• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.238-240
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• 2003

As remote sensing is weighty in GIS updating, it is indispensable to get spatial information quickly and exactly. In this study, we have designed and implemented the program by two algorithms of GDPA (Gradient Direction Profile Analysis) and Hough transformation to extract linear features automatically from high-resolution imagery. We applied the software to embody both algorithms to KOMPSAT-EOC, IKONOS, and Landsat-ETM and made a comparative study of results.

• Journal of Advanced Marine Engineering and Technology
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• 제13권2호
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• pp.78-88
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• 1989

The fundamental objective of this paper has been to develop a means for incoporating the concept of the linear fractional transformation more generally and easily into multivariable feedback design procedure. When we design a continuous system, generally, we are constrained by design methods which arise specifically for the system. Also, in the design of descrete systems, it is the same concept. But the approach developed in this paper is very flexible in the view that in spite of being the continuous or discrete, the design can be done using a well known design method in both cases. That is, when we design a contnuous system or discrete system, the design can be done by a standard design method of continuous systmes or discrete ones, depending on the choice of the linear fractional transformation. Therefore, it is noted that this concept has broken the unflexibility of the conventional design rules for multivariable control system. In essence, the concept shows that if a given system is controllable, some desirable design, for examples, pole assignment within prespecified region, optimal controllers with poles within prespecified region etc., could be done easily by transforming a desirable region into a standard region, such as the complex left-half plane or the unit disk, by the chosen linear fractional transformation, and then by designing the transformed system using the well known standard results.

• Korean Journal of Mathematics
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• 제28권3호
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• pp.639-647
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• 2020

A simple type of Cohen's transformation consists of a polynomial and a linear fractional transformation. We study the effectiveness of Cohen transformation to find N-polynomials over finite fields.

• 호남수학학술지
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• 제41권3호
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• pp.505-514
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• 2019

In this paper, some results are obtained from studying convolution on the spectrum of full transformation semigroup and some of its subsemigroups using Cayley's table. The shift of ${\alpha}$ determines its eigenvalues and one-dimensional linear convolution is complex in Symmetric group.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.574-574
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.575-583
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Architectural research
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• 제8권1호
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• pp.47-56
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• 2006

In this research, the analysis of post-buckling behavior of tapered columns has been performed under a combined load of uniformly distributed axial load along the length and concentric axial load at free end by solving the nonlinear differential equation with the differential transformation technique. The buckling load at various slopes at free end of column is calculated and the results of the analysis using the differential transformation technique is verified with those of previous studies. It is also shown through the results that the buckling load of sinusoidal tapered columns is largest, the linear is second largest, and the parabolic is small in the all ranges of slopes at free end and the deflection of parabolic tapered columns in the x coordinates is largest, the sinusoidal is second largest, and the linear is smallest in the range of slope 0 to 140 degrees at free end. However, when the range of the slope is 160 to 176 degrees at the free end, the deflection of sinusoidal tapered columns in the x coordinates is largest, the linear is second largest, and the parabolic is smallest. In addition, for the linear tapered column, the buckling load increases along with the flexural stiffness ratio. Also, for the parabolic and the sinusoidal tapered column, the buckling loads increase and decrease as the flexural ratios increase in the range of flexural stiffness ratio n = 1.0 to n = 2.0. Through this research, it is verified that the differential transformation technique can be applied to solve the nonlinear differential equation problems, such as analysis of post-buckling behavior of tapered columns. It is also expected that the differential transformation technique apply to various more complicated problems in future.

• Genomics & Informatics
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• 제17권4호
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• pp.39.1-39.20
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• 2019

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogeneous sets of biosequences (composite data points). A composite data point is a set of ordinary data points (e.g., set of feature vectors). We theoretically extend the derivation of the largest generalized eigenvalue-based distance metric D_ij(γ₁) in any linear and non-linear feature spaces. We prove that D_ij(γ₁) is a metric under any linear and non-linear feature transformation function. We show the sufficiency and efficiency of using the decision rule $\bar{{\delta}}_{{\Xi}i}$(i.e., mean of D_ij(γ₁)) in classification of heterogeneous sets of biosequences compared with the decision rules min_𝚵iand median_𝚵i. We analyze the impact of linear and non-linear transformation functions on classifying/clustering collections of heterogeneous sets of biosequences. The impact of the length of a sequence in a heterogeneous sequence-set generated by simulation on the classification and clustering results in linear and non-linear feature spaces is empirically shown in this paper. We propose a new concept: the limiting dispersion map of the existing clusters in heterogeneous sets of biosequences embedded in linear and nonlinear feature spaces, which is based on the limiting distribution of nucleotide compositions estimated from real data sets. Finally, the empirical conclusions and the scientific evidences are deduced from the experiments to support the theoretical side stated in this paper.

• Journal of Advanced Marine Engineering and Technology
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• 제38권2호
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• pp.182-187
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• 2014

We propose nonlinear GMM-based transformation functions in an attempt to deal with the over-smoothing effects of linear transformation for voice processing. The proposed methods adopt RBF networks as a local transformation function to overcome the drawbacks of global nonlinear transformation functions. In order to obtain high-quality modifications of speech signals, our voice conversion is implemented using the Harmonic plus Noise Model analysis/synthesis framework. Experimental results are reported on the English corpus, MOCHA-TIMIT.

• 한국음향학회지
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• 제20권3호
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• pp.15-23
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• 2001

본 논문에서는 임의의 사람이 발성한 음성을 마치 다른 사람이 발성한 것처럼 들리도록 하는 음성변환 기술에 대하여 설명하고, 화자간의 성도 특성과 여기신호 특성 파라미터 변환을 독립적으로 수행하기 위한 변환방법을 실험한다. 성도 특성 파라미터 변환은 입력되는 음성신호에서 LPC (Linear Predictive Cofficient)켑스트럼을 추출하여 선형다변회귀모델에 적용하여 수행하고, 여기신호 특성 파라미터 변환은 잔차신호를 추출하여 LP-PSOLA (Linear Predictive-Pitch Synchronous Overlap and Add) 합성방식을 이용한 화자간의 평균 피치주기 변환으로 수행된다. 실험결과는 선형다변회귀모델과 LP-PSOLA 합성방식을 이용하여 변환된 음성이 대상화자의 음성에 유사함을 보여준다