was reduced as the chronological age of the participants increased, with no difference in gender. The results in Experiment 2 showed no significant emotional inhibition cost (i.e., emotional Stroop effect) during cognitive inhibition task, and there was no effect of gender nor age. This suggests that the emotional component conveyed in words did not lead to cognitive bias effects. This study proposes that the cognitive and emotional inhibition processes are seemingly independent mechanisms that engage in complex interactions during inhibition processing of behavioral response.