• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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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 the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.54-59
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• 1999

In this paper, a generalized auto-regressive(AR) model is proposed for linear prediction based on adaptive spatio-temporal support region(ASTSR). The conventional AR model suffers from the drawback that the prediction error increases in the edge region because the rectangular support region of the edge does not satisfy the stationary assumption. Thus, the proposed approach puts an emphasis on the formulation of a spatio-temporally adaptive support region for the AR model, called ASTSR. The ASTSR consists of two parts: the adaptive spatial support region(ASSR) connected with edges and the adaptive temporal support region(ATSR) related to temporal discontinuities. The AR model based on ASTSR not only produces more accurate model parameters but also reduces the computational complexity in the motion picture restoration.

• Structural Engineering and Mechanics
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• v.15 no.3
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• pp.299-314
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• 2003

This investigation presents an efficient method for identifying modal characteristics from the measured displacement, velocity and acceleration signals of multiple channels on structural systems. A Vector Backward Auto-Regressive model (VBAR) that describes the relationship between the output information in different time steps is used to establish a backward state equation. Generally, the accuracy of the identified dynamic characteristics can be improved by increasing the order of the Auto-Regressive model (AR) in cases of measurement of data under noisy circumstances. However, a higher-order AR model also induces more numerical modes, only some of which are the system modes. The proposed VBAR model provides a clear characteristic boundary to separate the system modes from the spurious modes. A numerical example of a lumped-mass model with three DOFs was established to verify the applicability and effectiveness of the proposed method. Finally, an offshore platform model was experimentally employed as an application case to confirm the proposed VBAR method can be applied to real-world structures.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.713-719
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• 2009

In this paper, we develope a water demand forecasting algorithm using AR(Auto-regressive) and MLP(Multi-layer perceptron). To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "A" purification plant at Jeon-Buk province during 2007-2008, and then performed the proposed method with various input factors selected through various analyses. As noted in experimental results, the performance of three types model such as multi-regressive, AR(Auto-regressive), and AR+MLP(Auto-regressive + Multi-layer perceptron) show 5.1%, 3.8%, and 3.6% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict short-term water demand for the efficient operation of a water purification plant.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.366-385
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• 2013

In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.249-250
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• 2002

In this paper, the relationship among the 3-D surface topography parameters are studied. Several surface topography parameters that are important in tribology are calculated against various surface topography data. 3-D surface data with desired properties are generated by using the non-causal 2-D auto-regressive (AR) model. The non-causal 2-D AR model is a random 3-D surface topography model that can generate 3-D surface topography data with specified parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.561-570
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• 2009

Ultra-wideband communication systems based on impulse radio have merits that are possible for the high data rate transmission, high resolution ranging are positioning system. Conventionally, in order to accomplish these features, the high-speed ADC (Analog to Digital Convertor) is necessary to apply radio determination system operating in time domain. However, considering low rate - wireless personal area network (LR-WPAN) aims to low-cost hardware implementation, the expensive ADC converting GHz sampling per second is not appropriate. So, this paper introduces a low complex AR (Auto Regressive) model based non-coherent ranging scheme operating in frequency domain with using low-speed ADC utilizing analog Voltage Control Oscillator (VCO) mode for the frequency domain transformation. To verify the superiority of the proposed ranging and location algorithm working in frequency domain, the suggested IEEE 802.15.4a TG channel model is used to exploit affirmative features of the proposed algorithm with conducting the simulation results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.8
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• pp.1014-1017
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• 2012

In this paper, we introduce a method which enhances the azimuth resolution to obtain the high-resolution SAR image. We used ${\omega}-k$ algorithm to obtain the SAR image and extrapolation using auto-regressive(AR) method to enhance the azimuth resolution in the 2-D frequency domain. The AR method is a linear prediction model-based extrapolation technique. In the result, we showed the performance comparison with respect to the target range and the prediction order of Burg algorithm which is one of AR method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.131-134
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• 1998

본 논문에서는 시공간 적응적 기반영역 (Adaptive Spatio-Temporal Support Region : ASTSR)을 바탕으로 하는 일반화된 Auto-Regressive(AT)모델을 제안한다. 시공간 적응적 기반 영역은 영상 내 경계선의 특성과 동영상에서의 시간적 불연속 (temporal discontinuity) 개념을 이용하여 구성되어질 수 있다. 설정된 시공간 적응적 기반영역은 기존의 AR 모델에 적용되어지는 직사각형 형태의 기반영역에 비하여 보다 정상상태(stationarity)의 특성을 가지며 이로 인해 더 정확한 모델 파라미터들을 추출해 낼 수 있을 뿐 아니라 데이터의 처리량에서도 큰 이득을 얻을 수 있다. 제안된 방법은 손상된 동영상 데이터를 복원(motion picture restoration)하는 측면에 응용되어 실험되어졌으며 기존의 모델과 비교하여 우수한 성능을 보여주었다.