• 한국인터넷방송통신학회논문지
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• 제9권2호
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• pp.1-6
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• 2009

많은 양의 데이터로부터 유용성있는 정보의 추출, 진단 및 예후에 대한 결정, 질병 치료의 응용 등은 바이오 인포머틱스(Bioinformatics)분야에서 매우 중요한 문제들이다. 본 논문에서는 암진단시스템에 적용하기위해 support vector machine을 위한 weogjted lernel fuction과 빠른 수렴성과 좋은 분류성능을 갖는 학습방법을 제안하였다. 제안된 kernel function에서 기본적인 kernel fuction의 weights는 암진단 학습단계에서 결정되고 분류단계에서 파리미터로 사용된다. 대장암 데이터와 같은 임상 데이터에 대한 실험결과에서 제안된 방법은 기존의 다른 kernel fuction들 보다 더 우수하고 안정적인 분류성능을 보여주었다.

• 전기학회논문지
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• 제62권4호
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• 한국터널지하공간학회 논문집
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• 제21권5호
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• pp.641-656
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• 2019

본 연구에서는 쉴드 TBM (Tunnel Boring Machine) 터널 디스크 커터의 적절한 교체 시기를 예측하기 위한 방법으로 머신러닝 기법을 사용한 방법을 제안하였으며, 이를 위해 국내 기 시공된 쉴드 TBM 현장의 데이터를 이용하여 다양한 머신러닝 알고리즘 중 SVM (Support Vector Machine)을 이용하여 예측 모델을 구축하고 그 성능을 평가하였다. 지반 조건별 디스크 커터의 마모와 높은 상관성을 갖는 TBM 기계 데이터와 디스크 커터 교체 이력을 분류하고, 이들을 SVM의 변수로 사용하여 3종류의 분류 함수를 적용하여 각각 학습을 한 후 예측을 수행한 결과, 각 지반 조건에 대해서 3종류의 SVM 분류 함수 중 전체적으로 RBF (Radial Basis Function) SVM의 예측성능이 가장 우수하며(평균적으로 80%의 정확도, 10% 오분류율), 지반 조건별로 구분 시 디스크 커터 교체 데이터의 수가 많을수록 예측 결과가 좋은 것으로 나타났다. 향후 많은 데이터를 축적하고 이를 모두 활용하여 학습모델을 지속적으로 발전시켜 나간다면 이와 같은 디스크 커터 교환주기를 예측하기 위한 머신러닝 기법의 실무 적용성이 매우 클 것으로 기대한다.

• Structural Engineering and Mechanics
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• 제71권6호
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• pp.627-641
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• 2019

The paper focuses on bending analysis of the functionally graded (FG) plates with arbitrary shapes and boundary conditions. The material property of FG plates is modelled by using the power law distribution. Based on the first order shear deformation plate theory (FSDT), the governing equations as well as boundary conditions are formulated and obtained by using the principle of virtual work. The coupled Boundary Element-Radial Basis Function (BE-RBF) method is established to solve the complex FG plates. The proposed methodology is developed by applying the concept of the analog equation method (AEM). According to the AEM, the original governing differential equations are replaced by three Poisson equations with fictitious sources under the same boundary conditions. Then, the fictitious sources are established by the application of a technique based on the boundary element method and approximated by using the radial basis functions. The solution of the actual problem is attained from the known integral representations of the potential problem. Therefore, the kernels of the boundary integral equations are conveniently evaluated and readily determined, so that the complex FG plates can be easily computed. The reliability of the proposed method is evaluated by comparing the present results with those from analytical solutions. The effects of the power index, the length to thickness ratio and the modulus ratio on the bending responses are investigated. Finally, many interesting features and results obtained from the analysis of the FG plates with arbitrary shapes and boundary conditions are demonstrated.

