• Korean Journal of Applied Biomechanics
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• v.15 no.3
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• pp.153-159
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• 2005

The purpose of this study was to investigate the Effective X-Factor in golf swing. The term X-Factor means the relative rotation of shoulders with respect to hips during the golf swing. To ascertain the Effective X-Factor that resulted in a high club head speed at impact six golfers' swing motions were videotaped and analyzed using three-dimensional techniques. The results can be summarized as follows. The standard deviations of the professionals' average club head speeds were higher than the amateurs'. This means that the professionals' swing skills were better than amateurs' in driving accuracy and consistency. As the club head speeds were increased gradually the X-Factors and the club head speeds had reached to the subjects' average club head speeds, but the X-Factors and the club head speeds were not increased above the subjects' average club head speeds. The X-Factor Stretch early in the down swing was existed and Professional stretched values were higher than the amateurs. In conclusion my research results suggested that the increase in Effective X-Factors had no relationship to the increase in club head speeds.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1062-1064
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• 2019

개인 동호회 및 선수 사람들이 라켓스포츠를 하고 있지만 실력 향상에는 어려움을 가지고 있다. 현재 라켓스포츠에서 단지 스윙 연습정도만 지원하는 것이 현실이다. 본 연구에서는 저비용 3~9축 다축센서와 개인 스마트폰의 블루투스 연동을 이용한 가상운동 훈련 솔루션을 개발하기 위한 스윙 정확도 측정 방법을 연구한다.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.73-77
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• 2019

Data mining techniques can also be used to increase the efficiency of production in the tax sector, which requires professional skills. As tax-related computerization was carried out, large amounts of data were accumulated, creating a good environment for data mining. In this paper, we have developed a system that can help tax accountant who have existing professional abilities by using data mining techniques on accumulated tax related data. The data mining technique used is random forest and improved by using f1-score. Using the implemented system, data accumulated over two years was learned, showing high accuracy at prediction.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.500-509
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• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.71-75
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• 2024

This research focuses on processing unstructured data efficiently, containing various formulas in document processing and management regarding the terms and rules of domestic insurance documents using text mining techniques. Through parsing and compilation technology, document context, content, constants, and variables are automatically separated, and errors are verified in order of the document and logic to improve document accuracy accordingly. Through document debugging technology, errors in the document are identified in real time. Furthermore, it is necessary to predict the changes that intelligent document processing will bring to document management work, in particular, the impact on documents and utilization tasks that are double managed due to various formulas and prepare necessary capabilities in the future.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.17-22
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• 2024

Software cost and schedule estimation is usually based on the estimated size of the software. Advanced estimation techniques also make use of the diverse factors viz, nature of the project, staff skills available, time constraints, performance constraints, technology required and so on. Usually, estimation is based on an estimation model prepared with the help of experienced project managers. Estimation of software cost is predominantly a crucial activity as it incurs huge economic and strategic investment. However accurate estimation still remains a challenge as the algorithmic models used for Software Project planning and Estimation doesn't address the true dynamic nature of Software Development. This paper presents an efficient approach using the contemporary Constructive Cost Model (COCOMO) augmented with the desirable feature of fuzzy logic to address the uncertainty and flexibility associated with the cost drivers (Effort Multiplier Factor). The approach has been validated and interpreted by project experts and shows convincing results as compared to simple algorithmic models.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.505-514
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• 2012

This study is for effectiveness of golf skills to average score using path analysis in Professional golf association. The variables in this study were that seven independent variable were driving accuracy, green in regulation, driving distance, sand save ratio, scrambling, putting average, and two endogenous variables were birdie average, bogey average, and dependent variable was the scoring average. To analyze these variables, path analysis was used through AMOS 18.0 program and Alpha level sets at.05. As the result, the final model had significant goodness-of-fit (GFI=.989, RMSEA=.044, TLI=.991, CFI=.998) and showed that green in regulation, driving distance, sand save ratio, scrambling, and putting average significantly affected average score directly. Especially, the scrambling was the highest affectation to average score and the sand save ratio was the lowest affectation to the average score.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.121-128
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• 2024

With the recent rise in the use of AI-based online translation tools, interest in their methods and effects on education has grown. This study involved 30 prospective elementary school teachers who completed an English writing task using an AI-based online translation tool. The study focused on assessing the impact of these tools on English writing skills and their practical applications. It examined the usability, educational value, and the advantages and disadvantages of the AI translation tool. Through data collected via writing tests, surveys, and interviews, the study revealed that the use of translation tools positively affects English writing skills. From the learners' perspective, these tools were perceived to provide support and convenience for learning. However, there was also recognition of the need for educational strategies to effectively use these tools, alongside concerns about methods to enhance the completeness or accuracy of translations and the potential for over-reliance on the tools. The study concluded that for effective utilization of translation tools, the implementation of educational strategies and the role of the teacher are crucial.

• Journal of Music and Human Behavior
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• v.16 no.1
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• pp.47-72
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• 2019

This study reviewed and analyzed English-written studies using a rhythmic tapping task for motor control of children with autism spectrum disorder (ASD). Inclusion criteria for the participants were children with ASD and typically developing (TD) children. The keywords used for the outcome variables included rhythmic tapping, timed movement, and synchronization. Ten studies were included in the final analysis. The included studies were analyzed in terms of target variables, auditory stimuli, and measurements. A meta-analysis was also conducted to examine how children with ASD performed rhythmic tapping tasks compared to children with TD. In the identified studies, five variables were used: timed movement control, timing reproduction, bimanual coordination, synchronization, and interpersonal synchronization. It was found that rhythmic tapping performance was analyzed in terms of accuracy and precision of the movement and reported as significantly correlated to social skills measures. The meta-analysis results showed that there were no significant differences between the ASD and TD groups in continuing rhythmic movements when the presented auditory stimuli ended, whereas there were significant group differences in their ability to maintain their motor performance consistently and to synchronize with auditory cue or with others. These results support the rhythmic tapping task as an effective measure for not only motor control but also social skills development in children with ASD.

• Journal of Korea Water Resources Association
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• v.47 no.11
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• pp.1039-1050
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• 2014

The goal of this study is to demonstrate the diversity of model performance for various climatic elements and indicators. We evaluated the skills of the most advanced 17 General Circulation Models (GCMs) i.e., CMIP5 (Climate Model Inter-comparison project, phase 5) climate models in reproducing retrospective climatology from 1950 to 2000 over the Southeast US for the key climatic elements important in the hydrological and agricultural perspectives (i.e., precipitation, maximum and minimum temperature, and wind speed). The biases of raw CMIP5 GCMs were estimated for 16 different climatic indicators that imply mean climatology, temporal variability, extreme frequency, etc. using a grid-based observational dataset as reference. Based on the error (RMSE) and correlation (R) of GCM outputs, the error-based GCM ranks were assigned on average over the indicators. Overall, the GCMs showed much better accuracy in representing mean climatology of temperature comparing to other elements whereas few GCM showed acceptable skills for precipitation. It was also found that the model skills and ranks would be substantially different by the climatic elements, error statistics applied for evaluation, and indicators as well. This study presents significance of GCM uncertainty and the needs of considering rational strategies for climate model evaluation and selection.