• 한국음향학회지
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• 제31권2호
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• pp.93-99
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• 2012

음성코딩 시 성도는 Linear Predictive Coding (LPC) 계수를 이용해서 모델링 한다. 일반적으로 LPC 계수는 양자화와 선형보간 관점에서 유리한 Line Spectral Frequency (LSF) 파라미터로 변경하여 사용한다. 10차 이상의 다차원 LSF 데이터를 벡터 양자화를 이용하여 직접 코딩하게 되면 벡터 내 상관관계 (intra-frame correlation)를 모두 이용할 수 있으므로 rate-distortion 관점에서는 높은 효율을 기대할 수 있다. 하지만, 계산량과 메모리 요구량이 높아져서 실제 코딩 시스템에서는 사용할 수 없게 되므로, 차원을 나누어 압축하는 Split Vector Quantization (SVQ)이 이용된다. 또한, LSF 데이터는 과거 벡터와의 벡터 간 상관관계 (inter-frame correlation)가 높으므로, 이를 이용한 Predictive Split Vector Quantization (PSVQ)이 사용되고 있다. PSVQ는 SVQ 보다 높은 rate-distortion 성능을 보인다. 본 논문에서는 음성 저장 장치를 위한 최적의 PSVQ를 구현하기 위해서 다수의 과거 프레임 정보와의 벡터 간상관관계 (inter-frame correlation)를 고려한 Multi-Frame AR-model 기반 SVQ (MF-AR-SVQ)를 제안하였다. 기존 PSVQ와 비교해 보았을 때, MF-AR-SVQ는 계산량과 메모리 요구량의 큰 증가 없이, 평균 spectral distortion 관점에서 약 1비트의 성능 향상을 보였다.

• 전기전자학회논문지
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• 제26권2호
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• pp.150-159
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• 2022

본 논문에서는 증강현실(AR) 당구 콘텐츠를 위한 물리 시뮬레이션을 제안한다. 제안하는 증강현실 당구 콘텐츠에 대한 물리 시뮬레이션의 특징은 다음과 같다. 먼저, 증강현실 환경에서 실제와 비슷한 당구공의 움직임을 구현하기 위해 당구공에 적용되는 힘과 관성모멘트 계산을 하여 물리식을 도출한다. 다음에 타격 지점에 대한 가상 당구공의 회전과 관련된 가상 당구공의 속도와 각속도를 구한다. 다음으로, 가상 당구공의 움직임 궤적이 실제 당구공과 비슷한 움직임을 구현하기 위하여 입사벡터, 법선벡터, 반사벡터 등의 물리식을 도출하게 된다. 이러한 방정식을 증강현실 환경에 적용하여 AR 당구 콘텐츠를 구현할 수 있다. 이러한 물리 시뮬레이션은 사용자가 가상 당구대를 사용하여 실제와 유사함을 느낄 수 있도록 하며 실제 환경과 상호 작용하게 돕는다. 실험 결과 실제 당구공의 경로와 가상 당구공의 경로 사이의 정확도 범위는 97.75%~99.11%로 계산됐다. 따라서 본 논문에서 제안하는 증강현실 당구 콘텐츠에 대한 물리 시뮬레이션의 성능은 실제 당구공의 경로와 유사함을 확인하였다.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제18권1호
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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.

• 천문학회지
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• 제32권2호
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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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• 제5권3호
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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.

• 한국정보통신학회논문지
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• 제23권2호
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• pp.117-126
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• 2019

부정맥 분류를 위한 기존 연구들은 분류의 정확성을 높이기 위해 신경망, 퍼지, 시계열 주파수 분석, 비선형 분석법 등이 연구되어 왔다. 이러한 방법들은 분류율를 향상시키기 위해 정확한 특징점과 많은 양의 신호를 처리해야 하기 때문에 데이터의 가공 및 연산이 복잡하며, 다양한 부정맥을 분류하는데 어려움이 있다. 본 연구에서는 AR(Auto Regressive) 모델링 기반의 특징점 추출과 SVM(Support Vector Machine)을 통한 조기수축 부정맥 분류 방법을 제안한다. 이를 위해 잡음을 제거한 ECG 신호에서 R파를 검출하고 QRS와 RR 간격의 특정 파형 구간을 모델링하였다. 이후 최적 세그먼트 길이(n1, n2), 최적 차수( p1, p2)의 4가지 AR 모델링 변수를 추출하고 SVM을 통해 Normal, PVC, PAC를 분류하였다. 연구의 타당성을 입증하기 위해 MIT-BIH 부정맥 데이터베이스를 대상으로 한 R파의 평균 검출 성능은 99.77%, Normal, PVC, PAC 부정맥은 각각 99.23%, 97.28, 96.62의 평균 분류율을 나타내었다.

• IEIE Transactions on Smart Processing and Computing
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• 제4권4호
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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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• 제7권2호
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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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• 제17권3호
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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.