• Journal of Korean Physical Therapy Science
• /
• v.10 no.1
• /
• pp.83-89
• /
• 2003

An electromyogram (EMG) using surface electrodes is one of the indirect tests most frequently used to ascertain muscle fatigue. An EMG can be used in two ways. The first technique determines the root mean square (RMS), which reflects the amplitude of the EMG signal. The second technique determines the median and mean power frequencies through EMG power spectrum analysis. The purpose of this article is for determine the correlation between work and percent root mean square(%RMS) and between work and MDF of EMG based on muscle contractions. It is used the %RMS, which reflects the amplitude of the EMG signal For MDF, it is used the frequency power spectrum analysis method, which involves the fast Fourier transformation (FFT) of the original Signals.

• Journal of Communications and Networks
• /
• v.9 no.1
• /
• pp.18-27
• /
• 2007

The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first- and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a non-stationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.

• Journal of Welding and Joining
• /
• v.19 no.3
• /
• pp.298-304
• /
• 2001

Turbine blade is subject to force of three types ; the torsional force by torsional mount, the centrifugal force by the rotation of rotor and the cyclic bending force by steam pressure. The cyclic bending force was a main factor on fatigue strength. SEM fractography in root of turbine blade showed micro-clack width was not dependent on stress intensity factor range. Especially, fatigue did not exist on SEM photograph in root of turbine blade. To clear out the fracture mechanism of turbine blade, nanofractography was needed on 3-dimensional crack initiation and crack growth with high magnification. Fatigue striation partially existed on AFM photograph in root of turbine blade. Therefore, to find a fracture mechanism of the torsion-mounted blade in nuclear power plant, the relation between stress intensity factor range and surface roughness measured by AFM was estimated, and then the load amplitude ΔP applied to turbine blade was predicted exactly by root mean square roughness.

• Journal of the Korean Society of Systems Engineering
• /
• v.16 no.1
• /
• pp.58-67
• /
• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

• ETRI Journal
• /
• v.25 no.6
• /
• pp.437-444
• /
• 2003

We derive a new joint power and rate control rule with which we can minimize the mean transmission delay in CDMA networks for a given mean transmission power. We show that it is optimal to respectively control the power inverse-linearly and the rate linearly to the square root of channel gain while maintaining the signal-to-interference ratio at a constant. We also show that the proposed joint power/rate control rule achieves excellent performance results in terms of the probability of the instantaneous delay being within a target delay against one-dimensional control schemes.

• Korean Journal of Organic Agriculture
• /
• v.25 no.2
• /
• pp.279-293
• /
• 2017

This study suggests the yield forecast models for autumn chinese cabbage and radish using crop growth and development information. For this, we construct 24 alternative yield forecast models and compare the predictive power using root mean square percentage errors. The results shows that the predictive power of model including crop growth and development informations is better than model which does not include those informations. But the forecast errors of best forecast models exceeds 5%. Thus it is important to establish reliable data and improve forecast models.

• The Korean Journal of Applied Statistics
• /
• v.31 no.6
• /
• pp.785-799
• /
• 2018

With the development of renewable energy sector, the importance of solar energy is continuously increasing. Solar radiation forecasting is essential to accurately solar power generation forecasting. In this paper, we used time series models (ARIMA, ARIMAX, seasonal ARIMA, seasonal ARIMAX, ARIMA GARCH, ARIMAX-GARCH, seasonal ARIMA-GARCH, seasonal ARIMAX-GARCH). We compared the performance of the models using mean absolute error and root mean square error. According to the performance of the models without exogenous variables, the Seasonal ARIMA-GARCH model showed better performance model considering the problem of heteroscedasticity. However, when the exogenous variables were considered, the ARIMAX model showed the best forecasting accuracy.

• Wind and Structures
• /
• v.30 no.1
• /
• pp.37-54
• /
• 2020

To investigate the motion-induced aeroelastic effects (or aerodynamic feedback effects) on a square cylinder in uniform flow, a series of wind tunnel tests involving the pressure measurement of a rigid model (RM) and simultaneous measurement of the pressure and vibration of an aeroelastic model (AM) have been systematically carried out. More specifically, the aerodynamic feedback effects on the structural responses, on the mean and root-mean-square wind pressures, on the power spectra and coherence functions of wind pressures at selected locations, and on the aerodynamic forces were investigated. The results indicated the vibration in the lock-in range made the shedding vortex more coherent and better organized, and hence presented unfavorable wind-induced effects on the structure. Whereas the vibration in the non-lock-in range generally showed insignificant effects on the flow structures surrounding the square cylinder.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2013.04a
• /
• pp.793-794
• /
• 2013

• Nuclear Engineering and Technology
• /
• v.53 no.12
• /
• pp.4179-4188
• /
• 2021

This study identifies efficient earthquake intensity measures (IMs) for seismic performances and fragility evaluations of the reactor containment building (RCB) in the advanced power reactor 1400 (APR1400) nuclear power plant (NPP). The computational model of RCB is constructed using the beam-truss model (BTM) for nonlinear analyses. A total of 90 ground motion records and 20 different IMs are employed for numerical analyses. A series of nonlinear time-history analyses are performed to monitor maximum floor displacements and accelerations of RCB. Then, probabilistic seismic demand models of RCB are developed for each IM. Statistical parameters including coefficient of determination (R²), dispersion (i.e. standard deviation), practicality, and proficiency are calculated to recognize strongly correlated IMs with the seismic performance of the NPP structure. The numerical results show that the optimal IMs are spectral acceleration, spectral velocity, spectral displacement at the fundamental period, acceleration spectrum intensity, effective peak acceleration, peak ground acceleration, A95, and sustained maximum acceleration. Moreover, weakly related IMs to the seismic performance of RCB are peak ground displacement, root-mean-square of displacement, specific energy density, root-mean-square of velocity, peak ground velocity, Housner intensity, velocity spectrum intensity, and sustained maximum velocity. Finally, a set of fragility curves of RCB are developed for optimal IMs.