• 한국지진공학회논문집
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• 제27권3호
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• pp.139-145
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• 2023

It is essential to determine a proper earthquake time history as a seismic load in a seismic design for a critical structure. In the code, a seismic load should satisfy a design response spectrum and include the characteristic of a target fault. The characteristic of a fault can be represented by a definition of a type of possible earthquake time history shape that occurred in a target fault. In this paper, the pseudo-basis function is proposed to be used to construct a specific type of earthquake, including the characteristic of a target fault. The pseudo-basis function is derived from analyzing the earthquake time history of specific fault harmonic wavelet transform. To show the feasibility of this method, the proposed method was applied to the faults causing the Gyeong-Ju ML5.8 and Pohang ML5.3 earthquakes.

• 한국전산구조공학회논문집
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• 제14권3호
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• pp.381-390
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• 2001

본 연구에서는 균열의 특이성과 불연속성을 Element-Free Galerkin(EFG) 법에 반영하기 위해 특이기저함수를 포함하는 확장항을 기존의 EFG 근사함수에 추가하고 균열면을 가로지르는 형상함수 구성시 불연속함수를 적용한 향상된 EFG 균열해석기법을 제안하였다. 기존의 EFG법이 균열선단주변의 특이응력장을 표현하기 위해 상당한 절점추가를 필요로 하지만 본 연구에서 제안한 기법은 절점의 추가나 해석모형의 수정이 필요 없다. 또한, 기존의 확장근사함수를 사용하는 EFG법이 계방정식의 크기를 상당히 증가시키는데 반해, 개선된 EFG 균열해석기법은 확장근사함수를 적용범위를 국소영역으로 제한하여 계방정식의 크기증가를 최소화하고서도 정도 높은 수치해를 얻었다. 수치예제는 제안된 기법의 향상된 면모와 효율성을 검증하여 준다.

• 전기학회논문지
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• 제66권5호
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2203-2205
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• 1998

This paper describes fuzzy radial basis function networks(FRBFN) extracting fuzzy rules through the learning from training data set. The proposed FRBFN is derived from the functional equivalence between RBF networks and fuzzy inference systems. The FRBFN learn by assigning new fuzzy rules and updating the parameters of existing fuzzy rules. The parameters of the FRBFN are adjusted using the standard LMS algorithm. The performance of the FRBFN is illustrated with function approximation and system identification.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제26권1호
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• pp.13-33
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• 2019

The main aim of this paper is to establish some comparative growth properties of composite entire functions on the basis of their relative (p, q)-th order, relative (p, q)-th lower order, relative (p, q)-th type, relative (p, q)-th weak type of entire function with respect to another entire function where p and q are any two positive integers.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.163.1-163
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• 2001

In this paper, we use time-frequency localization analysis method to analize the target function and the area of the target space. When we analize the function with the time and frequency axis simultaneously, the characteristic of the function is shown more precisely and the area is covered by a certain block. After we analize the target function in the time-frequency space, we can decide the activation functions and compose the hidden layer of the RBFN by choosing the radial basis function which can represent the characteristic of the target function, RBFN made by this method, designs the good structure proper to the target problem because we can decide the number of hidden node first.

• 한국정밀공학회지
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• 제23권8호
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• pp.89-99
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• 2006

In this paper a new shape reconstruction method that allows us to construct surface models from very large sets of points is presented. In this method the global domain of interest is divided into smaller domains where the problem can be solved locally. These local solutions of subdivided domains are blended together according to weighting coefficients to obtain a global solution using partition of unity function. The suggested approach gives us considerable flexibility in the choice of local shape functions which depend on the local shape complexity and desired accuracy. At each domain, a quadratic polynomial function is created that fits the points in the domain. If the approximation is not accurate enough, other higher order functions including cubic polynomial function and RBF(Radial Basis Function) are used. This adaptive selection of local shape functions offers robust and efficient solution to a great variety of shape reconstruction problems.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.731-738
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• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• 정보처리학회논문지B
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• 제14B권5호
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• pp.377-382
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• 2007

본 논문에서는 방사 기저함수 네트워크의 파라미터를 전 영역에서 최적화하는 학습 알고리즘을 제안한다. 기존의 학습 알고리즘들은 지역 최적화만을 수행하기 때문에 성능의 한계가 있고 최종 결과가 초기 네트워크 파라미터 값에 크게 의존하는 단점이 있다. 본 논문에서 제안하는 하이브리드 모의 담금질 기법은 모의 담금질 기법의 전 영역 탐색 능력과 경사 기반 학습 알고리즘의 지역 최적화 능력을 조합하여 전 파라미터 영역에서 해를 찾을 수 있도록 한다. 제안하는 기법을 함수 근사화 문제에 적용하여 기존의 학습 알고리즘에 비해 더 좋은 학습 및 일반화 성능을 보이는 네트워크 파라미터를 찾을 수 있으며, 초기 파라미터 값의 영향을 크게 줄일 수 있음을 보인다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권5호
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• pp.555-562
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• 2004

소프트웨어 회사들은 새로운 개발보다는 기존 프로젝트의 유지보수와 성능향상 프로젝트를 보다 많이 수행한다. 기존의 비용 추정 모델들은 유지보수 프로젝트들에 적용할 수 있지만, 유지보수 분야에 적용시키기 위해서는 변경이 필요하다. 본 논문은 개발 프로젝트와 유지보수 프로젝트의 기능점수 계산방법을 분류하고 ISBSG의 밴치마킹 자료를 회귀 분석한 결과를 토대로 유지보수 프로젝트의 비용을 측정할 수 있는 방법을 제안하였다. 먼저, ISBSG 자료를 소프트웨어 비용에 영향을 미치는 요소인 프로그램 추가, 변경과 삭제 3가지 요소의 8가지 중에서 실제 유지보수가 나타나는 4가지 그룹으로 분류하였다. 그리고, 그룹별로 통계적 모델과 RBF 망(Radial Basis Function Network)을 이용한 모델을 개발하여 각각의 성능을 분석 평가한 결과 RBF 망이 통계적 모델보다 좋은 성능을 보였다.