• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.15-22
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• 2000

The analysis of power spectrum based on linear AR model is applied widely to quantize the response of autonomic nerve noninvasively, In this paper, we estimate the power spectrum density for heartrate variability of the electrocadiogram and pulse wave for short term data(less than two minute), The time series of heart rate variability is obtained from the time interval(RRI, PPI) between the feature point of the electrocadiogram and pulse wave for normal person, The generated time series reconstructed into new time series through polynomial interpolation to apply to the AR mode. The power spectrum density for AR model is calculated by Burg algorithm, After applying AR model, the power spectrum density for heart rate variability of the electrocadiogram and the pulse wave is shown smooth spectrum power at the region of low frequence and high frequence, and that the power spectrum density of electrocadiogram and pulse wave has similar form for same subject.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.411-416
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• 2019

This study focused on IKEA Place in IKEA and Amazon AR View in Amazon to study the differences between AR shopping applications. The first literature study examined the status and prospects of the AR market and conducted a case study on AR shopping applications. In the second phase, 'Honeycomb Model' of Peter mobile was reorganized into six usability principles, focusing on the major functions of AR shopping mall application, to conduct surveys and in-depth interviews based on the task. Based on this study, it is expected to help increase AR-based application user experience, concentrating on the actual experience of the user and finding out the major experience factors.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.1
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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.

• Journal of Welding and Joining
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• v.17 no.6
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• pp.100-107
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• 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.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.88-93
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• 2001

This study discusses 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 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 resulting pattern vectors of AR coefficients are then passed to the feature extraction block. Inside the feature extraction block, only those features that are most sensitive to different types of cutting mechanisms are selected. The experimental correlations between the different chip formation mechanisms and AR model coefficients are established.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.26-33
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• 2001

Though speaker recognition based on conventional AR HMM shows good performance, its lack of modeling the environmental noise makes its performance degraded in case of practical noisy environment. In this paper, a robust speaker recognition system based on AR HMM is proposed, where noise is considered in the observation signal model for practical noisy environment and duration-term is considered to increase performance. Experimental results, using the digits database from 100 speakers (77 males and 23 females) under white noise and car noise, show improved performance.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.04c
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• pp.22-26
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• 2008

In this paper, we developed a hybrid simulation model of carbon laser ablation under the Ar plasmas consisted of fluid and particle methods. Three kinds of carbon particles, which are carbon atom, ion and electron emitted by laser ablation, are considered in the computation. In the present simulation, we adopt capacitively coupled plasma with asymmetrical electrodes. As a result, in Ar plasmas, carbon ion motions were suppressed by a strong electric field and were captured in Ar plasmas. Therefore, a low number density of carbon ions were deposited upon substrate. In addition, the plume motions in Ar gas atmosphere was also discussed.

• Electrical & Electronic Materials
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• v.9 no.5
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• pp.498-505
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• 1996

In the present work, we have developed the simulator to optimize the process conditions of the AR(antireflection) and HR(high-reflection) coatings for the high power laser diode. The simulator can run on the PC. After making the simple optical model, we establish the Maxwell equations for the model by the operator conversion. By using the Mathematica, we derive a matrix for the multilayer system by applying the equations to the model and optimize the AR and HR coating process conditions by obtaining the reflection rate from the matrix. We also prove the validity of the simulator by comparing the simulation with the characteristics of the laser diode which is AR and HR coated according to the optimized conditions.

• 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.