• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Journal of dental hygiene science
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• v.17 no.4
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• pp.333-340
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• 2017

The prevalence of periodontal disease was steadily increased. The best prevention methods for periodontal disease are teeth brushing and scaling. The purpose of this study was to investigate the status of scaling experience and related factors among some workers. Total 455 workers in 5 manufacturing companies in Gwangju were selected using convenience sampling method. General characteristics, work-related characteristics, oral health-related characteristics and scaling experience were collected by self-reported questionnaires. Chi-square tests, t-tests and multiple logistic regression analysis were performed to investigate the factors influencing the scaling experience using SPSS software. Statistical significance was defined as a p-value<0.05. The proportion of scaling experience during the past year was 47.0%. In simple analysis, age, current working position, number of oral disease, interest in oral health, use of secondary oral products, oral health screening use, oral health education experience and awareness of scaling inclusion in the National Health Insurance (NHI) coverage were associated with scaling experience. Finally, the odds ratios (ORs) for scaling experience were significantly higher in younger subjects (adjusted OR [aOR], 3.09; 95% confidence internal [CI], 1.60~5.96), assistant manager (aOR, 2.68; 95% CI, 1.55~4.63), subjects with high interest in oral health (aOR, 2.15; 95% CI, 1.02~4.52), subjects with oral health screening use (aOR, 2.76; 95% CI, 1.50~5.11) and awareness of scaling inclusion in the NHI coverage (aOR; 2.91, 95% CI, 1.80~4.72) in multiple logistic regression analysis. Scaling experience was relatively low (47.0%). The related factors with scaling experience were age, working position, use of screening and awareness of scaling inclusion in the NHI coverage. Considering these factors will increase the utilization rate of scaling.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.111-118
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• 2024

An in-structure response spectrum (ISRS) is required to evaluate the seismic performance of a nuclear power plant (NPP). However, when a new ISRS is required because of the change in the unique spectrum of an NPP site, considerable costs such as seismic response re-analyses are incurred. This study provides several approaches to generate approximate methods for ISRS scaling, which do not require seismic response re-analyses. The ISRSs derived using these approaches are compared to the original ISRS. The effect of the ISRS of the approximate method on the seismic response and seismic performance of one of the main systems of an NPP is analyzed. The ISRS scaling approximation methods presented in this study produce ISRSs that are relatively similar at low frequencies; however, the similarity decreases at high frequencies. The effect of the ISRS scaling approximate method on the calculation accuracy of the seismic response/seismic performance of the system is determined according to the degree of similarity in the calculation of the system's essential mode responses for the method.

• Journal of Environmental Science International
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• v.29 no.1
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• pp.15-23
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• 2020

The determination of soil characteristics is important in the simulation of rainfall runoff using a distributed FLO-2D model in catchment analysis. Digital maps acquired using remote sensing techniques have been widely used in modern hydrology. However, the determination of a representative parameter with spatial scaling mismatch is difficult. In this investigation, the FLO-2D rainfall-runoff model is utilized in the Yongdam catchment to test sensitivity based on three different methods (mosaic, arithmetic, and predominant) that describe soil surface characteristics in real systems. The results show that the mosaic method is costly, but provides a reasonably realistic description and exhibits superior performance compared to other methods in terms of both the amount and time to peak flow.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.20-23
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• 2019

Demand response is usually operated through using the power rates and incentives. Demand management based on power charges is the most rational and efficient demand management method, and such methods include rolling base charges with peak time, sliding scaling charges depending on time, sliding scaling charges depending on seasons, and nighttime power charges. Search for other methods to stimulate resources on demand by actively deriving the demand reaction of loads to increase the energy efficiency of loads. In this paper, ESS algorithm for saving energy based on predicting the amount of solar power generation that can be used for buildings with small loads not under electrical grid.

• Journal of dental hygiene science
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• v.16 no.6
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• pp.472-480
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• 2016

In this study, 110 adults aged 40 to 69 years were surveyed from April 28, 2016, to May 28, 2016 to analyze their periodontal status according to daily stress, coping methods, and oral health behavior. The collected data were analyzed using the t-test, one-way analysis of variance, and hierarchical multiple regression. Daily stress levels of all subjects were most frequent potential risk 64.5% of the subjects, the high risk 19.1% and 16.4% of the health group. Regarding stress coping methods, active methods recorded 2.46, passive methods recorded 2.32. Regarding oral health behaviors, subjects brushed an average of 2.45 times daily, for an average of 2 minutes. Futhermore, 69.1% of subjects brushed before bedtime and 89.1% practiced scaling. Regarding periodontal status, the O'Leary index was 73.45, gingivitis index was 1.30, an average of 2.83 quadrants possessed a periodontal pocket. The hierarchical multiple regression analysis identified, type of employment (${\beta}=-0.348$), scaling (${\beta}=-0.253$), and age (${\beta}=0.244$) as factors that influence the number of quadrants possessing a periodontal pocket. These results confirmed that the oral health behavior of scaling, but not stress levels of coping methods, strongly influenced periodontal status.

