• Journal of Korean society of Dental Hygiene
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• v.18 no.3
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• pp.269-280
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• 2018

Objectives: The purpose of this study was to investigate the change in the posture of dental hygiene students and clinical dental hygienists when implementing dental scaling before and after posture correction training using the rapid upper limb assessment (RULA) method and 3D motion analysis. Methods: Thirty-two healthy volunteers performed dental scaling to remove artificial calculus on dental manikin. The movement and angle of the joints were verified by RULA and 3D motion analysis during the procedure. The subjects were also photographed for 1 minute during the procedure for 10 minutes while the calculus was removed. After the removal of the calculus, the subject and the instructor checked the video together. Posture correction training was conducted by the instructor so that the subject could perform the calculus removal operation in the correct posture. Artificial calculus of the adjacent teeth was then removed for the same period of time, and the change in posture was reviewed. Results: The total score of the posture change using RULA was $5.72{\pm}0.58$ before training and $4.31{\pm}0.10$ after training, showing a significant decrease after training (p<0.001), and upper arm, lower arm, wrist position, neck and waist position showed significant decrease after training. The three-dimensional motion analysis showed significant differences according to the criteria measured at all measurement sites except the left shoulder (p<0.05) Conclusions: It was confirmed through RULA and 3D motion analysis that postural correction training using calculus removal images was effective, and that correct postural education is essential to preventing musculoskeletal diseases caused by removal of calculus.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.12
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• pp.96-104
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• 2009

In the real system environments, the performance of the application is not linearly proportional to the clock frequency of the microprocessor, in contrast to the general assumption of conventional dynamic voltage scaling. In this paper, we analytically model the relation between the performance of the application and the clock frequency of the microprocessor, and introduce the energy-optimal scheduling algorithm for a task set with distinct application characteristics. In addition, we present a theorem for the energy-optimal scheduling, which the derivative of the energy consumption with respect to the execution time should be the same for all the tasks. The proposed scheduling algorithm always generates the energy-optimal scaling factor thanks to the theorem for energy-optimal scheduling. We achieved about 7% additional energy reduction in the experiments using synthetic task sets.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.2
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• pp.95-103
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• 2007

Dynamic voltage scaling (DVS) is a well-known and effective power management technique. While there has been research on slack distribution, voltage allocation and other aspects of DVS, its effects on non-voltage-scalable devices has hardly been considered. A DC-DC converter plays an important role in voltage generation and regulation in most embedded systems, and is an essential component in DVS-enabled systems that scale supply voltage dynamically. We introduce a power consumption model of DC-DC converters and analyze the energy consumption of the system including the DC-DC converter. We propose an energy-optimal off-line DVS scheduling algorithm for systems with DC-DC converters, and show experimentally that our algorithm outperforms existing DVS algorithms in terms of energy consumption.

• Journal of Korean society of Dental Hygiene
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• v.7 no.4
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• pp.521-534
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• 2007

The purpose of this study to provide base data of various dental hygiene management systems are necessary to improve the oral health of visitor oral prophylaxis practice units, investigating and analyzing the oral health behavior and awareness of 130 scaling patients who visited the oral prophylaxis practice units of J Health College from April to May of the year 2007. The following conclusions were obtained 1. 50% of them had more than 2 times of toothbrushing a day, and 45.5% had more than 3 times of toothbrushing a day. 2. Toothbrushing was done after having a breakfast in 75.4% and 71.5% brushed their teeth after having a dinner. As the time to brush teeth, 45.4% of the subjects spentless than 3 minutes and 39.2% of them spent less than 2 minutes, and 48.5% of them bushed their teeth in up and down directions and 43.8% used mixed approaches. 3. The usage period of a toothbrush lasted about 3 month in 33.1% and 26.2% used a toothbrush about 2 month, and 20% of the subjects had the experience of using dental floss or interdental brush. 4. 61.5% of the subjects had the experience of having scaling treatment. The frequency of scaling was found to be 38.5%. 5. As the cause of having caries of the teeth, 73.8% responded it as unfaithfully brushing and 50% the subjects considered smoking is very harmful to dental health. 6. The most important behavior for dental health was found to be not eating sugars that were pointed out by 75.4% of subjects. Based upon the above listed study results, various dental hygiene management systems are necessary to improve the oral health of patients who visit oral prophylaxis practice units, especially, the correct toothbrushing and periodic oral examination with preventive scaling were thought to be necessary.

