• Journal of Korean society of Dental Hygiene
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• v.19 no.6
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• pp.1067-1075
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• 2019

Objectives: The present study aimed to compare the accuracy of removable partial denture (RPD) frameworks fabricated by selective laser sintering (SLS) before and after electropolishing. Methods: A partially edentulous mandibular model was used as the working model. Scanning of the model was performed using a dental scanner. The framework was designed using CAD software. The metal framework was formed using an SLS 3D printer. 3D scans of the two fabricated prototypes produced before and after electropolishing were overlapped with reference data. The fit was calculated based on Root Mean Square (RMS). Fabrication accuracy was verified using the paired t-test to compare the discrepancy before and after electropolishing. Results: The mean (SD) values of RMS before and after electropolishing were 126.6 (34.19) and 75.86 (21.36), respectively. There was a statistically significant difference before and after electropolishing (p<0.05). Conclusions: Metal frameworks made with SLS 3D printers showed clinically acceptable fit after electropolishing.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.330-335
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• 1999

The goal of the study is to determine the most sensitive parameter to represent the degree of muscle force and fatigue. Various numerical parameters such as the first coefficient of Autoregressive (AR) Model, Root Mean Square (RMS), Zero Crossing Rate (ZCR), Mean Power Frequency (MPF), Median Frequency (MF) were tested in this study. Ten healthy male subjects participated in the experiment. They were asked to extend their trunk by using the right and left erector spinae muscles during a sustained isometric contraction for twenty seconds. The force levels were 15%, 30%, 45%, 60%, and 75% of Maximal Voluntary Contraction (MVC), and the order of trials was randomized. The results showed that RMS was the best parameter to measure the force level of the muscle, and that the first coefficient of AR model was relatively sensitive parameter for the fatigue measurement at less than 60% MVC condition. At the 75% MVC, however, both MPF and the first coefficient of AR Model showed the best performance in quantification of muscle fatigue. Therefore, the sensitivity of measurement can be improved by properly selecting the parameter based upon the level of force during a sustained isometric condition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• Proceedings of the KSRS Conference
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• v.1
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• pp.394-397
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• 2006

Gridded products of surface wind/wind-stress over the world ocean have been constructed by using satellite scatterometer as the Japanese Ocean Flux data sets with Use of Remote-sensing Observation (J-OFURO) data. Our previous validation study in the tropical Pacific using TAO/Triton and NDBC buoys revealed high reliability of our products. In this study, the Kuroshio Extension Observatory (KEO) buoy data are used for validation of other gridded wind-stress products including the NCEP-1 and 2 in the western North Pacific region where there have been few in-situ data. Results reveal that our J-OFURO product has almost zero mean difference and smallest root-mean-square (RMS) difference, while the NCEP-1 and 2 ones significantly positive biases and relatively high RMS difference. Intercomparison between the J-OFURO and NCEP products in a wide region of the North Pacific covered by the westerly winds exhibits that the NCEPs have larger magnitudes in the wind stress than the J-OFURO's, suggesting overestimation of the NCEPs.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.92.1-92.1
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• 2012

In this work we have examined the performance of the WSA/ENLIL cone model provided by Community Coordinated Modeling Center (CCMC). The WSA/ENLIL model simulates the propagation of coronal mass ejections (CMEs) from the Sun into the heliosphere. We estimate the shock arrival times at the Earth using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters from Michalek et al. (2007) as well as their associated interplanetary (IP) shocks. We make a comparison between CME arrival times by the WSA/ENLIL cone model and IP shock observations. For the WSA/ENLIL cone model, the root mean square(RMS) error is about 13 hours and the mean absolute error(MAE) is approximately 10.4 hours. We compared these estimates with those of the empirical model by Kim et al.(2007). For the empirical model, the RMS and MAE errors are about 10.2 hours and 8.7 hours, respectively. We are investigating several possibilities on relatively large errors of the WSA/ENLIL cone model, which may be caused by cone model velocities, CME density enhancement factor, or CME-CME interaction.

• The Journal of Korean Physical Therapy
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• v.21 no.4
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• pp.51-56
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• 2009

Purpose: This study examined the difference in the static balance ability according to the visual cues and postural tasks in normal subjects. Methods: Thirty participants (12 male, 18 female; mean age $24.63\pm1.43$ years) stood barefoot on a force platform in a one-legged stance, tandem Romberg stance and tandem Romberg with neck extension stance with a visual cue open and closes. The static balance was assessed by the center of pressure (CoP), surface electromyography root mean square (RMS) of the leg muscles according to the stance position. Results: In the CoP tests, the difference in the unit path length and circumference area was affected by the visual cue according to the stance posture (p<0.01). In the RMS tests, the difference in the tibialis anterior and medial gastrocnemius muscle was affected by visual cue in accordance with the stance posture (p<0.01). Conclusion: The visual cue and postural task affect the balance ability in normal subjects. Therefore, this study provides clinical evidence that the balance and postural control can be improved. Therapeutic intervention, such as an obstacle course, and a lower leg muscle performance program with a change in the base of support can affect the balance and postural control.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.142-146
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• 2009

