• Journal of the Korean Society of Physical Medicine
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• v.16 no.1
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• pp.33-39
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• 2021

PURPOSE: This study compared the body composition according to the sport of short-distance and middle & long-distance athletes to identify the factors that affect the body composition. METHODS: Forty-eight athletes selected as youth national athletes in 2019 participated in the study. The study participants measured the length of the lower extremities, body composition, and anaerobic ability. An independent sample t-test was conducted to compare the body composition according to the event. In addition, the Pearson correlation was performed to identify the factors that influence the body composition. RESULTS: The leg length of the Short and Middle & long-distance athletes were similar (p = .584). On the other hand, there were differences in the body fat percentage (p = .001), lean percentage (p = .001), and BMI (p = .001). In addition, the body fat percentage was correlated with the lean body mass (r = .419) and BMI (r = .447). Furthermore, the lean body mass was correlated with the BMI (r = .849) and the peak power (r = .662) and mean power (r = .686) of the anaerobic capacity. Moreover, the BMI was correlated with the peak power (r = .490) and mean power (r = .543) of the anaerobic capacity. The peak power of the anaerobic ability was correlated with the mean power (r = .931). CONCLUSION: The body composition differed according to the sport. The body fat percentage correlated with the lean body mass and the BMI. The lean body mass correlated with the BMI, peak power of anaerobic ability, and mean power. The BMI correlated with the anaerobic capacity.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.155-162
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• 1999

reliability from components reliability. In this case, it assumes that components failure is mutually independent, but it may not true in real systems. In this study, the mean cost per unit time is computed as the ratio of mean life to the mean cost. The mean life is obtained by the reliability function under power rule model. The mean cost is obtained by the mathematical model based on the inspection interval. A heuristic method is proposed to determine the optimal number of redundant units and the optimal inspection interval to minimize the mean cost per unit time. The assumptions of this study are as following : First, in the load-sharing k-out-of-n:G system, total loads are applied to the system and shared by the operating components. Secondly, the number of failed components affects the failure rate of surviving components as a function of the total load applied. Finally, the relation between the load and the failure rate of surviving components is set by the power rule model. For the practical application of the above methods, numerical examples are presented.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.915-918
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• 2001

In this paper we study a new convergence behavior of the least mean fourth (LMF) algorithm where the error raised to the power of four is minimized for a multiple sinusoidal input and Gaussian measurement noise. Here we newly obtain the convergence equation for the sum of the mean of the squared weight errors, which indicates that the transient behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant. It should be noted that no similar results can be expected from the previous analysis by Walach and Widrow.

• Korean Journal of Optics and Photonics
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• v.8 no.4
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• pp.320-326
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• 1997

Broadband sources for fiber-optic gyroscope were constructed using erbium-doped fibers. Output power, linewidth, and mean wavelength were compared between four different source configurations. Among them, double pass configuration exhibited the highest output power, as high as 5.5 mW with 25 mW pumping at 1.48 ${\mu}{\textrm}{m}$ wavelength. It also showed nearly zero sensitivity of mean wavelength for the variation of pump power when a sufficient pumping was provided. Amplifier/Source configuration resulted in the highest detected power(power received by the gyro detector) that is more than 100 times larger than those of the other configurations, though it was the lowest of source output power. As the feedback level increased, the source power decreased while the linewidth increased, and mean wavelength varied significantly which would affect the scale factor of the gyroscope.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.161-167
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• 2019

Recently, demand forecasting techniques have been actively studied due to interest in stable power supply with surging power demand, and increase in spread of smart meters that enable real-time power measurement. In this study, we proceeded the deep learning prediction model experiments which learns actual measured power usage data of home and outputs the forecasting result. And we proceeded pre-processing with moving average method. The predicted value made by the model is evaluated with the actual measured data. Through this forecasting, it is possible to lower the power supply reserve ratio and reduce the waste of the unused power. In this paper, we conducted experiments on three types of networks: Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) and we evaluate the results of each scheme. Evaluation is conducted with following method: MSE(Mean Squared Error) method and MAE(Mean Absolute Error).

