• 한국안전학회지
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• 제16권1호
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• pp.88-93
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• 2001

본 연구에서는 복합소재인 GFRP(Glass Fiber Reinforced Polyester)의 2차원 절삭공정에서 절삭 메커니즘과 소재의 신뢰도 및 안전성과 밀접한 관련이 있는 표면정도를 중심으로 한 공정의 특성화를 시도하고, 주파수 분석에 관하여도 논의한다. 구체적으로는, 공정중 발생하는 절삭력 신호를 AR(Autoregressive) 모델링하여 해석에 사용한다. 특히, 특징추출과정을 통해 AR계수로 이루어진 패턴벡터 중 다양한 절삭 메카니즘에 민감한 계수만 선택할 수 있다. 이들 계수와 절삭 메커니즘과의 실험적 관계를 설정함으로써 섬유경사각(Fiber orientation angle), 절삭 변수 그리고 공구형상이 절삭 메커니즘에 미치는 영향을 평가하였다.

• 한국지반공학회지:지반
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• 제9권3호
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• pp.67-76
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• 1993

우리나라는 많은 산막지역으로이루어져 있으며 우기에 많은산사태의 발생으로 인하여 인명과 재산의 손실을 입고 있다. 따라서, 산사태의 발생에 대한 예측 시스템과 위험도 분석 연구가 필요하며, 본 연구의 목적은 관측된 지하수위의 분석을 통하여 산사태 발생을 예측하는 가능성에 대한 것이다. 이를 위하여 AR 모델을 사용하여 모델계수를 일정하게 하는 경우와 변화시키는 경우로 나누어 분석하였다. AR모델계수를 일정하게 하는 경우에는 AR(1), AR(2), AR(3) 모델을 선택하여 각 각의 모델계수를 구하였고, AR모델계수를 변화시키는 경우에는 변형된 AR(1)과 전형적인 AR (2) 모델을 과정 모델로 이용하여 Kalman Filtering 기법에 의하여 모델계수를 구하였다. 그 결과, 모델계수를 변화시키는 실시간 예측 방법이나 AR모델계수가 일정한 경우 모두 산사면 에서의 지하수위를 잘 예측해주며, 지하수위 뿐만아니라 시간별 강우강도를 고려함으로써 더욱 정 확한 예측을 할 수 있을 것으로 사료된다.

• Smart Structures and Systems
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• 제17권6호
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• Journal of the Korean Statistical Society
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• 제25권4호
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• pp.529-544
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• 1996

This paper deals with the problem of estimating the autoregressive random coefficient of a first-order random coefficient autoregressive time series model applied to panel data of time series. The autoregressive random coefficients across individual units are assumed to be a random sample from a truncated normal distribution with the space (-1, 1) for stationarity. The estimates of random coefficients are obtained by an empirical Bayes procedure using the estimates of model parameters. Also, a Monte Carlo study is conducted to support the estimation procedure proposed in this paper. Finally, we apply our results to the economic panel data in Liu and Tiao(1980).

• The Journal of the Acoustical Society of Korea
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• 제15권2E호
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• pp.4-7
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• 1996

In this paper, we propose a formula to compute the exact autocorrelations of the autoregressive (AR) process. For an arbitrary value of N, we first review the Yule-Walker equation and some basic properties of the AR model. We then modify the Yule-Walker equation to construct a new system of N+1 linear equations that can be used to solve for the N+1 autocorrelation coefficients for lags 0, 1, …, N, provided that the AR parameters of order N and the power of the white noise of the AR process are given.

• International Journal of Safety
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• 제3권1호
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• pp.1-5
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• 2004

This study discusses frequency analysis based on autoregressive (AR) time series model, and the characterization of surface quality in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The experimental correlations between the fiber pull-out and AR model coefficients are then established.

• Communications for Statistical Applications and Methods
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• 제20권1호
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• pp.85-96
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• 2013

A new bootstrap method combined with the stationary bootstrap of Politis and Romano (1994) and the classical residual-based bootstrap is applied to stationary autoregressive (AR) time series models. A stationary bootstrap procedure is implemented for the ordinary least squares estimator (OLSE), along with classical bootstrap residuals for estimated errors, and its large sample validity is proved. A finite sample study numerically compares the proposed bootstrap estimator with the estimator based on the classical residual-based bootstrapping. The study shows that the proposed bootstrapping is more effective in estimating the AR coefficients than the residual-based bootstrapping.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권2호
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• pp.103-110
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• 2004

This paper proposes a new method on the EMG AR(autoregressive) modeling in pattern recognition for various head motions. The proper electrode placement in applying AR or cepstral coefficients for EMG signature discrimination is investigated. EMG signals are measured for different 10 motions with two electrode arrangements simultaneously. Electrode pairs are located separately on dominant muscles(S-type arrangement), because the bandwidth of signals obtained from S-type placement is wider than that from C-type(closely in the region between muscles). From the result of EMG pattern recognition test, the proposed mIAR(modified integrated mean autoregressive model) technique improves the recognitions rate around 17-21％ compared with other the AR and cepstral methods.

• Smart Structures and Systems
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• 제11권1호
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• pp.1-17
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• 2013

This paper intended to investigate the feasibility of bridge health monitoring using a linear system parameter of a time series model identified from traffic-induced vibrations of bridges through a laboratory moving vehicle experiment on scaled model bridges. This study considered the system parameter of the bridge-vehicle interactive system rather than modal ones because signals obtained under a moving vehicle are not the responses of the bridge itself but those of the interactive system. To overcome the shortcomings of modal parameter-based bridge diagnosis using a time series model, this study considered coefficients of Autoregressive model (AR coefficients) as an early indicator of anomaly of bridges. This study also investigated sensitivity of AR coefficients in detecting anomaly of bridges. Observations demonstrated effectiveness of using AR coefficients as an early indicator for anomaly of bridges.

• 응용통계연구
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• 제20권1호
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• pp.103-115
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• 2007

본 논문에서는 1차 자기회귀모형에서 자기회귀계수에 대한 여러 가지 추정량들의 분포함수에 대한 근사 방법에 대해 연구하였다. 자기회귀계수의 여러 추정량들을 이차형식의 관점에서 이해하고, Na와 Kim(2005)에 의한 안장점근사의 결과를 이용한 새로운 근사법을 제시하였다. 이 방법은 정규근사를 비롯한 기존의 근사법과는 달리 추정량에 대한 근사분포의 유도과정이 불필요하며, 소표본은 물론 통계적 추론의 주요 관심영역에서의 근사정도가 매우 뛰어난 장점을 가지고 있다. 모의실험을 통해 Edgeworth 근사를 비롯한 기존의 여러 근사법보다 효율이 뛰어남을 확인하였다.