• Journal of Periodontal and Implant Science
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• v.42 no.6
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• pp.243-247
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• 2012

Purpose: The effects of magnetostrictive and piezoelectric devices on tooth surfaces seem to differ with regard to the root surface roughness they produce. This study aimed to compare the results of scaling using magnetostrictive and piezoelectric devices on extracted teeth. Methods: Forty-four human extracted teeth were assigned to four study groups (n=11). In two groups (C100 and C200), the teeth were scaled using a magnetostrictive device and two different lateral forces: 100 g and 200 g, respectively. In the other two groups (P100 and P200), the teeth were scaled with a piezoelectric device with 100 g and 200 g of lateral force, respectively. The teeth were scaled and the data on the duration of scaling and the amount of surface were collected and analyzed using the t-test. Results: The mean time needed for instrumentation for the piezoelectric and magnetostrictive devices was 50:54 and 41:10, respectively, but their difference was not statistically significant (P=0.171). For root surface roughness, we only found a statistically significantly poorer result for the C200 group in comparison to the P200 group (P=0.033). Conclusions: This study revealed that applying a piezoelectric scaler with 200 g of lateral force leaves smoother surfaces than a magnetostrictive device with the same lateral force.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.5_6
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• pp.299-306
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• 2003

This paper formulates a problem of embedded real-time system re-engineering, and presents its solution approach. Embedded system re-engineering is defined as a development task of meeting performance requirements newly imposed on a system after its hardware and software have been fully implemented. The performance requirements nay include a real-time throughput and an input-to-output latency. The proposed solution approach is based on a bottleneck analysis and nonlinear optimization. The inputs to the approach include a system design specified with a process network and a set of task graphs, task allocation and scheduling, and a new real-time throughput requirement specified as a system's period constraint. The solution approach works in two steps. In the first step, it determines bottleneck precesses in the process network via estimation of process latencies. In the second step, it derives a system of constraints with performance scaling factors of processing elements being variables. It then solves the constraints for the performance staling factors with an objective of minimizing the total hardware cost of the resultant system. These scaling factors suggest the minimal cost hardware upgrade to meet the new performance requirement. Since this approach does not modify carefully designed software structures, it helps reduce the re-engineering cycle.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.135-146
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• 2010

The development of image technologies in such area as broadcasting and movies has recently increased our attention to 3D stereoscopic images. In addition, the development of stereoscopic image representation technologies in 3D contents becomes more active over time due to the representational limitations of 2D images. Without limitation to the above-mentioned area, stereoscopic image technologies have been developed and studied so that they can be widely accessed in diverse areas including medical services and education. Due to the refined production, however, required to represent a three dimensional effects and the fatigue caused by the perception of a three dimensional effects, the stereoscopic image technologies are not combined into real time systems such as games where environments change unforeseeably. In this study we design a technique to adjust the depth scaling that will enable efficient management of a three dimensional effects and to relieve fatigue through automatic view point interval adjustment in accordance with situations based on the geometrical structure of the DirectX SDK graphic pipeline. Through this, we would like to suggest a new alternative idea to activate the production of games combined with stereoscopic image technologies.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.9 no.3
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• pp.115-124
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• 1997

We measured the variance of eddy diffusion and associated ‘diffusion coefficients’ in coastal regions of Korea by observing the separation distances among multiple drifters deployed simultaneously at the same initial position. The variance of eddy diffusion was found to be proportional to $t^m$, where t is the time and m is a non-integer scaling exponent between 1.5 and 3.5. The observed scaling exponent of eddy diffusion cannot be reproduced by diffusion models employing constant eddy diffusivity. In this study, we applied fractal theory in simulating exponential increase of variance of eddy diffusion. We employed the fGn(fractional Gaussian noise) as a ‘modified’ random walks corresponding to the oceanic eddy diffusion. The variance of eddy diffusion, which corresponds to the fBm(fractional Brown motion) of our diffusion model, is proportional to $t^{2H}$, where H is Hurst scaling exponent. The temporal increase of the variance. with scaling exponent between 1 and 2, was successfully reproduced by our fractal diffusion model. However, our model cannot reproduce scaling exponent greater than 2. The scaling exponents greater than 2 are associated with the velocity shear of the mean flow.

• 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.