• Physical Therapy Korea
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• v.6 no.2
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• pp.16-31
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• 1999

The purpose of this study was to assess the fatigue in lumbar and abdominal muscles in patients with chronic low back pain compared with normal subjects using spectral analysis with mean power frequency and median power frequency. The experimental group consisted of twenty subjects who had experienced chronic low back pain for over one year after the onset day. A control group consisted of twenty normal subjects with no history of low back pain. All subjects stood in an apparatus to perform sustained contraction in the lumbar and abdominal muscles for 30 seconds with 60% maximal voluntary isometric contraction (MVIC). The resulting electromyographic (EMG) recorded time serial data were transformed into frequency serial data by Fast Fourier Transformation (FFT). The results were as follows: 1) lumbar muscles measured, the frequency change ratio of both median power frequency and mean power frequency was significantly greater for experimental group compared with control group group (p<0.05). In measured two abdominal muscles (inferior rectus abdominis, obliquus externus abdominis) except superior rectus abdominis, the frequency change ratio of both median power frequency and mean power frequency was significantly greater for experimental group compared with control group (p<0.05). 2) In all three (longissimus thoracis, iliocostalis lumborum, multifidus) lumbar muscles measured, the initial frequency value of both median power frequency and mean power frequency was significantly lower for the experimental group compared with the control group (p<0.05). In the two (inferior rectus abdominis, obliquus externus abdominis) abdominal muscles measured (superior rectus abdominis not included), the initial frequency value of both median power frequency and mean power frequency was significantly lower for the experimental group compared with the control group (p<0.05). These results suggest that in patients with chronic low back pain there is a trend for more fatigue to occur in both lumbar and abdominal muscles than in the normal control group. This would seem to suggest that in treatment programs for patients with chronic low back pain, improvement of endurance in all trunk muscles should be considered.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.253-256
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• 2003

In ecological studies, animal science, or entomology, the variance of count is considered to have the power of the mean relationship with the mean count as Taylor (1961) presented his famous 'Taylor's Power Law'. In this talk, we are going to review the development of TPL and its extension toward pest management sampling scheme. Different estimation methods are compared. Quasilikelihood approach is suggested to incorporate covariate information. Possible extensions will be discussed.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.2
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• pp.154-157
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• 2010

In this paper, the Time-varying Failure Rates(TFR) of power distribution system components are extracted from the recorded failure data of KEPCO(Korea Electric Power Corporation) and the reliability of power distribution system is evaluated using Mean Failure Rate(MFR) and TFR. The TFR is approximated to bathtub curve using the exponential and Weibull distribution function. In addition, Kaplan-Meier estimation is applied to TFR extraction because of incomplete failure data of KEPCO. Also the reliability of the real power distribution system of Korea is evaluated using the MFR and TFR extracted from real failure data, respectively and the results of each case are compared with each other. As a result, it is proved that the reliability evaluation using the TFR is more realistic than MFR. In addition, it is presented that the application method at power distribution system maintenance and repair using the result of TFR.

• Journal of Power Electronics
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• v.15 no.6
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• pp.1619-1627
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• 2015

This paper presents a power system harmonic elimination using the mixed adaptive linear neural network and variable step-size leaky least mean square (ADALINE-VSSLLMS) control algorithm based active power filter (APF). The weight vector of ADALINE along with the variable step-size parameter and leakage coefficient of the VSSLLMS algorithm are automatically adjusted to eliminate harmonics from the distorted load current. For all iteration, the VSSLLMS algorithm selects a new rate of convergence for searching and runs the computations. The adopted shunt-hybrid APF (SHAPF) consists of an APF and a series of 7^th tuned passive filter connected to each phase. The performance of the proposed ADALINE-VSSLLMS control algorithm employed for SHAPF is analyzed through a simulation in a MATLAB/Simulink environment. Experimental results of a real-time prototype validate the efficacy of the proposed control algorithm.