• 한국항행학회논문지
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• 제11권3호
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• pp.252-258
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• 2007

무선모바일 통신망에서는 사용자의 이동성보장 기술과 사용자가 요구하는 서비스품질(QoS)을 만족시키기 위한 효율적인 무선자원관리기술이 많이 연구되어 왔다. 본 연구에서는 시계열 예측기법(Time series prediction) 인 ARMA(Auto Regressive Moving Average) 모델을 이용하여 사용자가 요구하는 자원의 양을 예측하여 동적으로 자원을 할당함으로써 사용자의 이동성에 따른 QoS를 보장할 수 있는 자원할당방법을 제안한다. 제안한 방법은 ARMA 예측모델을 사용하여 이전에 핸드오프연결이 사용한 채널 수를 기초로 앞으로 필요로 하는 채널 수를 예측하여 예약함으로써 원하는 핸드오프 손실률에서 서비스가 이루어지도록 한다. 시뮬레이션을 통하여 기존의 RCS(Reserved channel scheme) 방법과 비교하여 핸드오프 연결의 손실률과 자원의 이용률에서 우수함을 보인다.

• 전력전자학회논문지
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• 제27권4호
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• pp.316-324
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• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.

• International Journal of Aeronautical and Space Sciences
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• 제3권1호
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• pp.86-94
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• 2002

The initial alignment of inertial platform for navigation system was considered. An adaptive filtering technique is developed for the system with unknown and varying sway disturbance. It is assumed that the random sway motion is the second order ARMA(Auto Regressive Moving Average) model and performed parameter identification for unknown parameters. Designed adaptive filter contain both a Kalman filter and a self-tuning filter. This filtering system can automatically adapt to varying environmental conditions. To verify the robustness of the filtering system, the computer simulation was performed with unknown and varying sway disturbance.

• 한국구조물진단유지관리공학회 논문집
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• 제19권4호
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• pp.43-48
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• 2015

본 연구에서는 지진계 센서의 동적범위를 향상시키는 새로운 방법을 제안하였다. 먼저, 센서에 포함된 저주파수 대역 잡음을 ARMA(Auto Regresive Moving Average) 모델로 모델링하고 시스템 식별 방법으로 그 모델을 식별한다. 다음으로, 모델링된 잡음과 지진파 입력을 칼만필터 식에 포함하여 칼만필터에 의한 지진파입력을 추정한다. 제안한 방법을 새로이 개발된 MEMS 기반 3축 가속도 형태의 지진계에 적용하여 성능을 검증하였다. 시험 결과는 제안한 방법이 단순한 LPF(Low Pass Filter)를 사용한 경우에 비해 동적범위를 개선시킴을 보여준다.

• Advances in Energy Research
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• 제6권2호
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.45-48
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• 1999

Model predictive control algorithm requires a relevant model of the system to be controlled. Unfortunately, the first principle model describing a polymerization reaction system has a large number of parameters to be estimated. Thus there is a need for the identification and control of a polymerization reactor system by using available input-output data. In this work, the polynomial auto-regressive moving average (ARMA) models are employed as the input-output model and combined into the nonlinear model predictive control algorithm based on the successive linearization method. Simulations are conducted to identify the continuous styrene polymerization reactor system. The input variables are the jacket inlet temperature and the feed flow rate whereas the output variables are the monomer conversion and the weight-average molecular weight. The polynomial ARMA models obtained by the system identification are used to control the monomer conversion and the weight-average molecular weight in a continuous styrene polymerization reactor It is demonstrated that the nonlinear model predictive controller based on the polynomial ARMA model tracks the step changes in the setpoint satisfactorily. In conclusion, the polynomial ARMA model is proven effective in controlling the continuous styrene polymerization reactor.

• Journal of Electrical Engineering and information Science
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• 제2권3호
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• pp.61-65
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• 1997

This paper proposes an intelligent controller for brushless DC motor and load with unknown nonlinear dynamics. The proposed intelligent control system consists of a plant identifier and PID controller with varying gains. The identifier is constructed using an Auto Regressive Moving Average (ARMA) model. In order to tune the parameters of the identifier and the gains of the PID controller efficiently, e also propose a modified Evolution Strategy. Experimental results show that the proposed intelligent controller for brushless DC motor has good control performance under unknown disturbance.

• 한국정보통신학회논문지
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• 제25권12호
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• pp.1756-1761
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• 2021

2019년 12월경 후베이 우한시에서 발생한 코로나19 바이러스가 점차 줄어드는 듯 보였으나, 2020년 11월, 2021년 6월 기준으로 점차 늘어나고 있으며, 전세계적으로 총 1억 9천 2백만명, 대한민국 기준 총 확진자는 대략 18만4천명으로 추정된다. 이에 따른 대책으로 중앙재난안전대책본부는 사회적 거리두기 4단계를 시행하면서 강력한 대응책을 내고있지만, 델타바이러스등 전염성이 강한 코로나 변이 바이러스가 기승을 부리면서 국내 일일 확진자 수는 1800명대 까지 증가하게 되었다. 그에따라 코로나바이러스의 심각성을 강조하고자 코로나 누적 확진자 수를 ARIMA 알고리즘을 이용해 예측한다. 그 과정에서 추세와 계절성을 제거하기 위해서 차분을 이용하고, MA, AR, 자기상관함수와 편자기상관함수를 이용해 ARIMA에서 p,d,q값을 결정하고 예측한다. 마지막으로 예측값과 실제값을 비교해 얼마나 잘 예측되었는지 평가한다.

• Journal of applied mathematics & informatics
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• 제5권2호
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• pp.373-382
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• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.

• 한국생산제조학회지
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• 제9권5호
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• pp.144-149
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• 2000

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.