• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.3
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• pp.42-51
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• 1999

An experimental modeling of cutting and structural dynamics and the on-line detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics of cutting process but also for the analytic realization of diagnostic systems. In this regard, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision round shape machining such as turning, drilling and boring in mold and die making. In this study, simulation and experimental work were performed to show the malfunctioned behaviors. For this purpose, two new recursive approach (REIVM, RLSM) were adopted fur the on-line system identification and monitoring of a machining process, we can apply these new algorithm in real process for the detection of abnormal machining behaviors such as chipping, chatter, wear and round shape lobe waviness.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.587-594
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• 2009

In this paper the change point problem of the error terms in linear regression models is considered. Since fixed or stochastic independent variables and weakly dependent errors are assumed, usual multiple regression models and time series models including ARMA are covered. We use the estimates of probability density function based on residuals in order to test the distribution change of the unobserved errors. Under some mild conditions, the test using the residuals is proved to have the same limiting distribution as the test based on true errors.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.183-193
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• 1994

The dynamic equation of a flexible robot manipulator is formulated by the assumed-mode method and the Lagrange equation. The controller is designed for a flexible robot manipulator including a joint actuator. The controller consists of a parmaeter estimator and the adaptive controller. A parameter estimator evaluates ARMA model`s parameter using RLS algorithm. An adaptive controller is designed based on a reference model and a minimum prediction error controller.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.147-153
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• 1990

This paper treats modeling of dynamic load characteristics for power for systems. The dynamic load is represented as 4th order multivariable ARMA model under the assumption that the dynamic load characteristics can be described by the dynamics of only one induction motor. The parameters of the proposed ARMA model are identified using the well-known RLS method. This paper presents two kind of identification results : one is for induction motors and the other is for field data at Donghae station. From these results, the proposed model is quite suitable for the dynamic load characteristics. It has, however, a disadvantage in the viewpoint that the identified parameters are not those of the induction motor.

• Journal of the Korean Society for Precision Engineering
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• v.1 no.1
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• pp.34-49
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• 1984

The stability of machine tool systems is analyzed by considering the machining process as a stochastic process without decomposing into machine tool structural dynamics and cutting processes. In doing so the time series analysis technique developed by Wu and Pandit is applied systematically to the relative vibration between cutting tool and work- piece measured under actual working conditions. Various characteristic properties derived from the fitted ARMA(Autoregressive Moving Average) Models and those from raw data directly are investigated in relation with the system stability. Both damping ratio and absolute value of the characteristic roots of the AR part of the most significant dynamic mode are preferred as stability indicating factors to the other pro-perties such as theoretical variance .gamma. (o) or absolute power of the most dominant dynamic mode. Maximum aplitude during a certain interval and variance estimated from raw data are shown to be very sensi- tive to the type of the signal and the location of measurement point although they can be obtained rather easily. The relative vibration signal is also analyzed by FFT(Fast Fourier Transform) Analyzer for the purpose of comparison with the spectrums derived from the fitted ARMA models.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.135-141
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• 2003

For the analysis of machined surface topography and machine-tool chatter, the cutting system is considered to be a single degree of freedom system. This paper presents a modeling method of equivalent vibratory system for precision cutting in end-milling using an impact test, an Autoregressive Moving Average (ARMA) mode] and a bisection method It has been shown that the proposed modeling method provides a good identification of the cutting system. The advantages of the proposed method in comparison to the existing method are that it is very easy and accurate.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.267-277
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• 2016

This paper considers we consider the estimation of copula parameters based on residuals in stochastic regression models. We prove that a semiparametric estimator using residual empirical distributions is consistent under some conditions and apply the results to the copula-ARMA model. We provide simulation results for illustration.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.2
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• pp.79-87
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• 1994

A real-time flood forecasting system(FLOFS) was developed for the real-time and predictive determination of flood discharges and stages, and to aid in flood management decisions in the Keum River Estuary Dam. The system consists of three subsystems : data subsystem, model subsystem, and user subsystem. The data subsystem controls and manages data transmitted from telemetering systems and simulated by models. The model subsystem combines various techniques for rainfall-runoff modeling, tidal-level forecasting modeling, one-dimensional unsteady flood routing, Kalman filtering, and autoregressivemovingaverage(ARMA) modeling. The user subsystem in a menu-driven and man-machine interface system.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2983-2985
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• 2000

이 논문의 목적은 시계열 예측 엔진의 개발과 그 엔진을 Application S/W로 구현하는 것이다 시계열 예측 엔진은 과거의 데이터를 분석하여 예측을 위한 식의 차수와 형태를 결정하고 이를 바탕으로 파라미터를 결정한 후 미래의 간을 예측하는 3가지 단계를 거친다. 석기에 쓰이는 기법들은 여러 가지가 있는데 본 논문에서는 ARMA(Auto Regressive Moving Average)를 기본으로 분석하였다 Application S/W는. 개발된 예측 엔진에서 분석될 과거 데이터를 입력받아 예측 엔진 구동에 사용되고 그 결과를 그래프로 나타내는 일련의 과정을 거친다. Application S/W 개발의 많은 Programming Language가 존재하지만 본 논문에서는 Visual C누 +을 사용하였다. 또한 이 논문에선, 특정 교차로를 통과하는 교통량 변화에 대한 데이터를 이용하여 예측을 수행하고. 그 결과를 Application S/W에 적용시켰다.