• Smart Structures and Systems
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• 제32권5호
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• 전자공학회논문지SC
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• 제45권3호
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• pp.68-79
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• 2008

대동맥 증강지수는 심실의 부하뿐만 아니라 대동맥의 탄력성을 직접적으로 나타낼 수 있는 장점 때문에 동맥의 경직도를 평가하는 지표로 주목받고 있다. 하지만, 정확한 대동맥 증강지수를 계산하기 위해서는 직접 카테터를 피험자에 삽입하여 측정해야 하기 때문에 임상에 적용하기에는 한계가 존재한다. 이러한 문제점 때문에 전달함수를 이용하여 요골 동맥 맥파로부터 대동맥 맥파를 간접적으로 추정하는 방법이 이용되고 있다. 본 논문에서는 전달함수를 구하기 위하여 Millar 카테터를 이용한대동맥 맥파와 토노메트릭 방식의 압력센서를 이용하여 요골동맥 맥파를 측정하였다. 또한, 기존의 증강점 검출 알고리즘 대신단계적으로 미분 차수를 증가시키면서 증강점을 검출하는 새로운 알고리즘을 제안하였다. 10차 ARX 모델을 이용하여 전달함수를 구현하였으며, 잔차 분석을 통하여 모델을 검증하였다. 증강점 검출 알고리즘 검증을 위하여 네 가지 종류의 합성파를 만들어 제안된 알고리즘이 기존 알고리즘 보다 더 정확한 결과를 나타내는 것을 확인할 수 있었다. 본 연구는 쉽게 측정할 수 있는 요골동맥 맥파를 이용하여 대동맥의 경직도를 평가할 수 있는 방법을 제시하였으며 이를 통하여 다양한 심혈관 질환의 조기 진단에 기여할 수 있을 것이다.

• 한국정밀공학회지
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• 제23권9호
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• pp.200-207
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• 2006

In this paper a new approach which combines implicit surface scheme and NURBS surface interpolation method is proposed in order to generate a complete surface model from unorganized point cloud data. In the method a base surface was generated by creating smooth implicit surface from the input point cloud data through which the actual surface would pass. The implicit surface was defined by a combination of shape functions including quadratic polynomial function, cubic polynomial functions and radial basis function using adaptive domain decomposition method. In this paper voxel data which can be extracted easily from the base implicit surface were used in order to generate rectangular net with good quality using the normal projection and smoothing scheme. After generating the interior points and tangential vectors in each rectangular region considering the required accuracy, the NURBS surface were constructed by interpolating the rectangular array of points using boundary tangential vectors which assure C$^1$ continuity between rectangular patches. The validity and effectiveness of this new approach was demonstrated by performing numerical experiments for the various types of point cloud data.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.2552-2557
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• 2007

Shape optimization of an internal cooling passage with staggered dimples on single surface is performed and performances of surrogates are evaluated in this paper. Optimizations are performed so that turbulent heat transfer can be enhanced compromising with pressure loss due to friction. The three-dimensional governing differential equations have been solved to find the overall Nusselt number and friction factor which are related to the objective functions of this problem. Three design variables were selected among the dimensionless geometric variables. Basic surrogate models such as second order polynomial response surface approximation (RSA), Kriging meta-modeling technique, radial basis neural network (RBNN), and derived press based averaged (PBA) surrogate model are constructed. The optimal points are searched from the above constructed surrogates by sequential quadratic programming (SQP). It is shown that use of multiple surrogates can increase the robustness in prediction of better design with minimum computational cost.

• Ocean Systems Engineering
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• 제2권2호
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• pp.137-145
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• 2012

Recent developments in underwater image recognition methods have received large attention by the ocean engineering researchers. In this paper, an improved bi-dimensional empirical mode decomposition (BEMD) approach is employed to decompose the given underwater image into intrinsic mode functions (IMFs) and residual. We developed a joint algorithm based on BEMD and Canny operator to extract multi-pixel edge features at multiple scales in IMFs sub-images. So the multiple pixel edge extraction is an advantage of our approach; the other contribution of this method is the realization of the bi-dimensional sifting process, which is realized utilizing regional-based operators to detect local extreme points and constructing radial basis function for curve surface interpolation. The performance of the multi-pixel edge extraction algorithm for processing underwater image is demonstrated in the contrast experiment with both the proposed method and the phase congruency edge detection.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.235-238
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• 1996

In this paper, a new optimization method which is a kind of random searching is presented. The proposed method is called RasVal which is an abbreviation of Random Search Method with Variable Seaxch Length and it can search for a global minimum based on the probability density functions of searching, which can be modified using informations on success or failure of the past searching in order to execute intensified and diversified searching. By applying the proposed method to a nonlinear crane control system which can be controlled by the Universal Learning Network with radial basis function(R.B.P.), it has been proved that RasVal is superior in performance to the commonly used back propagation learning algorithm.