• 대한산업공학회지
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• 제39권2호
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• pp.109-118
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• 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.

• Journal of Mechanical Science and Technology
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• 제20권12호
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• pp.2043-2051
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• 2006

This paper considered identification of vibration characteristics of flexible structure with vector channel periodic lattice filter. We present an algorithm for AR coefficients for the vector-channel lattice filters, and characteristic equation and transfer function are derived from these coefficients. Vibration lattice filter is then constructed from the vector channel lattice filter, and performance of this vibration filter is tested with a test signal which is a combination of many sine waves to compare the performance of scalar and vector channel lattice. Also it is applied to the cantilever data to identify properties of the system, such as natural frequencies and damping ratios, to show its performance.

• 한국소음진동공학회논문집
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• 제13권1호
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• pp.48-55
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• 2003

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

• 한국음향학회지
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• 제26권3호
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• pp.129-135
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• 2007

본 논문에서는 LFM 잔향 신호를 효율적으로 백색화하여 표적의 탐지확률을 높이는 기법을 제안한다. 제안한 기법에서는 LFM의 주파수 변화율을 역으로 보상하는 선형 dechirping 기법을 이용하여 시간에 따라 주파수가 변하는 LFM 신호의 잔향을 CW와 같이 데이터 블록 내에서 일정한 주파수 특성을 유지할 수 있도록 변환하였다. 또한 표적이 존재하지 않는 인접 빔 신호를 참조 신호로 사용하여 AR (autoregressive)계수로 각 구간의 주파수 응답을 모델링하고 역 필터를 구현하여 표적이 존재하는 빔 신호를 필터링함으로써 LFM 잔향을 효율적으로 백색화하였다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.184-187
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• 1997

In terms of a system identification, we present a method for autoregressive(AR) modelling of variious biomedical signal. Model order is estimated fly low rank approximation and coefficients are determined by innovation processes of Kalman filter derivation. An application of the method is given for visual evoked potentials.

• Smart Structures and Systems
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• 제5권1호
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• 한국음향학회지
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• 제15권4호
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• pp.50-57
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• 1996

본 논문에서는 최적의 웨이브렛 선택방법과 이 선택된 웨이브렛으로 F-검정을 이용하여 AR파라메터를 전개시키는 방법을 제안하였으며 웨이브렛 선택 방법으로서 평가함수를 도입하였다. 이 평가함수를 이용하여 웨이브렛들(D4-D20)을 합성신호에 대해서 시험하였다. 이때 선택된 웨이브렛을 이용하여 합성신호와 실제 음성신호에 대해서 AR파라메터들을 웨이브렛 전개 했을때의 웨이브렛 계수를 구하였다. 제안된 방법을 평가하기 위해서 칼만필터 알고리즘과 비교하였다. 그 결과 제안된 알고리즘이 칼만필터보다 약5-10dB정도 더 우수한 성능을 나타내었다.

• 전자공학회논문지
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• 제53권2호
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• pp.47-52
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• 2016

스마트 기기와 소형 디스플레이에 사용되는 디지털 영상은 다운스케일링 (Downscaling)된 영상이 사용된다. 본 논문에서는 영상 픽셀값의 경사도에 따른 특징벡터를 이용한 다운스케일링 포저리 (Forgery) 영상 검출 알고리즘을 제안한다. 제안된 알고리즘에서, 원영상의 픽셀값 경사도로부터 자기회귀 (AR: Autoregressive) 계수를 계산한다. 이는 다운스케일링 포저리 영상 검출기의 SVM (Support Vector Machine) 분류를 위한 학습에 사용된다. 제안된 다운스케일링 검출 알고리즘은 동일 10-Dim. 특징벡터의 MFR (Median Filter Residual) 스킴과 686-Dim.의 SPAM (Subtractive Pixel Adjacency Matrix) 스킴과 비교하여 다운스케일링 90% 영상 포저리에서 성능이 우수하며, 평균필터링 ($3{\times}3$) 영상과 미디언필터링 ($3{\times}3$) 영상에서 높은 검출율을 보여 주었다. 특히, 평균필터링과 미디언필터링 영상에서는 성능평가 전체 항목에서 민감도 (Sensitivity; TP: True Positive rate)와 1-특이도 (1-Specificity; FP: False Positive rate)의 AUC (Area Under Curve)가 모두 1에 수렴하여 'Excellent (A)' 등급임을 확인하였다.

• 한국산업안전학회:학술대회논문집
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• 한국안전학회 2000년도 추계 학술논문발표회 논문집
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• pp.39-45
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• 2000

In recent years, composite materials such as fiber reinforced plastics (FRP) have gained considerable attention in the aircraft and automobile industries due to their light weight, high modulus and specific strength. In practice, control of chip formation appears to be the most serious problem since chip formation mechanism in composite machining has significant effects on the finished surface ［1,2,3,4,5］. Current study will discuss frequency analysis based on autoregressive (AR) time series model and process characterization in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized model 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 correlation between the different chip formation mechanisms and model coefficients are established.(omitted)

• Smart Structures and Systems
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• 제24권3호
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• pp.319-332
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• 2019

Structural health monitoring (SHM) is of great importance to super high-rise buildings. The Shanghai Tower is currently the tallest building in China, and a complete SHM system was simultaneously constructed at the beginning of the construction of the tower. Due to the variety of sensor types and the large number of measurement points in the SHM system, an online automatic structural health assessment method with few computations and no manual intervention is needed. This paper introduces a structural health assessment method for the Shanghai Tower that uses the coefficients of an autoregressive (AR) time series model as structural state indicators. An analysis of collected data indicates that the coefficients of the AR model are affected by environmental factors, and the principal component analysis method is used to remove the influence of environmental factors. Finally, the control chart method is used to track the changes in structural state indicators, and a plan for online automatic structure health state evaluation is proposed. This method is applied to long-term acceleration and inclination data from the Shanghai Tower and successfully identifies the changes in the structural state. Overall, the structural state indicators of the Shanghai Tower are stable, and the structure is in a healthy state.