• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.286-286
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• 2000

We consider a negative unity feedback control system in which Che PIO, PI, PD or P controller and a transfer function having only poles are in cascade, We define the notion of the structural polynomial which means that there exists a subdomain of the coefficient space in which the polynomial is Hurwitz (left half plane stable) polynomial. We obtain the necessary and sufficient condition of the structure of the transfer function of which the characteristic polynomial is a structural polynomial, In addition, this paper present another necessary and sufficient condition for the existence of a constant gain controller with which the characteristic polynomial is structurally stable, For the structurally stabilizable P controller, it is allowed that the transfer function may not to all pole plants.

• 생물정신의학
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• 제18권1호
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• pp.5-14
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• 2011

Neuroimaging in psychiatry encompasses the powerful tools available for the in vivo study of brain structure and function. MRI including the volumetry, voxel-base morphometry(VBM) and diffusion tensor imaging (DTI) are useful for assessing brain structure, whereas function MRI, positron emission tomography(PET) and magnetoencephalography(MEG) are well established for probing brain function. These tools are well tolerated by the vast majority of psychiatric patients because they provide a powerful but noninvasive means to directly evaluate the brain. Although neuroimaging technology is currently used only to rule in or rule out general medical conditions as opposed to diagnosing primary mental disorders, it may be used to confirm or make psychiatric diagnoses in the future. In addition, neuroimaging may be valuable for predicting the natural course of psychiatric illness as well as treatment response.

• Journal of the Korean Statistical Society
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• 제19권1호
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• pp.1-14
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• 1990

This paper proposes an approach to the identification of trnasfer function models. A strategy for the identification of the model structure is based on R and S arrays constructed by the impulse response function of the model. Theoretical patterns of the arrays associated with the model are investigated, and the practical implementation method of the suggested approach is also discussed. Finally two published samples are employed to demonstrate the practicability of the approach.

• 한국해양공학회지
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• 제10권4호
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• pp.149-159
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• 1996

Multipurpose development of the coast and ocean can be considered as multifunction construction combining the functions of coastal protection, waterfront amenity and creation or rehabilitation of habitats. Multfunction development of coastal and ocean spaces can be accomplished by applying the ecosystem control structure of artificial habitats which will cultivate fishing ground with ecological harmony to the coastal protection system. To evaluate the applicability of ecosystem control structures as as fundamental coastal protection structure, wave control function of the structure is studied by numerical and physical analyses. Dimensional analysis and hydraulic experiment point out the importance of width and crest depth of ecosystem control structure, construction water depth and wave steepness. Wave control efficiency is estimated by the attenuation coefficient $(K_H)$ according to wave steepness $(H_0/L_0)$, relative constructed water depth $(h_i/H_0)$, relative berm width $(B/L_0)$ and relative crest depth $(h_B/H_0)$ of eosystem control structure. Empirical fomulas are suggested based on the results of model test by applying the multiple model based on this experimental results and numerical wave shoaling-dissipation-breaking model appears to be valid for the analysis of wave transformation around ecosystem control structure in the coastal waters.

• 소음진동
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• 제7권1호
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• pp.61-69
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• 1997

Despite of the development in the finite element method, it is difficult to get the finite element model describing the dynamic characteristics of the complex structure exactly. Therefore a number of different methods have been developed in order to update the finite element model of a structure using vibration test data. This paper outlines the basic formulation for the frequency response function based updating method. One important advantage of this method is that the intermediate step of performing an eigensolution extraction is unnecessary. Using simulated experimental data, studies are conducted in the case of 10 DOF discrete system. The solution of noisy and incomplete experimental data is discussed. True measured frequency response function data are used for updating the finite element model of a beam and a plate. Its applicability to the joint identification is also considered.

• Applied Science and Convergence Technology
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• 제25권6호
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• pp.128-132
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• 2016

We investigated the effect of Ar ion sputtering on the surface electronic structure of indium tin oxide (ITO) using X-ray and ultraviolet photoelectron spectroscopy (XPS and UPS) measurements with increasing Ar ion sputtering time. XPS measurements revealed that surface contamination on ITO was rapidly removed by Ar ion sputtering for 10 s. UPS measurements showed that the work function of ITO increased by 0.2 eV after Ar ion sputtering for 10 s. This increase in work function was attributed to the removal of surface contamination, which formed a positive interface dipole relative to the ITO substrate. However, further Ar ion sputtering did not change the work function of ITO although the surface stoichiometry of ITO did change. Therefore, removing the surface contamination is critical for increasing the work function of ITO, and Ar ion sputtering for a short time (about 10 s) can efficiently remove surface contamination.

• 융합신호처리학회논문지
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• 제11권1호
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• pp.80-87
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• 2010

함수합성법은 주어진 입출력 데이터 쌍으로부터 입출력관계를 충족하는 함수를 예측하는 것으로, 특성을 알 수 없는 시스템을 제어할 때에 필수적이다. 일반적으로 시스템은 비선형인 성질을 갖는 경우가 많고, 함수 합성에 취급하는 변수, 정수, 제약 등으로 조합된 문제가 발생하기가 쉽다. 그 함수를 합성하는 방법 중 한 가지로 유전적 프로그래밍이 제안되고 있다. 이것은 함수를 트리구조로 표시한 함수 트리에 유전적 조작을 적용하여, 입출력 관계를 충족하는 함수 트리를 탐색하는 방법이다. 본 논문에서는 기존의 유전적 프로그래밍에 의한 함수 합성법의 문제점을 지적하고, 새로운 4종류의 개선법을 제안한다. 즉, 함수 트리를 탐색할 때에 함수가 복잡하게 되는 것을 방지하기 위하여 함수 트리의 성장 억제, 조기 수렴을 목표로 하는 국소 탐색법의 채택, 함수 트리 내의 필요 없이 길어지는 요소의 효과적인 삭제, 대상으로 하는 문제의 특성을 이용하는 방법이다. 이러한 개선법을 이용할 경우, 기존의 유전적 프로그래밍에 의한 함수 합성법보다도 짧은 시간에 우수한 구조의 함수 트리가 구해지는 것을 2-spirals 문제에 대하여 컴퓨터 시뮬레이션을 통하여 확인하였다.

• Korean Journal of Mathematics
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• 제5권1호
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• pp.55-60
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• 1997

A continuum structure function(CSF) is a non-decreasing mapping from the unit hypercube to the unit interval. The reliability importance of component $i$ in a CSF at system level ${\alpha}$, $R_i({\alpha})$) say, is zero if and only if component $i$ is almost irrelevant to the system at level ${\alpha}$. A condition to check whether a component is almost irrelevant to the system is presented. It is shown that $R^{(m)}_i({\alpha}){\rightarrow}R_i({\alpha})$ uniformly as $m{\rightarrow}{\infty}$ where each $R^{(m)}_i({\alpha})$ is readily calculated.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.195-198
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• 2000

We propose a new wavelet neural network structure, for which we apply new recurrent nodes to the network, in this paper for the dynamic system identification and control. We will construct the wavelet neural network by using wavelet frame function. The function does not have the best approximation property, but it may be possible to apply some modification to the structure of the network because the constriction of orthogonality is loosened a little. This wavelet neural network we propose can obtain previous state information by its structure of the network without any addition of input, though the conventional wavelet network needs additional previous state input for the improvement of the dynamic performance. In numerical experience, the performance of the new wavelet neural network we propose in the nonlinear system with uncertainity of parameter Is equal to that of the wavelet network which used the additional previous information input, superior to that of the conventional wavelet network.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.