• Journal of Sasang Constitutional Medicine
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• v.20 no.1
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• pp.56-66
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• 2008

1. Objectives and Methods This study was performed to investigate the relationship between Sasang constitution and decision making style. Subjects were 69 men and 45 women. Decision making style score, physical measurements and results were measured and analyzed according to Sasang constitution. 2. Results Soeumin group had significantly high score in rational score compared with those of Soyangin. Soyangin group had significantly high score in intuitive score compared with those of Soeumin. Soeumin group had significantly high score in dependent score compared with those of Taeeumin. According to binary logistic regression analysis for decision making style score, Sasang constitution were significant risk factors and ORs of Taeeumin were significantly higer than those of Soyangin in rational score, ORs of Soyangin were significantly higer than those of Soeumin in intuitive score, and ORs of Soeumin were significantly higer than those of Taeeumin in dependent Score. 3. Conclusions Soyangin had significantly high score in intuitive score. Soeumin had significantly high score in dependent score. We found many evidences that Soyangin is intuitive style and Soeumin dependent style in the text of ${\ulcorner}$Dongyi Suse Bowon${\lrcorner}$ and other research. But as far as Taeeumin was concerned, the result of Taeeurnin’s was not coincident with other research. More cases and research were needed to confirm the personality and phychological type of Taeeumin. This study result will be an important method that classify Sasang Costitution and consultation of student career decision making and studying attitude.

• Journal of Engineering Education Research
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• v.16 no.4
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• pp.30-36
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• 2013

Applying intuitive learning method on engineering education, especially for the mechanical engineering, is hardly found from the previous case studies and is not easily proved its beneficial verification. Verbal repetition is also rarely used to investigate its positive effects on educational methodology for both science and engineering disciplines. To prove the education effects of these two methods; we used intuitive thinking time period at the beginning of each lecture and let students repeat the concepts and the equations verbally. These two methods were related to the subjects of each lecture, and were used for students to try to draw engineering thinking from natural phenomena that they could easily experience in daily life. The methods could help them to memorize theoretical ideas. We investigated the effects of intuition and verbal repetition methods by comparing the scores of final exam with those of midterm exam. The results revealed significant improvement; 77.6% of the students achieved higher score in their final exam compared to midterm exam. We plan to investigate qualitative contributions of intuition and verbal repetition methods to the students' achievement for the further research.

• Science of Emotion and Sensibility
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• v.12 no.1
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• pp.45-54
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• 2009

Categorization in a vital means for dealing with the multitudes of entities in the world surrounding people. Among others, the perceptual and the evaluative similarities factors strongly affect categorization. The conventional SD-type procedure are insufficient in this regard, since it requires an individual subject to make isolated judgments about each stimulus to identify categorization in terms of a group tendency. It disregards the individual categorization in which the similarities are of great importance. Thus in this study the phased emotional evaluation method is suggested based on the intuitive categorization of stimuli and on the similarity judgement of representative/ non-representative case in each category. To verify the effectiveness of the suggested evaluation method the scanned jewelry images are selected as test stimuli for emotional evaluation experiment. As a result of the evaluation experiment, the conventional SD-type procedure is complemented by the emotional evaluation method in phases of the task of intuitive categorization, the selection of the representative images and the setup of the evaluation score of the representative images to internally supplied anchors of evaluating non-representative images.

• Journal of Korean Academy of Nursing
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• v.31 no.5
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• pp.835-845
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• 2001

This study was to identify the relationship between personality type, college admission SAT scores and GPA scores of student nurses. Method: The data was collected from 270 student nurses enrolled in a baccaleaureate program in Seoul. MBTI was used to identify students' personality and SAT score and GPA score were collected over 4 years. The collected data was analyzed by using SPSS Win. package. Result: 1. There were slightly more extrovert (E) type (54.4%) students than the introvert (I) type; more sensing (S) type (71.1%) than the intuitive (N) type. 2. The introvert type students had significantly higher SAT scores than those of the extrovert type (p=.002). 3. The judging type students had significantly higher GPA scores throughout their college years than the perceiving type. 4. There was no statistically significant relationship between SAT and GPA scores. SAT scores did not accurately predict students' academic achievement in college in this sample. Conclusion: The distribution of the personality types in the sample was different from the general population which may suggest that college admission criteria is biased toward certain personality type. Since different personality types process information and cope with the outside world differently, effective teaching strategies need to be considered for each class.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.259-267
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• 2018

