• Advances in concrete construction
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• 제13권1호
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• pp.11-23
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• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(1)
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• pp.353-356
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• 2000

Multi-bias parameter extraction technique for HEMT small signa] equivalent circuits is presented in this paper. The technique in this paper uses S-parameters measured at various bias points in the active region to construct one optimization problem, of which the vector of unknowns contains only a set of bias-independent elements. Tests are peformed on measured S-parameters of a pHEMT at 30 bias points. Results indicate that the calculated S-parameters is similar to the measured data.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.1522-1527
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• 2004

In this paper, slurry fluid motion, abrasive particle motion, and effects of groove sizing on the pads are numerically investigated in the 2D geometry. Groove depth is optimized in order to maximized the abrasive effect. The simulation results are analyzed in terms of shear stress on pad, groove and wafer, streamline and velocity vector. The change of groove depth entails vortex pattern change, and consequently affects material removal rate. Numerical analysis is very helpful for disclosing polishing mechanism and local physics.

• 대한수학회지
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• 제49권1호
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• pp.99-112
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• 2012

This paper attempts to present a numerical method for solving optimal control problems. The method is based upon constructing the n-th degree Jacobi polynomials to approximate the control vector and use differentiation matrix to approximate derivative term in the state system. The system dynamics are then converted into system of algebraic equations and hence the optimal control problem is reduced to constrained optimization problem. Numerical examples illustrate the robustness, accuracy and efficiency of the proposed method.

• Journal of Advanced Marine Engineering and Technology
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• 제22권5호
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• pp.652-659
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• 1998

The present study is aimed to develop a new PIV operating software through optimization of vector tracking identification including versatile pre-processings and post-processing techniques. And the result exhibits an improved version corresponding various input and output multimedia compared to previous commercial software developed by other makers. An upgraded identification method called grey-level cross correlation coefficient method by direct calculation is suggested and related user-friendly pop-up menu are also represented. Post-processings comprising turbulence statistics are also introduced with graphic output functions.

• Management Science and Financial Engineering
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• 제19권1호
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• pp.25-28
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• 2013

Consider the inverse bin-packing number problem. Given a set of items and a prescribed number K of bins, the inverse bin-packing number problem, IBPN for short, is concerned with determining the minimum perturbation to the item-size vector so that all the items can be packed into K bins or less. It is known that this problem is NP-hard (Chung, 2012). In this paper, we investigate some special cases of IBPN that can be solved in polynomial time. We propose an optimal algorithm for solving the IBPN instances with two distinct item sizes and the instances with large items.

• 한국항공우주학회지
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• 제33권1호
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• pp.39-47
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• 2005

본 논문에서는 정지궤도 위성의 상대 운동과 최적화 기법과의 결합을 통해 새로운 형태의 위치유지 기법을 제안하였다. 상대 궤도 운동을 나타내기 위해 궤도 압축방법을 이용하여 비선형 미분 방정식 형태가 아닌 닫힌 해 형태의 모델을 사용하였으며, 매우 정확한 궤도 전파가 가능함을 확인하였다. 기존의 위치유지 기법은 궤도 요소를 이용하여 목표 궤도를 획득함으로써 궤도 형상을 유지하지만, 본 논문에서는 정밀한 위치유지를 위해 위성의 상대 위치를 직접 제어하였다. 최적화 목적 함수의 설정을 통해 다양한 형태의 기동 전략을 수립하였고, 구속 함수를 이용하여 상황에 따른 위치유지 범위를 설정하였다. 이 방법은 최적화 함수의 변경을 통해 다양한 위치유지 기법을 쉽게 적용할 수 있고, 그에 따른 궤도 운동을 분석할 수 있다. 비선형 시뮬레이션을 통해 위성의 위치가 허용범위 내에 적절하게 유지되고 있음을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• 제12권2호
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• pp.1-10
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• 2001

신경망은 점차 분류 및 함수추정을 위한 현대 통계적 방법론으로 부각되고 있다. 신경망은 특히 선형 회귀함수를 일반화시키는 유연한(flexible) 방법을 제공하며 일반적 비선형 함수를 모수화하는 방법으로 간주된다. 본 논문에서는 함수추정을 위한 신경망을 생각한다. 신경망이 훈련자료를 과대적합하는 것을 피할 수 있도록 하는 간단한 방법은 정칙화(regularization)이다. 신경망에서는 정칙화를 위해 주로 가중치 감소법(weight decay method)을 사용한다. 함수추정을 위해 가중치감소 신경망을 사용할 때 은닉노드수, 가중치모수, 학습률 및 학습반복회수가 중요한 모수이다. 본 논문에서는 유전자 알고리즘을 사용하여 가중치감소 신경망의 중요한 모수들을 자동으로 최적화하는 방법을 제안하고 결과적으로 가중치감소 신경망을 자동학습하는 방법을 설명한다. 그리고 다른 함수추정방법들과 자동학습된 가중치감소 신경망을 비교분석한다.

• 전기학회논문지P
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• 제59권3호
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• pp.279-287
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• 2010

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• 응용통계연구
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• 제18권2호
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• pp.421-434
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• 2005

기계학습은 패턴분류의 한 도구로써 광범위하게 연구되고 있다. 기계학습 방법들 중에서 서포트 벡터 기계(Support Vector Machines)는 많은 분야에서 연구되어지는 것으로 이진 패턴 분류문제에서 고차원의 특징공간에서 두 집합들 사이에 가장 큰 분리를 제공하는 최대 여유도(margin)를 가지는 분리 초평면을 찾는 것이다. 최대 여유도의 분리의 개념에 기초하여 Mangasarian(1968)은 다중-표면 방법(multi-surface method)을 제안하였고, 1980년대에 목적 계획법을 이용한 방법들이 광범위하게 개발되었다. 본 논문에서는 다목적 계획법과 목적 계획법을 이용한 수리계획법인 서포트 벡터 기계의 두가지 방법들을 제안하고 수치 예제들을 통하여 효용성에 대하여 논의하고자 한다.