• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제10권2호
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• pp.41-47
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• 2006

The Renewal process and the Non-homogeneous Poisson process (NHPP) process are probably the most popular models for describing the failure pattern of repairable systems. But both these models are based on too restrictive assumptions on the effect of the repair action. For these reasons, several authors have recently proposed point process models which incorporate both renewal type behavior and time trend. One of these models is the Modulated Power Law Process (MPLP). The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose Bayes estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model. Numerical examples illustrate the estimation procedure.

• 대한의용생체공학회:의공학회지
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• 제33권3호
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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.

• 산업경영시스템학회지
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• 제42권1호
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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.

• 한국안전학회지
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• 제14권2호
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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.

• 한국임상수의학회지
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• 제23권3호
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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.

• 한국항해항만학회지
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• 제36권9호
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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 ${\gamma}=0$.

• 전기학회논문지P
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• 제59권2호
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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 the Korean Data and Information Science Society
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• 제18권2호
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• pp.535-541
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• 2007

The Non-homogeneous Poisson process is probably the most popular model since it can model systems that are deteriorating or improving. The renewal process is a model that is often used to describe the random occurrence of events in time. But both these models are based on too restrictive assumptions on the effect of the repair action. The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose maximum likelihood estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model.

• 산업경영시스템학회지
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• 제39권1호
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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.

• 응용통계연구
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• 제26권2호
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• pp.349-360
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• 2013

최근 일별 최대 전력수요 예측은 전력설비 계획 및 운용에 매우 중요한 사안으로 주목받고 있다. 본 연구는 일별 최대 전력수요 예측을 위하여 대표적 시계열 모형을 소개하고, 예측의 성능 비교를 위하여 RMSE(Root mean squared error)와 MAPE(Mean absolute percentage error)를 사용한다. 연구결과로 보완된 Holt-Winters 모형과 Reg-ARIMA 모형이 다른 모형에 비하여 우수한 예측 성능을 보였다.