In this study, we study the system to remotely transmit musical score based on Internet of Things(IoT). When musicians play music, they occasionally use the instrument and the musical score at the same time. At this time, the degree of immersion in music is reduced. In order to solve this problem, this paper proposes automatic sheet page turning system based on Internet of Things(IoT). The system can be remotely adjusted using the foot without using the hand. The system consists of a Wireless Smart Button equipped with Bluetooth and an application capable of operating the Wireless Smart Button. The Wireless Smart Button used Arduino based open source. We designed an application(App) to utilize it and proposed an intuitive UI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1709-1721
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• 2021

This study identified the impact of career identity and career decision-making type on career preparation behavior in nursing students. The participants were 198 nursing students enrolled in the 3rd and 4th grades located in J province, and data were collected from June 14, 2021 to 27, 2021, using an online questionnaire. Data were analyzed with descriptive statistics, independent t-test, one-way ANOVA, Pearson's correlation, and hierarchical regression using SPSS/WIN 23.0 program. As a result, the mean score of career identity, rational type, intuitive type, dependent type, and career preparation behavior were 2.79(range 1~4), 3.78(range 1~5), 3.38(range 1~5), 3.01(range 1~5), and 3.51(range 1~5), respectively. Career preparation behavior was significantly correlated with career identity(r=.40, p<.001), rational type(r=.50, p<.001), intuitive type(r=.22, p=.002), and dependent type(r=-.20, p=.004). Factors influencing career preparation behavior were career identity(β=.23, p=.001), rational type(β=.31, p<.001), intuitive type(β=.27, p<.001), and dependent type(β=-.20, p=.002). These variables accounted for 24.6%p of the variance in career preparation behavior(F=11.93, p<.001). These research results suggest that it is necessary to establish a career identity and develop and apply a program that includes career guidance according to career decision-making type to improve the career preparation behavior of nursing students.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.308-311
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• 2021

This paper describes the development of a functional game for preventing dementia for the elderly based on virtual reality. The game was developed using the Unity 3D engine to create a fishing spot, a virtual reality space. In consideration of the fact that the object of the game is an elderly person relatively unfamiliar with the virtual reality, the number of operation buttons is minimized and the sense of resistance and fatigue are reduced by an intuitive game configuration. In addition, the game was designed to give people a sense of accomplishment with a score system after the game, and to encourage them to participate in the game Overall, the developed game consists of main, interface, stage, score, TTS, tutorials, and ending credit. Each category stage is divided into three stages and realized in one integrated environment, and VRHMD can be used to games that enhance memory, attention, and judgment.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1697-1707
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• 2021

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.1-6
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• 2023

In this paper, we present a new method for classifying malicious URLs to reduce cases of learning difficulties due to unfamiliar and difficult terms related to information protection. This study plans to extract only visually distinguishable features within the URL structure and compare them through map learning algorithms, and to compare the contribution values of the best map learning algorithm methods to extract features that have the most impact on classifying malicious URLs. As research data, Kaggle used data that classified 7,046 malicious URLs and 7.046 normal URLs. As a result of the study, among the three supervised learning algorithms used (Decision Tree, Support Vector Machine, and Logistic Regression), the Decision Tree algorithm showed the best performance with 83% accuracy, 83.1% F1-score and 83.6% Recall values. It was confirmed that the contribution value of https is the highest among whether to use https, sub domain, and prefix and suffix, which can be visually distinguished through the feature contribution of Decision Tree. Although it has been difficult to learn unfamiliar and difficult terms so far, this study will be able to provide an intuitive judgment method without explanation of the terms and prove its usefulness in the field of malicious URL detection.