• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.189-199
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• 1992

Standard acceptance sampling plans models the production process as a sequence of independent identically distributed Beruoulli random variables. However, the quality of items sampled sequentially from an ongoing production process often exhibits statistical dependency that is not accounted for in standard acceptance sampling plans. In this paper, a dependent production process is modelled as an ARMA process and as a two-state Markov chain. A simulation study of each is performed. A comparison of the probability of acceptance is done for the simulation method and for the approximation method.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.783-791
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• 2004

In this paper we review recent developments in nonlinear time series modeling on both conditional mean and conditional variance. Traditional linear model in conditional mean is referred to as ARMA(autoregressive moving average) process investigated by Box and Jenkins(1976). Nonlinear mean models such as threshold, exponential and random coefficient models are reviewed and their characteristics are explained. In terms of conditional variances, ARCH(autoregressive conditional heteroscedasticity) class is considered as typical linear models. As nonlinear variants of ARCH, diverse nonlinear models appearing in recent literature including threshold ARCH, beta-ARCH and Box-Cox ARCH models are remarked. Also, a class of unified nonlinear models are considered and parameter estimation for that class is briefly discussed.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2000.11a
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• pp.199-204
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• 2000

Generally, The systems are so complex that it not possible to obtain reasonable model using physical insight. Also a model based on physical insight contains a number of unknown parameters even if the structure is derived from physical laws. To solve these problems, the systems identification is described in this paper. So, AC servo motor system which has both open loop and closed loop is selected as an example for identification. A state-space model of AC servo motor system is identified through open loop experiment and identified through closed loop experiment and using pole placement integral controller to open loop system. As the results, From ARMA model, We have obtained continuous-time state space model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.2
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• pp.60-66
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• 2003

For the estimation of chatter lobe boundary it is very important to calculate the natural mode of cutting process. There are many time series algorithms for getting the natural mode of structural endmilling dynamics considering the cutting process. In this study, we have compared several time series methods such as AR algorithm, ARX, ARMAX, ARMA, Box Jenkins, Output Error, Recursive ARX, Recursive ARMAX considering the sampling frequency. As a results, the ARX, ARMAX and IV 4 are more desirable algorithms for the calculation of modal parameters such as natural frequency and damping ratio In endmilling operation. Also these algorithms may be adopted for the natural mode estimation of endmilling operation for chatter lobe prediction.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.40-45
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• 1999

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.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.394-398
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• 2011

수자원시스템의 설계, 계획, 운영에 있어 핵심적인 수문변수의 미래거동에 대한 보다 나은 추정치가 필요하다. 예를 들어, 수력발전, 레크리에이션 이용과 하류지역의 오염희석과 같은 다중 목적 기능을 유지하기 위하여 다목적댐을 운영할 때에, 다가오는 미래시간에 대한 계획된 유량의 예측이 요구된다. 예측의 목적은 미래에 발생할 정확한 예상치를 제공하는 것이다(Keith W. Hipel, 1994). 본 연구의 주요 목적은 금강수계인 대청댐에서 다변량 추계학적 시스템의 해석을 위한 모형의 추정과 등정을 위한 과정을 개발하는데 있다. 일반적 추계학적 시스템 모형이 표현되며 그것으로부터 수문학적 시스템의 모형을 매우 적절하게 유도하기 위한 다중 입력-단일 출력 TF, TFN, ARMAX모형을 유도하는데 있다. 이 모형은 수문학적 시스템을 위한 경우인 상관된 입력을 설명할 수 있도록 개발된다. 일반적인 모형을 만드는 전략이 사용되며 실제유역시스템에 적용하여 검토해 보고자 한다.

• Journal of Communications and Networks
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• v.14 no.3
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.91-106
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• 1992

It an inventory control system, the demand over time are often assumed to be independently identically distributed (i. i. d.). However, the demands may well be correlated over time in many situations. The estimation of reorder points is not simple for correlated demands with variable lead time. In this paper, a general class of autoregressive and moving average processes is considered for modeling the demands of an inventory item. The first four moments of the lead-time demand (L) are derived and used to approximate the distribution of L. The reorder points at given service level are then estimated by the three approximation methods : normal approximation, Charlier series and Pearson system. Numerical investigation shows that the Pearson system and the Charlier series performs extremely well for various situations whereas the normal approximation show consistent underestimation and sensitive to the distribution of lead time. The same conclusion can be reached when the parameters are estimated from the sample based on the simulation study.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.5
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• pp.7-12
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• 2005

This paper presents several time series methods to analyze the chatter mechanics by using the power spectrum of these algorithms considering the cutting dynamics. In this study, several time series models such as AR(burg, forwardbackward, geometric lattice, instrument variable, least square, Yule Walker), ARX(1s, iv4), ARMAX, ARMA, Box Jenkins, Output Error were modeled and compared with one another. Finally, it was proven that time series modelings are also a desirable and reliable algorithm than the other conventional methods(FFT) for the calculation of the chatter mode in turning operation. Also, the spectrum of times series methods is a little bit more powerful than the FFT fer the detection of a high noisy and weak chatter mode. The radial cutting force Fy has been used for spectrum and chatter stability lobe analysis in this study.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.171-180
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• 1997

Effects of order misspecification on statistical behavior of unit root tests are studied. We derive the limiting distributions of the Dickey-Fuller test statistics whose numerators are of the form c .int. W dW + .kappa. where W is a standard Brownian motion on [0, 1] and c is a real number. The term .kappa. is a major consequence of order misspecification and its explict expression is derived. Based on an analysis of .kappa., effects of order misspecification on unit root tests for AR(2), ARMA(1, 1), and AR(3) models are investigated.