• The Journal of the Acoustical Society of Korea
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• v.31 no.2
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• pp.93-99
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• 2012

For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

• Journal of IKEEE
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• v.26 no.2
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• pp.150-159
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• 2022

In this paper, we propose a physics simulation for augmented reality (AR) billiards content. The characteristics of the physics simulation for the proposed AR billiards content are as follows. First, physical equations are derived by calculating the force and moment of inertia applied to the billiards ball to realize the motion of the billiards ball similar to the real one in the AR environment. Then, we determine the velocity and angular velocity of the virtual billiards ball associated with the rotation of the virtual billiards ball with respect to the impact point. Second, using some vectors such as incidnet vector, normal vector, reflection vector, the trajectory of the virtual billiards ball would be implement. these equations are applied to AR environment so that AR billiards content could be implement. This physics simulation allows users to feel like the real world using a virtual pool table and induce them to interact with the real environment. As a result of the experiment, the accuracy range between the path of the real billiards ball and the path of the virtual billiards ball was calculated to be 97.75% to 99.11%. Therefore, it was determined that the performance of the physics simulation for the AR billiards content proposed in this paper performs similarly to the path of the real billiards ball.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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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.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.73-80
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• 2007

Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.

• Journal of The Korean Astronomical Society
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• v.32 no.2
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• pp.127-136
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• 1999

In this study we present the study of solar active regions based on BOAO vector magnetograms and H$\alpha$ filtergrams. With the new calibration method we analyzed BOAO vector magnetograms taken from the SOFT observational system to compare with those of other observing systems. In this study it has been demonstrated that (1) our longitudinal magnetogram matches very well the corresponding Mitaka's magnetogram to the extent that the maximum correlation yields r=0.962 between our re-scaled longitudinal magnetogram and the Mitaka's magnetogram; (2) according to a comparison of our magnetograms of AR 8422 with those taken at Mitaka solar observatory their longitudinal fields are very similar to each other while transverse fields are a little different possibly due to large noise level; (3) main features seen by our longitudinal magnetograms of AR 8422 and AR 8419 and the corresponding Kitt Peak magnetograms are very similar to each other; (4) time series of our vector magnetograms and H-alpha observations of AR 8419 during its flaring (M3.1/1B) activity show that the filament eruption followed the sheared inversion line of the quadrupolar configuration of sunspots, indicating that the flare should be associated with the quadrupolar field configuration and its interaction with new filament eruption. Finally, it may be concluded that the Solar Flare Telescope at BOAO works normally and it is ready to do numerous observational and theoretical works associated with solar activities such as flares.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.3
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• pp.145-156
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• 2016

A variety of methods for indoor positioning systems have been underway to ensure the safety of emergency rescuers who are working in dangerous situations such as fire fighters. However, since most systems display locations of rescue workers in two-dimension (2D)-based maps, it is difficult for a commander located in the outside to recognize locations of rescuers inside the building intuitively. An augmented reality (AR)-based indoor positioning monitoring system can display locations of rescuer inside the building that can be seen by commanders to help intuitive recognition of positioning. To monitor AR-based indoor positioning, it is necessary to have an estimation technique of line of sight vector of observers. In the present study, an estimation technique of a line of sight vector using ultra-wide band tranceiver installed inside the indoor to trace locations is presented.

• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.195-201
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• 2015

For median filtering (MF) detection in altered digital images, this paper presents a new feature vector that is formed from autoregressive (AR) coefficients via an AR model of the gradients between the neighboring row and column lines in an image. Subsequently, the defined 10-D feature vector is trained in a support vector machine (SVM) for MF detection among forged images. The MF classification is compared to the median filter residual (MFR) scheme that had the same 10-D feature vector. In the experiment, three kinds of test items are area under receiver operating characteristic (ROC) curve (AUC), classification ratio, and minimal average decision error. The performance is excellent for unaltered (ORI) or once-altered images, such as $3{\times}3$ average filtering (AVE3), QF=90 JPEG (JPG90), 90% down, and 110% up to scale (DN0.9 and Up1.1) images, versus $3{\times}3$ and $5{\times}5$ median filtering (MF3 and MF5, respectively) and MF3 and MF5 composite images (MF35). When the forged image was post-altered with AVE3, DN0.9, UP1.1 and JPG70 after MF3, MF5 and MF35, the performance of the proposed scheme is lower than the MFR scheme. In particular, the feature vector in this paper has a superior classification ratio compared to AVE3. However, in the measured performances with unaltered, once-altered and post-altered images versus MF3, MF5 and MF35, the resultant AUC by 'sensitivity' (TP: true positive rate) and '1-specificity' (FN: false negative rate) is achieved closer to 1. Thus, it is confirmed that the grade evaluation of the proposed scheme can be rated as 'Excellent (A)'.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.439-451
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• 2000

To compute derivatives using matrix vector multiplication method, new algorithms were introduced in [1.2]n By these algorithms, we reduced roundoff error in computing derivative using Chebyshev collocation methods (CCM). In this paper, some applications of these algorithms ar presented.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.821-830
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• 2006

This paper develops support vector based fuzzy linear and nonlinear regression models and applies it to forecasting the exchange rate. We use the result of Tanaka(1982, 1987) for crisp input and output. The model makes it possible to forecast the best and worst possible situation based on fewer than 50 observations. We show that the developed model is good through real data.