• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.44-50
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• 2008

Focusing on the exponential smoothing method equivalent to (1, 1) order ARMA model equation, a new method of estimating smoothing constant using exponential smoothing method is proposed. This study goes beyond the usual method of arbitrarily selecting a smoothing constant. First, an estimation of the ARMA model parameter was made and then, the smoothing constants. The empirical example shows that the theoretical solution satisfies minimum variance of forecasting error. The new method was also applied to the stock market price of electrical machinery industry (6 major companies in Japan) and forecasting was accomplished. Comparing the results of the two methods, the new method appears to be better than the ARIMA model. The result of the new method is apparently good in 4 company data and is nearly the same in 2 company data. The example provided shows that the new method is much simpler to handle than ARIMA model. Therefore, the proposed method would be better in these general cases. The effectiveness of this method should be examined in various cases.

• Journal of Integrative Natural Science
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• v.14 no.3
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• pp.116-122
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• 2021

Properties and comparison of multivariate CUSUM and EWMA charts for monitoring Σ of multivariate normal N(${\underline{\mu}}$, Σ) process has considered. Comparison of the performances of the considered charts, the numerical values are obtained by simulation with 10,000 iteration in terms of ATS, ANSS and ANSW. We found that EWMA chart with small values of smoothing constant more effectively detects the process changes than with large smoothing constant. And we also found that CUSUM chart with small value of reference value is more effectively detecting the process change than with large reference value. If a process engineer has interest in detecting small amount of shift rather than large shift, he/she can be recommended to use small smoothing constant in EWMA chart and small reference value in CUSUM chart.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.257-263
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• 2009

Focusing on the idea that the equation of exponential smoothing method (ESM) is equivalent to (1, 1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Theoretical solution was derived in a simple way. Mere application of ESM does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month. A new method to cope with this issue is required. In this paper, combining the trend removal method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removal by a linear function is applied to the original shipping data of consumer goods. The combination of linear and non-linear function is also introduced in trend removal. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful especially for the time series that has stable characteristics and has rather strong seasonal trend and also the case that has non-linear trend. The effectiveness of this method should be examined in various cases.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.178.1-178.1
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• 2010

기후변화 대응을 위해 세계적으로 신 재생에너지의 분담율(penetration rate)은 갈수록 증가하고 있고, 정부에서는 2015년까지 신 재생에너지의 개발에 총 40조원을 투자한다는 적극적인 계획을 세우고 있다. 하지만 신 재생에너지 중 전력 생산에 가장 큰 비중을 차지하는 풍력발전은 비급전성과 간헐성 등의 제약으로 인해 안정적인 전력을 공급하기 힘들뿐만 아니라 전력계통의 신뢰성을 악화시킬 수도 있는 리스크를 잠재하고 있는 에너지원이다. 이에 풍력발전 등 신 재생에너지원의 출력을 안정화시키기 위한 Smart Renewable 프로젝트가 현재 제주도에서 실증 단계에 있다. 이 논문에서는 한국전력 컨소시엄의 Smart Renewable 프로젝트 대상인 660kW급 풍력터빈과 200kWh급 리튬-이온 배터리 에너지저장장치를 이용하여, 풍력터빈의 출력을 평활화시키는 평활화 제어(Smoothing Control)와 일정시간동안 균일한 출력을 낼 수 있게 하는 정출력 제어(Constant Power Control)의 두 가지 기법을 시뮬레이션 하였다. t 시점의 에너지저장장치 잔존용량을 피드백 받아 t+1 시점의 풍력터빈과 에너지저장장치 합성출력의 목표치를 설정하는 잔존용량 피드백 방법을 이용하여 에너지저장장치의 운전모드, 초기 용량, 평활화 시정수(time constant) 등의 조건 변화가 평활화 제어와 정출력 제어에 미치는 영향을 각각 확인하고, 주어진 기기 조건 하에서 최적의 시정수 값과 운전모드를 도출하였다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.313-320
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• 2000

PEM algorithm cannot expend repeated algorithm, if penalty function is transcendental function. However, OSL algorithm has an advantage that repeated algorithm is easily derived, even though penalty function which has a complicated transcendental function. In spite of this advantage, this algorithm is restricted in convergence region of smoothing constant which increase penalized log-likelihood, so we cannot get the optimal image restoration because it cannot provide us with a various smoothing constant value for the digital image restoration. In this paper, in order to resolve the disadvantage of OSL algorithm, we would like to suggest the algorithm with smoothing constant enlarge the tolerance limit range of convergence and to find not only properties of its convergence but also usefulness of suggested algorithm through digital image simulation.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.211-216
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• 2013

In general, smoothing filters regularize functions by reducing differences between adjacent values. The smoothing filters, therefore, can regularize inverse solutions and produce more accurate subsurface structure when we apply it to full waveform inversion. If we apply a smoothing filter with a constant coefficient to subsurface image or velocity model, it will make layer interfaces and fault structures vague because it does not consider any information of geologic structures and variations of velocity. In this study, we develop a selective smoothing regularization technique, which adapts smoothing coefficients according to inversion iteration, to solve the weakness of smoothing regularization with a constant coefficient. First, we determine appropriate frequencies and analyze the corresponding wavenumber coverage. Then, we define effective maximum wavenumber as 99 percentile of wavenumber spectrum in order to choose smoothing coefficients which can effectively limit the wavenumber coverage. By adapting the chosen smoothing coefficients according to the iteration, we can implement multi-scale full waveform inversion while inverting multi-frequency components simultaneously. Through the successful inversion example on a salt model with high-contrast velocity structures, we can note that our method effectively regularizes the inverse solution. We also verify that our scheme is applicable to field data through the numerical example to the synthetic data containing random noise.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.481-487
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• 2013

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.111-118
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• 2012

The burst is to require abruptly high transmission rate in case of transmitting pre-stored variable bit rate video data, and it causes to be inefficient use of network resource, resource reservation. To avoid these problems, smoothing is transmission plan where variable rate video data is converted to a constant bit rate stream. These smoothing algorithms include CBA, MCBA, MVBA and others. To evaluate amount of burst reduction in the existing CBA, MCBA, MVBA algorithm, this paper compares the burst-related-factors of transmission plan in smoothing algorithms with original video sources which were stored Variable Bit Rate. There are maximum frame bytes, maximum GOP bytes, transmission rate variability per frame, transmission rate variability per GOP in burst-related evaluation factors. Experimental result shows burst-related factors of smoothing algorithms which were used for experiment lower than that of pre-stored video data, except special case.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.165-168
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• 2006

Exponential smoothing methods do not adapt well to unexpected changes in underlying process. Over the past few decades a number of adaptive smoothing models have been proposed which allow for the continuous adjustment of the smoothing constant value in order to provide a much earlier detection of unexpected changes. However, most of previous studies presented ad hoc procedure of adaptive forecasting without any theoretical background. In this paper, we propose a detection-adaptation procedure applied to simple and Holt's linear method. We derive level and slope change detection statistics based on Bayesian statistical theory and present distribution of the statistics by simulation method. The proposed procedure is compared with previous adaptive forecasting models using simulated data and economic time series data.