• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.439-446
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• 2006

In the present study, we applied wavelet transform to decompose the hydro-meteorological data such as precipitation and temperature into the components with different return periods with a primary objective for extraction of nonlinear dynamical component. For the transform, we used the Daubechies wavelet of order 9 ('db9') as a basis function. Also, we applied the correlation dimension analysis to determine whether or not the detail and approximation components at the respective decomposition stage with the increasing of scale in the wavelet transform reveal the nonlinear dynamical characteristics. In other words, we proposed the combined use of the wavelet transform and the correlation dimension analysis as methodology to extract the nonlinear dynamical component from the hydro-meteorological data. The derived result has shown the method proposed in the present study is suitable for the segregation and extraction of the nonlinear dynamical component which is, in general, difficult to reveal by using the raw data.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.7 s.124
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• pp.638-648
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• 2007

A principal components analysis of the entire median HRIRs in the CIPIC HRTF database reveals that the individual HRIRs can be adequately reconstructed by a linear combination of several orthonormal basis functions. The basis functions represent the inter-individual and inter-elevation variations in median HRIRs. There exist elevation-dependent tendencies in the weights of basis functions, and the basis functions can be ordered according to the magnitude of standard deviation of the weights at each elevation. We propose a HRIR customization method via tuning of the weights of 3 dominant basis functions corresponding to the 3 largest standard deviations at each elevation. Subjective listening test results show that both front-back reversal and vertical perception can be improved with the customized HRIRs.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.381-393
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• 2005

As a promising technique for dimension reduction in regression analysis, Sliced Inverse Regression (SIR) and an associated chi-square test for dimensionality were introduced by Li (1991). However, Li's test needs assumption of Normality for predictors and found to be heavily dependent on the number of slices. We will provide a unified asymptotic test for determining the dimensionality of the SIR model which is based on the probabilistic principal component analysis and free of normality assumption on predictors. Illustrative results with simulated and real examples will also be provided.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.345-351
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• 2008

미래의 기후변화 영향평가에 있어 전지구모형(General Circulation Model)은 가장 중요한 자료 중 하나이다. 즉, 온실가스 방출(emission) 시나리오에 기초한 전지구모형의 모의결과를 이용하면 미래 수자원에 대한 정보를 얻을 수 있다. 하지만 미래 수자원은 방출 시나리오, 상세화(downscaling) 기법, 강우-유출모형, 전지구모형의 종류에 따라 크게 달라질 수 있어 매우 큰 불확실성(uncertainty)을 포함하고 있다. 이러한 불확실성을 줄이는 방법 중 하나로 전지구모형의 모의능력에 따라 가중치(weight)를 부여하고 결합(combining)하는 multi-model 앙상블(ensemble) 기법이 선진국을 중심으로 활발히 연구되고 있다. 본 연구에서는 우선 기후변화 영향평가를 위하여 국내에서 사용가능한 전지구모형을 조사하고 그 중CCSM3, CSRIO, ECHAM4, GFDL, MIRCO를 선택하였다. 한강 충주댐 유역에 대하여 과거($1980{\sim}1999$년)와 미래($2030{\sim}2049$년) 기간에 대하여 전지구모형의 기후정보를 간단한 선형보간법을 이용하여 상세화하였다. 다음으로 multi-model 앙상블 기법을 조사하였다. 본 연구에서는 Giorgi et al.(2002)이 제안한 Reliability Ensemble Average(REA) 기법을 적용하여 선형보간법으로 상세화한 전지구모형의 모의결과에 가중치를 주어 불확실성을 줄이는 연구를 수행하였다. 특히 REA를 구성하는 식 중 모형의 편차(bias) 뿐만 아니라 분산(variance)까지 고려함으로서 이를 개선하는 Modified-REA를 제안하였다. 제안한 방안을 이용하여 결합한 전지구모형의 모의결과가 기존 REA의 결과보다 기후정보의 불확실성을 더 줄일 수 있는 것으로 나타났다.

