• The Pure and Applied Mathematics
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• v.19 no.3
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• pp.305-313
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• 2012

In this note we study Nesbitt's Inequality and modify it to make several inequalities. We prove some inequalities by using power series and Cauchy-Schwarz Inequality.

• Journal of Biomedical Engineering Research
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• v.33 no.3
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• pp.135-140
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• 2012

Previous investigators have shown that the frequency compression is related to the muscle fatigue and the decreasing conduction velocity of muscle fibers. The aim of the present study was to investigate the relationship between endurance times and frequency parameters such as mean power frequency and median frequency in the surface EMG signal during isotonic contractions. Eight healthy subjects performed voluntary isotonic contractions of biceps Brachii muscle until their endurance times which were determined when the subject could no longer follow the contraction cycle. The regressive slopes of mean power frequency and median frequency were used to describe the frequency compression of the surface EMG signal, and to test the predictability of endurance time. As results of experiment, significant correlations were found between endurance time and the regressive slopes of mean power frequency and mean frequency computed over 50%Tend of endurance time.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.993-1002
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• 2008

Correlated responses have been widely analyzed since Liang and Zeger (1986) introduced the famous Generalized Estimating Equations(GEE). However, their variance functions were restricted to known quantifies multiplied by scale parameter. In so many industries and academic/research fields, power-of-the-mean variance function is one of the common variance function. We suggest GEE-type pseudolikelihood estimation based on the power-of-the-mean variance using existing software and investigate it's efficiency for different working correlation matrices.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.50.2-50.2
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• 2019

Understanding three-dimensional structure and parameters (e.g., radial velocity, angular width, source location and density) of coronal mass ejections (CMEs) is essential for space weather forecast. In this study, we determine CME mean density in solar corona and near the Earth. We select 38 halo CMEs, which have the corresponding interplanetary CMEs (ICMEs), by SOHO/LASCO from 2000 to 2014. To estimate a CME volume, we assume that a CME structure is a full ice-cream cone which is a symmetrical circular cone combined with a hemisphere. We derive CME mean density as a function of radial height, which are approximately fitted to power-law functions. The average of power-law indexes is about 2.1 in the LASCO C3 field of view. We also obtain power-law functions for both CME mean density at 21 solar radii and ICME mean density at 1AU, with the average power-law index of 2.6. We estimate a ratio of CME density to background density based on the Leblanc et al.(1998) at 21 solar radii. Interestingly, the average of the ratios is 4.0, which is the same as a default value used in the WSA-ENLIL model.

• Journal of Veterinary Clinics
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• v.23 no.3
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• pp.258-262
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• 2006

This study was performed to evaluate the usefulness of gray-scale and power Doppler ultrasonography, and to compare with radiography for detection of the repairing in experimentally induced bone defects in dogs. In 4 adult beagle dogs bilateral bone defects were created in 8 canine femurs as sized as 5 mm diameter. Mean detection time of the ultrasonographic endosteal callus formations(mean $14.25{\pm}2.31$ days) was significantly shorter than that of the radiographic opacity chanees(mean $23.50{\pm}2.27$ days) in the defected region. Mean time of the neovascularizd flow signal(mean $6.00{\pm}3.59$ days) from the power Doppler ultrasonographic examination was significantly shorter than that of gray-scale ultrasonographic findings. With these results, gray-scale ultransonography and power Doppler ultrasonography can be used for an early detection modality for bone healing.

• Journal of Ocean Engineering and Technology
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• v.12 no.1
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• pp.107-112
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• 1998

The purpose of this study is to analyze the amount of available wave power and its characteristics related to the development of apractical system for ocean wave energy conversion in Korean coastal waters. The analysis method of wave power was established through comparison between theory and numerical simulation of deep sea wave by Inverse Fourier Transform with random phase method. Based on the results of comparison, wave power was estimated by use of data set from observed offshore and coastal waves and hindasted deep sea waves around the Korean peninsula. Annual mean wave power is estimated as about 1.8 ~ 7.0 kW for every metre of wave frontage at East sea, 1.5~5.3 kW at South sea and 1.0 ~ 4.1 kW at West sea, respectively. Mean wave power along deep sea front of coastal waters of Korea amounts to about 4.7 GW. Regional distribution and seasonal variation of wave power were discussed to develop practical utilization system of wave power of not so high grade of available wave power.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.8
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• pp.77-83
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• 2012

In this paper, when interference caused by the primary user exists, the capacity performance of the mean value-based power allocation scheme is analyzed and evaluated under the outdated channel environment in spectrum sharing systems. When interference due to the primary transmitter affects the secondary receiver, we derive the upper bound of the ergodic capacity of the mean value-based power allocation scheme in a closed form. Furthermore, based on that, we investigate how interference due to the primary transmitter degrades the ergodic capacity of the secondary user. In simulation results, we show the performance degradation of the secondary user due to interference caused by the primary user. In addition, we show that the region where the mean value-based power allocation scheme outperforms the outdated channel information-based power allocation scheme is reduced as interference by the primary user increases.

• Nuclear Engineering and Technology
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• v.38 no.3
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• pp.277-284
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• 2006

In order to ensure the structural integrity of nuclear welded structures during design life, the fatigue life has to be evaluated by fatigue analysis procedures presented in technical codes such as ASME B&PV Code Section III. However, existing fatigue analysis procedures do not explicitly consider the presence of welded joints. A new fatigue analysis procedure based on a structural stress/fracture mechanics approach has been recently developed in order to reduce conservatism by erasing uncertainty in the analysis procedure. A recent review of fatigue crack growth data under various mean loading conditions using the structural stress/fracture mechanics approach, does not consider the mean loading effect, revealed some significant discrepancies in fatigue crack growth curves according to the mean loading conditions. In this paper, we propose the use of the stress intensity factor range ${\Delta}K$ characterized with loading ratio R effects in terms of the structural stress. We demonstrate the effectiveness in characterizing fatigue crack growth and S-N behavior using the well-known data. It was identified that the S-N data under high mean loading could be consolidated in a master S-N curve for welded joints.

• 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.