• 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).

• Journal of Power System Engineering
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• v.4 no.4
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• pp.25-31
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• 2000

This paper represents the vector fields and three dimensional mean velocities in the X-Y plane of cone type swirl gas burner measured by using X-probe from the hot-wire anemometer system. This experiment is carried out at flowrate 350 and $450{\ell}/min$ respectively in the test section of subsonic wind tunnel. The vector plot shows that the maximum axial mean velocity component is focused in the narrow slits distributed radially on the edge of a cone type swirl burner, for that reason, there is some entrainment of ambient air in the outer region of the burner and the rotational flow can be shown in the inner region of the burner because mean velocity W is distributed about twice as large as mean velocity V due to inclined flow velocity ejecting from the swirl vanes of a cone type baffle plate of burner. Moreover, the mean velocities are largely distributed near the outer region of burner within $X/R{\fallingdotseq}1.5$, hence, the turbulent characteristics are anticipated to be distributed largely in the center of this region due to the large inclination of mean velocity and swirl effect.

• Bulletin of the Korean Mathematical Society
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• v.59 no.4
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• pp.979-991
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• 2022

A special class of exponential dispersion models is the class of Tweedie distributions. This class is very significant in statistical modeling as it includes a number of familiar distributions such as Gaussian, Gamma and compound Poisson. A Tweedie distribution has a power parameter p, a mean m and a dispersion parameter 𝜙. The value of the power parameter lies in identifying the corresponding distribution of the Tweedie family. The basic objective of this research work resides in investigating the existence of the implicit estimator of the power parameter of the Tweedie distribution. A necessary and sufficient condition on the mean parameter m, suggesting that the implicit estimator of the power parameter p exists, was established and we provided some asymptotic properties of this estimator.

• Journal of Navigation and Port Research
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• v.36 no.9
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• pp.729-735
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• 2012

The primary wave energy conversion by a three-dimensional bottom-mounted oscillating water column (OWC) wave power device in regular waves has been studied. The linear potential boundary value problem has been solved following the boundary matching method. The optimum shape parameters such as the chamber length and the depth of the front skirt of the OWC chamber obtained through two-dimensional numerical tests in the frequency domain have been applied in the design of the present OWC chamber. Time-mean wave power converted by the OWC device and the time-mean second-order wave forces on the OWC chamber structure have been presented for different wave incidence angles in the frequency-domain. It has been shown that the peak period of $P_m$ for the optimum damping parameter coincides with the peak period of the time.mean wave drift force when ${\gamma}=0$.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• Journal of Korean Physical Therapy Science
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• v.10 no.1
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• pp.83-89
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• 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 Biomedical Engineering Research
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• v.9 no.1
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• Journal of Environmental Impact Assessment
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• v.29 no.4
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• pp.272-285
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• 2020

We investigated statistical evaluation parameters for 3D meteorological and air quality models and selected several quantitative indicator references, and summarized the reference values of the statistical parameters for domestic air quality modeling researcher. The finally selected 9 statistical parameters are MB (Mean Bias), ME (Mean Error), MNB (Mean Normalized Bias Error), MNE (Mean Absolute Gross Error), RMSE (Root Mean Square Error), IOA (Index of Agreement), R (Correlation Coefficient), FE (Fractional Error), FB (Fractional Bias), and the associated reference values are summarized. The results showed that MB and ME have been widely used in evaluating the meteorological model output, and NMB and NME are most frequently used for air quality model results. In addition, discussed are the presentation diagrams such as Soccer Plot, Taylor diagram, and Q-Q (Quantile-Quantile) diagram. The current results from our study is expected to be effectively used as the statistical evaluation parameters suitable for situation in Korea considering various characteristics such as including the mountainous surface areas.

• Korean Journal of Acupuncture
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• v.30 no.4
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• pp.305-318
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• 2013

Objectives : The aim of this study was to examine the effect of electroacupuncture(EA) at an acupoint, HT8(Sobu), on normal humans by using power spectral analysis. We examined the effect on the Heart Rate Variability(HRV), and the balance of the autonomic nervous system. Methods : Thirty-two healthy volunteers participated in this study. EEG(Electroencephalogram) power spectrum exhibits site-specific and state-related differences in specific frequency bands. A thirty-two channel EEG study was carried out on thirty-two subjects(14 males; mean age=23.5 years old, 18 females; mean age=21.5 years old). HRV and EEG were simultaneously recorded before and after acupuncture. Results : In the ${\alpha}$(alpha) band, during the HT8-acupoint treatment, the power values in the ${\alpha}$(alpha) band significantly decreased(p<0.05) at 28 channels. In the ${\beta}$(beta) band significantly decreased(p<0.05) at 26 channels. In ${\delta}$(delta) band significantly decreased(p<0.05) at 18 channels. In ${\theta}$(theta) band significantly decreased(p<0.05) at 20 channels. ${\alpha}/{\beta}$ values were increased at 6 channels and decreased at 10 channels.${\beta}/{\theta}$ values were increased at 10 channels and decreased at 19 channels. Mean-RR(RR-interval), Complexity, RMSSD(Root mean square of successive differences), SDSD(Standard deviations differences between adjacent normal R-R intervals), norm HF showed a significantly increased and mean-HRV, norm LF, LHR(LF/HF Ratio) showed a significantly decreased after HT8-acupoint treatment(p<0.05). Conclusions : These results suggest that EA at the HT8 mostly causes significant changes on alpha(28 channels), beta(26 channels), delta(18 channels), theta(20 channels) bands and mean-HRV, mean-RR, complexity, RMSSD, SDSD, norm LF, norm HF and LHR. If practicing EA at the HT8, it will regulate the function of the cerebral cortex, decrease activity of the sympathetic and increase parasympathetic nervous activity.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1338-1348
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• 2017

In this study, a modified ripple correlation control (RCC) maximum-power point-tracking (MPPT) algorithm is proposed for a single-stage single-phase voltage source inverter (VSI) on a grid-connected photovoltaic system (GCPVS). Unlike classic RCC methods, the proposed algorithm does not require high-pass and low-pass filters or the increment of the AC component filter function in the voltage control loop. A simple arithmetic mean function is used to calculate the average value of the photovoltaic (PV) voltage, PV power, and PV voltage ripples for the MPPT of the RCC method. Furthermore, a high-accuracy and high-precision MPPT is achieved. The performance of the proposed algorithm for the single-stage single-phase VSI GCPVS is investigated through simulation and experimental results.