• Proceedings of the KSMRM Conference
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• 2001.11a
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• pp.176-176
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• 2001

목적： 기존의 역수신코일(inside-out receiver coil)로 관벽의 MR 영상을 얻을 때 영상영역이 좁고 수신감도가 불균일한 단점을 보완하면서 신호대 잡음비(S/N ratio)도 높일 수 있는 회전자계역수 신코일(quadratic inside-out receiver coil)의 개발을 목적으로 한다. 대상 및 방법： 8극형코일, 선형자계수신코일, 회전자계수신코일에 대하여 컴퓨터 모의실험으로 영상영역 및 감도의 균일성을 비교하였다. 회전자계수신코일은 안장 모양을 한 두 개의 선형자계코일이 서로 간섭이 일어나지 않도록 수직 방향으로 배열된 구조를 갖도록 하였다. 지름 3 cm 크기로 각 코일을 제작하였으며 지름 20 cm의 원통 중앙에 내경 4 cm의 관이 있는 팬텀을 만들어 MnC1$_2$를 섞은 물을 채운 다음 1.5T 초전도 MRI 장치와 0.3T 영구자석 MRI 장치에서 팬텀의 관벽 영상을 얻었다. 본 실험을 시행한 1.5T 장치의 구조 때문에 회전자계코일의 두 안장코일을 결합하는 회로를 제작하여 사용하였고 0.3T에서는 장치에 내장된 결합회로를 사용하였다. 또한 포르말린 용액에 보관된 소의 대장 조직 단면 영상을 FOV 10-12 cm로 얻어 회전자 계안장코일의 성능을 평가하였다.

• The Journal of the Korea Contents Association
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• v.6 no.2
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• pp.34-43
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• 2006

This paper shows modeling methods for VBR video trace. It is well known that VBR video trace is characterized as longterm correlated and highly intermittent burst data. To analyze this, we attempt to model it using neural network with auxiliary linear structures derived from residual threshold. For testing purpose, we generate VBR video trace from chaotic nonlinear function combined with the geometric random noise. The modeling result of the generated data shows that the attempted method represents more accurately than the traditional neural network. However, we also found that combining hRU to the attempted modeling method can yield a closer agreement to statistical features of the generated data than the attempted modeling method alone.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.945-956
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• 2021

In this study, we considered the algorithm of Legendre and Legendre (2012), which derives canonical correspondence analysis from weighted principal component analysis. And, it was proved that the canonical correspondence analysis based on the weighted principal component analysis is exactly the same as Ter Braak's (1986) canonical correspondence analysis based on the Gaussian response model. Ter Braak (1986)'s canonical correspondence analysis derived from a Gaussian response curve that can explain the abundance of species in ecology well uses the basic assumption of the species packing model and then conducts generalized linear model and canonical correlation analysis. It is derived by way of binding. However, the algorithm of Legendre and Legendre (2012) is calculated in a method quite similar to Benzecri's correspondence analysis without such assumptions. Therefore, if canonical correspondence analysis based on weighted principal component analysis is used, it is possible to have some flexibility in using the results. In conclusion, this study shows that the two methods starting from different models have the same site scores, species scores, and species-environment correlations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1273-1280
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• 2013

The UKF algorithm for tracking moving objects has fast convergence speed and good tracking performance without the derivative computation. However, this algorithm has serious drawbacks which limit its use in conditions such as Gaussian noise distribution. Meanwhile, the particle filter(PF) is a state estimation method applied to nonlinear and non-Gaussian systems without these limitations. But this method also has some disadvantages such as computation increase as the number of particles rises. In this paper, we propose the Unscented Particle Filter (UPF) algorithm which combines Unscented Kalman Filter (UKF) and Particle Filter (PF) in order to overcome these drawbacks.The performance of the UPF algorithm was tested to compare with Particle Filter using a 2-DOF (Degree of Freedom) Pendulum System. The results show that the proposed algorithm is more suitable to the nonlinear and non-Gaussian state estimation compared with PF.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.973-983
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• 2015

To improve the control performance of the IPMSM, a novel nonlinear disturbance observer is proposed by using the T-S fuzzy model. A T-S fuzzy model is the combination of local linear models considered at each operating point. Usually the inverse model is easy to obtain in linear systems but not in nonlinear systems. To design a nonlinear disturbance observer, a nonlinear inverse model is obtained based on nonlinear inverse model which is the fuzzy combination of the local linear inverse models. The proposed DOB is used with a PDC controller which is one of the T-S fuzzy controller, and its performance improvement is shown from the simulation results.