The purpose of this study is to investigate the angular velocity of forearm pronation/supination movement in Parkinson's disease patients, as a quantitative measure of the bradykinesia. Thirteen Parkinson's disease patients ($64{\pm}11.0$ yrs, male:6, female:7) participated in the experiments. The subjects' both right and left forearms were scored by a rater according to rapid alternating movement of hands category in the UPDRS (unified Parkinson's disease rating scale) and the angular velocity of forearm pronation/supination was measured at the same time. As analysis parameters, RMS (root mean square) angular velocity and RMS angle were used. The parameters showed negative correlation with the clinical score (RMS angular velocity: r= - 0.914, RMS angle: r= -0.749). The RMS angular velocity of all clinical scales were significantly different one another except for the non significant difference between those of scale 3 and 4. RMS angle of scale 0 was significantly different from those of scale 2, 3, 4 and that of scale 1 was significantly different from those of scale 3 and 4. This suggests that RMS angular velocity can be used for a quantitative measure of bradykinesia in motor examination.

• Clinical and Experimental Pediatrics
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• v.61 no.3
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• pp.78-83
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• 2018

Purpose: Frequent desaturation due to immature incoordination of suck-swallow-breathing in preterm infants can influence multiple organs such as the heart, lungs, and brain, which can then affect growth and development. Most notably in preterm infants, feeding desaturation may even affect pulmonary function during gavage feeding. Because respiratory muscle activities may reflect the work required during respiration, we evaluated the differences in these activities between full-term and preterm infants with feeding desaturation, and investigated the correlations with clinical variables. Methods: Nineteen preterm infants with feeding desaturation (group 1) and 19 age-matched full-term infants (group 2) were evaluated. Oromotor function was evaluated using video recording. The root-mean-square (RMS) envelope of the electromyography signal was calculated to quantify the activities of muscles involved in respiration. The differences in RMS between both groups and the correlation with clinical variables including gestational age (GA), birth weight (BW), and Apgar scores (AS) at 1 and 5 minutes after birth were evaluated. Results: The RMS values of the diaphragm (RMS-D) and rectus abdominis (RMS-R) were significantly greater in group 1 compared to group 2, and the 1- and 5-min AS were significantly lower in group 1 compared to group 2. RMS-D and RMS-R were inversely correlated with GA, BW, 1- and 5-min AS in all infants. Conclusion: This study showed that respiratory muscle activities were augmented during feeding in preterm infants compared to full-term infants. Additionally, respiratory muscle activities were inversely correlated with all clinical variables.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.51.2-51.2
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• 2010

We have investigated 63 intense geomagnetic storms (Dst $\leq$ -100 nT) that occurred from 1998 to 2006. Using these events, we compared Dst forecast models: Burton et al. (1975), Fenrich and Luhmann (1998), O'Brien and McPherron (2000a), Wang et al. (2003), and Temerin and Li (2002, 2006) models. For comparison, we examined a linear correlation coefficient, RMS error, the difference of Dst minimum value (${\Delta}$peak), and the difference of Dst minimum time (${\Delta}$peak_time) between the observed and the predicted during geomagnetic storm period. As a result, we found that Temerin and Li model is mostly much better than other models. The model produces a linear correlation coefficient of 0.94, a RMS (Root Mean Square) error of 14.89 nT, a MAD (Mean Absolute Deviation) of ${\Delta}$peak of 12.54 nT, and a MAD of ${\Delta}$peak_time of 1.44 hour. Also, we classified storm events as five groups according to their interplanetary origin structures: 17 sMC events (IP shock and MC), 18 SH events (sheath field), 10 SH+MC events (Sheath field and MC), 8 CIR events, and 10 nonMC events (non-MC type ICME). We found that Temerin and Li model is also best for all structures. The RMS error and MAD of ${\Delta}$peak of their model depend on their associated interplanetary structures like; 19.1 nT and 16.7 nT for sMC, 12.5 nT and 7.8 nT for SH, 17.6 nT and 15.8 nT for SH+MC, 11.8 nT and 8.6 nT for CIR, and 11.9 nT and 10.5 nT for nonMC. One interesting thing is that MC-associated storms produce larger errors than the other-associated ones. Especially, the values of RMS error and MAD of ${\Delta}$peak of SH structure of Temerin and Li model are very lower than those of other models.