• Journal of Korean Society for Geospatial Information Science
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• v.6 no.1 s.11
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• pp.117-128
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• 1998

National digital maps (NDM) produced by diverse production methods through various stages are ready to distribute to public. The position accuracy problems in NDM should be inspected and evaluated to guarantee the quality of NDM. The purpose of this study is 1) to find out factors of impeding accuracy by examining the position accuracy of NDM on scales of 1:1,000 and 1:5,000, 2) to form the technical basis of making accurate digital maps and 3) to increase reliability and practical use of NDM. In this study, we found out 1) obstacles of making accurate mM especially in solving horizontal and vertical location accuracy problems and 2) error sources in production methods as well as stages. These results can be contributed to increase accuracy on modifying and upgrading NDM.

• Korean Journal of Applied Biomechanics
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• v.19 no.4
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• pp.729-738
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• 2009

The purpose of this study was to analyze the impact accuracy and kinematic parameters of skill level and eye-tracking type during putting strokes. For comparison, five elite golfers and five novice golfers participated in this study. Three-dimensional kinematic data were collected for each subject while 10 putting trials were performed for each skill level and eye-tracking type. The APAS system was used to compute the impact accuracy and kinematic parameters of putter heads. The putting stroke was divided into three phases: back swing, downswing, and follow-through. The findings indicated that significant differences were found in skill level as it affected the rate of success. For impact accuracy and the displacement of putter heads, a significant difference was found for the skill level, particularly in backs-wing and follow-through. In addition, the displacement of the putter head had a greater influence on stroke accuracy than on velocity.

• Survey Research
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• v.11 no.1
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• pp.1-18
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• 2010

We compare various imputation methods for compensating wave nonresponse that are commonly adopted in many panel surveys. Unlike the cross-sectional survey, the panel survey is involved a time-effect in nonresponse in a sense that nonresponse may happen for some but not all waves. Thus, responses in neighboring waves can be used as powerful predictors for imputing wave nonresponse such as in longitudinal regression imputation, carry-over imputation, nearest neighborhood regression imputation and row-column imputation method. For comparison, we carry out a simulation study on a few income data from the Korean Welfare Panel Study based on two performance criteria: predictive accuracy and estimation accuracy. Our simulation shows that the ratio and row-column imputation methods are much more effective in terms of both criteria. Regression, longitudinal regression and carry-over imputation methods performed better in predictive accuracy, but less in estimation accuracy. On the other hand, nearest neighborhood, nearest neighbor regression and hot-deck imputation show higher performance in estimation accuracy but lower predictive accuracy. Finally, the mean imputation shows much lower performance in both criteria.

• Physical Therapy Rehabilitation Science
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• v.7 no.2
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• pp.78-82
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• 2018

Objective: The purpose of this study was to determine the effect of wearing a pelvic compression belt on ball speed and accuracy in instep shoots of youth soccer players. Design: Randomized cross-over design. Methods: We included 20 male junior soccer players with experience of more than 5 years. Participants were randomly assigned to two conditions: application of a pelvic compression belt and instep shooting or no application. Instep shooting was performed three times at a distance of 20 meters from the position of the goal post, and the ball speed was measured using a speed gun at a position 5 meters behind the goal post. The shooting accuracy was measured based on a 5-point scale. The shooting accuracy was measured by scoring 5 points at 2.44 meters in the middle of the goal area of area A, 3 points at 2.44 meters in the goal area of area B, and 0 in the case of shooting outside the goal area C. Results: After applying a pelvic compression belt, the mean speed of the ball was significantly increased (p<0.05). The maximum speed of the ball was significantly increased (p<0.05). The accuracy of the ball was significantly increased (p<0.05). Conclusions: Through this study, we expect that the use of the pelvic compression belt can be applied as a training method to improve the shooting ability of soccer players. Clinically, pelvic compression belts are expected to help rehabilitation soccer players to improve their shooting accuracy.

• The Journal of Advanced Prosthodontics
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• v.13 no.5
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• pp.327-332
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• 2021

