• Journal of Welding and Joining
• /
• 제17권6호
• /
• pp.100-107
• /
• 1999

In this paper, we deal with the speckle noise reduction and parameter estimation of ultrasonic NDT(non-destructive test) signals obtained during weld inspection of piping. The overall approach consists of three major steps, namely, speckle noise analysis, proposition of wavelet domain AR(autoregressive) model and flaw detection by proposed model parameter. The data are first processed whereby signals obtained using vertical and angle beam transducer. Correlation properties of speckle noise are then analyzed using multiresolution analysis in wavelet domain. The parameter estimation curve obtained using the proposed model is classified a flaw in weld region where is contaminated by severe speckle noise and also clear flaw signal is obtained through CA-CFAR threshold estimator that is a nonlinear post-processing method for removing the noise from reconstructed ultrasonic signal.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2002년도 춘계학술대회논문집
• /
• pp.109-114
• /
• 2002

The purpose of this study is to propose the general method for evaluating the equivalent damping ratios of a structure with supplemental response control dampers. We define Lyapunov function of which derivative can be expressed in autoregressive form and evaluate the equivalent damping ratios by using Lyapunov function and its derivative. This Lyapunov function may be called as generalized structural energy. In this study, it is assumed that the response of a structure is stationary random process and control dampers do not affect the modal shapes of a structure, and the structure has proportional damping. Proposed method can be used to get the equivalent damping ratios of a structure with non-linear control dampers such as friction dampers as well as linear control dampers. To show the effectiveness of the proposed method. we evaluate the equivalent damping ratios of a structure with viscous dampers. AMDs. and friction dampers. The equivalent damping ratios from proposed method are compared to those from eigenvalue analysis for linear control dampers. and those from time history analysis for non-linear control dampers. respectively.

• 전기학회논문지
• /
• 제63권2호
• /
• pp.291-296
• /
• 2014

Most of the statistical signal analysis processed in the time domain and the frequency domain are based on the assumption that the signal is weakly stationary(wide sense stationary). Therefore, it is necessary to know whether the surface EMG signals processed in the statistical basis satisfy the condition of weak stationarity. The purpose of this study is to analyze the accuracy of the Run-test, modified Run-test, RA(reverse arrangement)-test, and modified RA-test for assessing surface EMG signal stationarity. Six stationary and three non-stationary signals were simulated by using sine wave, AR(autoregressive) modeling, and real surface EMG. The simulated signals were tested for stationarity using nine different methods of Run-test and RA-test. The results showed that the modified Run-test method2 (mRT2) classified exactly the surface EMG signals by stationarity with 100% accuracy. This finding indicates that the mRT2 may be the best way for assessing stationarity in surface EMG signals.

• 대한산업공학회지
• /
• 제39권2호
• /
• pp.109-118
• /
• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• 한국윤활학회:학술대회논문집
• /
• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
• /
• pp.235-236
• /
• 2002

Tribological phenomena such as wear or transfer are influenced by various factors and have complicated behavior. Therefore, it is difficult to predict the behavior of the gribological phenomena because of their complexity. But, those tribological phenomena can be considered simply as to transfer micro material particles from the sliding interface. Then, we proposed the numerical simulation method for tribological phenomena such as wear of transfer using stochastic process models. This numerical simulation shows the change of the 3-D surface topography. In this numerical simulation, initial 3-D surface toughness data are generated by the method of non-causal 2-D AR (autoregressive) model. Processes of wear and transfer for some generated initial 3-D surface data are simulated. Simulation results show successfully the change of the 3-D surface topography.

• 한국군사과학기술학회지
• /
• 제21권4호
• /
• pp.529-540
• /
• 2018

As the equipment of the military has recently become more sophisticated and expensive, the cost of purchasing spare parts is also steadily increasing. Therefore, demand forecast accuracy is also becoming an issue for the effective execution of the spare parts budget. This study predicts the demand by using the data of spare parts consumption of the KF-16C fighter which is being operated in the Republic of Korea Air Force. In this paper, SARIMA(Seasonal Autoregressive Integrated Moving Average) is applied to seasonal data after dividing the spare parts consumptions into seasonal data and non-seasonal data. Proposing new methods, Majority Voting and Hybrid Method, to the non-seasonal data which consists of spare parts of low consumption with unclear pattern, We want to prove that the demand forecast accuracy of spare parts improves.

• 대한전자공학회논문지SP
• /
• 제46권5호
• /
• pp.126-134
• /
• 2009

본 논문은 움직이는 차 안에서 교통 표지판을 추적하는 영상 기반 시스템을 기술한다. 제안된 시스템은 복잡한 환경에서 강인한 추적의 성능을 위해 파티클 필터를 기반으로 하는 기본 구조를 가진다. 실제 환경에서 표지판을 실시간으로 추적하는 경우, 장애물에 의한 겹침 현상과 빠르게 변하는 도로 상황 때문에 시계열 데이터인 상태 정보를 예측하는 것은 많은 어려움이 있다. 따라서 본 논문에서는 이러한 단점을 해결하기 위하여 어파인 변환의 파라미터를 상태 정보로 사용한 자기 회귀 모델을 파티클 필터의 상태 전이 모델로써 사용하고, 강인 통계를 사용하여 장애물에 의한 겹침 현상을 판단하여 추적 성능을 향상시키는 알고리즘을 제안한다. 본 논문의 실험 결과에서는 본 논문에서 제안된 방법이 주행 중 실시간 추적을 위하여 효과적이며, 장애물에 의해 표지판이 겹치는 경우에도 추적이 잘 수행됨을 보인다.

• 응용통계연구
• /
• 제35권3호
• /
• pp.385-393
• /
• 2022

본 논문에서는 시계열 예측 분야에서 잘 알려져 있는 단변량 시계열 모형들을 이용하여, 그들의 단순 조합이 어떤 예측력을 보여주는지 연구한다. 고려된 단변량 시계열 모형으로는, 지수평활 및 ARIMA(autoregressive integrated moving average) 모형들과 그들의 확장된 형태인 모형들 그리고 예측의 벤치마크 모형으로 자주 사용되는 비계절 및 계절 랜덤워크 모형이다. 단순 조합의 방법은 중앙값과 평균을 이용하였으며, 검증을 위하여 사용된 데이터셋은 3,003개의 시계열 자료로 구성된 M3-competition 자료이다. 예측 성능을 sMAPE(symmetric mean absolute percentage error)와 MASE(mean absolute scaled error)로 평가한 결과, 단변량 시계열 모형들의 단순 조합이 아주 우수한 예측력을 가지고 있음을 확인하였다.

• Industrial Engineering and Management Systems
• /
• 제12권3호
• /
• pp.244-253
• /
• 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.

• 전기학회논문지
• /
• 제63권8호
• /
• pp.1128-1133
• /
• 2014

Most of the statistical signal analysis processed in the time domain and the frequency domain are based on the assumption that the signal is weakly stationary(wide sense stationary). Therefore, it is necessary to know whether the surface EMG signals processed in the statistical basis satisfy the condition of the weak stationarity. The purpose of this study is to find optimal segment length of surface EMG signal for assessing stationarity with the modified Run-test and RA-test. Ten stationary surface EMG signals were simulated by AR(autoregressive) modeling, and ten real surface EMG signals were recorded from biceps brachii muscle and then modified to have non-stationary structures. In condition of varying segment length from 20ms to 100ms, stationarity of the signals was tested by using six different methods of modified Run-test and RA-test. The results indicate that the optimal segment length for the surface EMG is 30ms~35ms, and the best way for assessing surface EMG signal stationarity is the modified Run-test (Run2) method using this optimal length.