• 한국전산유체공학회지
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• 제3권1호
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• pp.37-45
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• 1998

A new and efficient inverse design method based on the numerical optimization technique has been developed. The 2-D incompressible Navier-Stokes equations are solved for obtaining the objective functions and coupled with the optimization procedure to perform the inverse design. The steepest descent and the conjugate gradient method have been applied to find the searching direction. The golden section method was applied to compute the design variable intervals. It has been found that the airfoil and the pump impellers are well converged to their targeting shapes.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권4호
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• pp.153-162
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• 2019

최근 철도소음으로 인해 발생하는 궤도 주변 구조물의 민원 방지와 궤도 주변 산업단지의 초정밀 장비들의 정상적인 운영을 위해 철도 진동을 정량적으로 평가할 수 있는 기술개발이 필요하다. 기존의 해석적인 방법은 매우 복잡한 동적 응답 모델이 요구되며, 요구 모델의 부정확성으로 인한 결과의 신뢰성을 확보하기 어려운 문제가 있다. 따라서, 본 논문에서는 철도 진동에 영향을 주는 요소들을 분류한 국내 철도진동 실측 데이터베이스를 기반으로 Linear Regression, Gradient Descent 기법을 이용해 철도 운행으로부터 발생되는 진동값을 추론하는 철도진동 평가 알고리즘 및 시스템을 제안한다. 제안된 알고리즘으로 얻은 추론결과는 기존의 해석적 방법에 비해 높은 효율성과 정확성을 보인다.

• 비파괴검사학회지
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• 제25권3호
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• pp.189-200
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• 2005

Ultrasonic inspection methods are widely used for detecting flaws in materials. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an approach which uses LMS method to determine the coordinates of the ultrasonic probe followed by the use of SAFT to estimate the location of the ultrasonic reflector The method is employed for classifying NDE signals from the steam generator tubes in a nuclear power plant. The classification results using this scheme for the ultrasonic signals from cracks and deposits within steam generator tubes are presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1377-1379
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• 1996

In this paper, each parameter was identified by the gradient descent method to overcome difficulty deciding fuzzy rules of FLC for the unknown process and the type of membership Junctions. Usually PID or optimal control theories have been mostly usee in control field so far. However, optimal control requires much time for calculation because of adaptation for disturbance and nonlinearity. And intricate technique such as MRAS which can be realized only by an expert are limited to be used in the systems requiring rapid and precise response because of comparatively longer calculating time and complicateness. Gradient descent method is a method to find Z minimizing a function about a certain vector Z. And required output of FLC is gained using gradient approaching method in order to adapt control rule parameters of FLC. Simulation proved validation of this algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.63-74
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• 2017

Recently in the area of ICT, the M2M and IoT are in the spotlight as a cutting edge technology with the help of advancement of internet. Among those fields, the smart home is the closest area to our daily lives. Smart home has the purpose to lead a user more convenient living in the house with WLAN (Wireless Local Area Network) or other short-range communication environments using automated appliances. With an arrival of the age of IoT, this can be described as one axis of a variety of applications as for the M2H (Machine to Home) field in M2M. In this paper, we propose a novel technique for estimating the location of a terminal that freely move within a specified area using the RSSI (Received Signal Strength Indication) in the WLAN environment. In order to perform the location estimation, the Fingerprint and KNN methods are utilized and the LMS with the gradient descent method and the proposed algorithm are also used through the error correction functions for locating the real-time position of a moving user who is keeping a smart terminal. From the estimated location, the nearest fixed devices which are general electric appliances were supposed to work appropriately for self-operating of virtual smart home. Through the experiments, connection and operation success rate, and the performance results are analyzed, presenting the verification results.

• Journal of information and communication convergence engineering
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• 제13권2호
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• 한국수자원학회논문집
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• 제31권1호
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• pp.45-57
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• 1998

본 논문의 목적은 다목적 저수지의 홍수유입량 예측을 위한 방법으로 병렬다중결선의 계층구조를 가진 신경망이론에 의하여 홍수시 불확실한 비선형시스템의 특성을 같는 저수지 유입량 예측모형을 개발하는 것이다. 신경망이론을 이용한 예측모형의 개발을 위하여 역전파 학습알고리즘을 사용하였으며 역전파 학습알고리즘 사용시 흔히 대두되는 지역최소값 문제와 수렴속도의 향상을 위해서 최적화기법인 경사하강법을 이용한 모멘트법과 경사하강법과 Gauss-Newton 방법을 이용한 Leverberg-Marquardt 법을 사용하였다. 모형개발에 사용된 자료는 연속적인 값으로 입력자료와 출력자료를 강우와 댐유입량을 학습시킨 후, 저수지의 홍수유입량 예측을 위한 다층신경망 모형을 구성하였다. 학습시 사용한 자료를 토대로 개발된 모형을 검정한 결과 매우 만족스런 결과를 얻을 수 있었고 실제 충주댐 유역을 대상으로 저수지 홍수유입량 예측결과 모형의 타당성을 입증할 수 있었다.

• 대한전기학회논문지
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• 제37권6호
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• pp.368-375
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• 1988

This paper is concerned with the design of the optimal gains of the controller in the speed control system for the induction motor controlled by the microprocessor. The system is modelled with the discrete-time state equation, considering the time delay, for the facility of the optimization techniques. Introducing the conjugate gradient descent method, as the optimization technique, are derived the optimal gains, the gains which give the best transient characteristics. At the optimal gains obtained, the theoretcal transient responses are verified by experimental ones on a 5HP induction motor drive system.

• 설비공학논문집
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• 제7권4호
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• pp.566-576
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• 1995

A Computational code has been developed in order to design axial fans by the numerical optimization techniques incorporated with flow analysis code solving three-dimensional Navier-Stokes equation. The steepest descent method and the conjugate gradient method are used to look for the search direction in the design space, and the golden section method is used for one-dimensional search. To solve the constrained optimization problem, sequential unconstrained minimization technique, SUMT, is used with imposed quadratic extended interior penalty functions. In the optimization of two-dimensional cascade design, the ratio of drag coefficient to lift coefficient is minimized by the design variables such as maximum thickness, maximum ordinate of camber and chord wise position of maximum ordinate. In the application of this numerical optimization technique to the design of an axial fan, the efficiency is maximized by the design variables related to the sweep angle distributed by quadratic function along the hub to tip of fan.