• Journal of Periodontal and Implant Science
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• v.27 no.3
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• pp.575-584
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• 1997

The quality of periodontal instrument cutting edge is a basic element of effective root planing procedure. Using instruments, the sharp edge is changed into blunt or beveled edge. With the blunt instrument, the periodontal treatment can't be carried into accuracy and effective. The study on the wear of periodontal curet is insufficient, there are few publications about the change of sharpness of cutting egde after using instrument and a certen reports were published on the study of scanning electron microscope(SEM) examination. In this study, to declare the number of strokes for sharpening of instruments, the changes of cutting edge is measured by the clinical methods, tactile sensitivity examination and refraction light-white line test after scaling strokes and root planing strokes. SEM test was added for defined the changes of cutting edges. The 7/8 Gracey curets that have been never used was tested. Maxillary molars which were extracted from the School of Dental Medicine, Dankook University was used. Subjected teeth had attachment loss more than 6 mm in bucca-lingual surface and sufficient calculus of a band type in cervical area. The strokes of curet were executed 3, 5, 7, 9, 11, 13 times on scaling stroke and 10, 15, 20, 25, 30, 35 times on root planing stroke. A resident has periodontal experience over 3 years carried out the clinical examinations those tactile sensitivity examination and refraction light-white line test 5 times. The case there being tactile sensitivity certenly is 2, the case being felt tactile sensitivity is 1, and the case there not being tactile sensitivity is 0. The visual examination was recorded as following. The case that refracted white line is not recognised is 2, the case that uncerten is 1, and the case that acknowledged is 0. The results were obtained as follows. 1. After scaling strokes, the tactile sensitivity was reduced after 11 strokes and disappeared in 13 strokes. 2. In tactile sensitivity after root planing procedures, sensitivity was reduced after 25 strokes and disappeared in 35 strokes. 3. In case of visual examination, the detection of refracted white line was increased after 9 strokes of scaling procedures and the accuracy of wear wasn't showed after root planing procedures. 4. In SEM, metal projection was observed on new periodontal curet cutting edge and it was disappeared after scaling procedures. 5. In SEM, the cutting edge was showed changing linear into an aspect of the surface after 5 strokes of scaling procedures and 10 strokes of root planing procedures and showed beveled edge in 11 strokes of scaling procedures, 25 strokes of root planing procedures. The results of 3-type examination indicated that the sharpening of curet should be performed after 11 strokes of scaling procedures and 25 strokes of root planing procedures.

• The Journal of Korean Physical Therapy
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• v.20 no.3
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• pp.45-51
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• 2008

Purpose: We evaluated the physical stress and pain to the musculoskeletal system of a dental practitioner when engaging in a dental scaling training exercise to prevent the development of musculoskeletal injuries. Methods: The 18 female (average age: 21$\pm$1 years) subjects were voluntarily picked from a group of juniors who have completed a one-and-a-half year training course that includes training exercises on the dentiform and on live subjects (other trainees). The test is done by measuring pain, activity, grip strength, and finger dexterity for each subject's hand and wrist. Before the test all subjects were confirmed to be right-handed and were informed of the study and its objective. Measuring was done before and after each subject performed dental scaling for one hour using the scaler and the curet. Results: Pain levels increased for both hand and shoulders, but hand pain was often greater than shoulder pain. Grip strength significantly declined in the right hand but not the left. For joint mobility, the flexion and the extension for the shoulder joint did not change; but the range of motion for both wrist joints significantly increased. For the dexterity test, both hands showed increased dexterity after the exercise. Conclusion: Dental scaling can affect the shoulders and wrists/hands. Therefore, a musculoskeletal injury prevention program for dental practitioners, which may include encouraging them to assume correct body posture when at work, must be sought. This study evaluated only the shoulders, wrists, and hands; but future studies should include areas such as the cervical area, the back, and the lower limbs.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.427-431
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• 2010

Multidimensional scaling (MDS) is a widely used method for dimensionality reduction, of which purpose is to represent high-dimensional data in a low-dimensional space while preserving distances among objects as much as possible. MDS has mainly been applied to data visualization and feature selection. Among various MDS methods, the classical MDS is not readily applicable to data which has large numbers of objects, on normal desktop computers due to its computational complexity. More precisely, it needs to solve eigenpair problems on dissimilarity matrices based on Euclidean distance. Thus, running time and required memory of the classical MDS highly increase as n (the number of objects) grows up, restricting its use in large-scale domains. In this paper, we propose an efficient approximation algorithm for the classical MDS based on divide-and-conquer and CUDA. Through a set of experiments, we show that our approach is highly efficient and effective for analysis and visualization of data consisting of several thousands of objects.