PURPOSE. The purpose of this in vitro study was to evaluate the accuracy of digitally designed removable partial denture (RPD) frameworks, constructed by additive and subtractive methods castable resin patterns, using comparative 3D analysis. MATERIALS AND METHODS. A Kennedy class III mod. 1 educational maxillary model was used in this study. The cast was scanned after modification, and a removable partial denture framework was digitally designed. Twelve frameworks were constructed. Two groups were defined: Group A: six frameworks were milled with castable resin, then casted by the lost wax technique into Co-Cr frameworks; Group B: six frameworks were printed with castable resin, then casted by the lost wax technique into Co-Cr frameworks. Comparative 3D analysis was used to measure the accuracy of the fabricated frameworks using Geomagic Control X software. Student's t-test was used for comparing data. P value ≤ .05 was considered statistically significant. RESULTS. Regarding the accuracy of the occlusal rests, group A (milled) (0.1417 ± 0.0224) showed significantly higher accuracy than group B (printed) (0.02347 ± 0.0221). The same results were found regarding the 3D comparison of the overall accuracy, in which group A (0.1501 ± 0.0205) was significantly more accurate than group B (0.179 ± 0.0137). CONCLUSION. In indirect fabrication techniques, subtractive manufacturing yields more accurate RPDs than additive manufacturing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.239-246
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• 2019

The drone photogrammetry is actively utilized for obtaining highly accurate spatial information and other various monitoring purposes. It is general to plan the drone photogrammetry by referring to previous experiences or cases in order to obtain the required accuracy, but the drone photogrammetry is often carried out again due to poor accuracy. Since the required spatial accuracy of the drone photogrammetry process result becomes the means of objective evaluation regardless of the type of result, it should be determined carefully. Therefore, it is necessary to determine flight height, overlap, number and arrangement of ground control point, and exterior orientation factor acquisition method in order to meet the required 3D positional accuracy for the design of drone photogrammetry project. In this study, previous study cases for the analysis of drone photogrammetry accuracy were carefully analyzed and verified by applying such cases to testing area, and design guideline of drone photogrammetry project for a small area was prepared based on the analysis result. The presented project design guideline is expected to be a great help to business practice although it is not perfect, and if the design guideline is prepared through comprehensive analysis in future, it would be possible to provide a perfect manual.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.35-44
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• 2020

In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1551-1565
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• 2020

On April 4, 2019, a forest fire started in Goseong County and lasted for three days, burning the neighboring areas of Sokcho. The strong winds moved the blaze from one region to another region and declared the worst wildfire in South Korea in years. More than 1,880 facilities, including 400 homes, were burnt down. The fire burned a total area of 529 hectares (1,307 acres), which involved 13,000 rescuers and 16,500 military troops to control the fire occurrence. Thousands of people were evacuated, and two people are dead. This study generated post-wildfire maps to provide necessary data for evacuation and mitigation planning to respond to this destructive wildfire, also prevent further damage and restore the area affected by the wildfire. This study used KOMPSAT-3A and Sentinel-2 imagery to map the post-wildfire condition. The SVM showed higher accuracy (overall accuracy 95.29%) compared with ANN (overall accuracy of 94.61%) for the KOMPSAT-3A. Moreover, for Sentinel-2, the SVM attained a higher accuracy (overall accuracy of 91.52%) than the ANN algorithm (overall accuracy 90.11%). In total, four post-wildfire burned area maps were generated; these results can be used to assess the area affected by the Sokcho wildfire and wildfire mitigation planning in the future.

• The Journal of Advanced Prosthodontics
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• v.15 no.4
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• pp.179-188
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• 2023

PURPOSE. This in vitro study aimed to evaluate the performance of digital intraoral scanners in a completely edentulous patient with angled and parallel implants. MATERIALS AND METHODS. A total of 6 implants were placed at angulations of 0°, 5°, 0°, 0°, 15°, and 0° in regions #36, #34, #32, #42, #44, and #46, respectively, in a completely edentulous mandibular polyurethane model. Then, the study model created by connecting a scan body on the implants was scanned using a model scanner, and a 3D reference model was obtained. Three different intraoral scanners were used for digital impressions (PS group, TR group, and CS group, n = 10 in each group). The distances and angles between the scan bodies in these measurement groups were measured. RESULTS. While the Primescan (PS) impression group had the highest accuracy with 38 ㎛, the values of 104 ㎛ and 171 ㎛ were obtained with Trios 4 IOSs (TR) and Carestream 3600 (CS), respectively (P = .001). The CS scanner constituted the impression group with the highest deviation in terms of accuracy. In terms of dimensional differences in the angle parameter, a statistically significant difference was revealed among the mean deviation angle values according to the scanners (P < .001). While the lowest angular deviation was obtained with the PS impression group with 0.185°, the values of 0.499° and 1.250° were obtained with TR and CS, respectively. No statistically significant difference was detected among the impression groups in terms of precision values (P > .05). CONCLUSION. A statistically significant difference was found among the three digital impression groups upon comparing the impression accuracy. Implant angulation affected the impression accuracy of the digital impression groups. The most accurate impressions in terms of both distance and angle deviation were obtained with the PS impression group.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.