• 전기학회논문지
• /
• 제56권1호
• /
• pp.197-205
• /
• 2007

We have proposed a novel line detection method based on the estimated probability density function of gradient directions of edges. By estimating peaks of the density function, we determine groups of edges that have the same gradient direction. For edges in the same groups, we detect lines that correspond to peaks of the connectivity weighted distribution of the distances from the origin. In the experiments using the Data Matrix barcode images and LCD images, the proposed method showed better performance than conventional Methods in terms of the processing speed and accuracy.

• 대한수학회보
• /
• 제53권2호
• /
• pp.589-600
• /
• 2016

In this paper, we propose a new incremental gradient method for solving a regularized minimization problem whose objective is the sum of m smooth functions and a (possibly nonsmooth) convex function. This method uses an adaptive stepsize. Recently proposed incremental gradient methods for a regularized minimization problem need O(mn) storage, where n is the number of variables. This is the drawback of them. But, the proposed new incremental gradient method requires only O(n) storage.

• 한국환경성돌연변이발암원학회지
• /
• 제18권2호
• /
• pp.83-88
• /
• 1998

To detect the mutation in a given sequence, there are variety of methods developed by use of the gel electrophoresis. One of the methods, TGGE (Temperature Gradient Gel Electrophoresis), is a popular technique because it can detect mutations in DNA fragment with ease and at low cost. This study used 200 bp BamHI-digested DNA fragment containing the human $\varepsilon$-globin promoter which was mutated[$\varepsilon$ F1*(-141), GATA- I*(-163), and GATA-1* & $\varepsilon$F1]. This BamHI-digested DNA fragment was directly used to detect the mutated DNA fragment on 50% denaturant gel with temperature gradient of 45$^{\circ}C$ through $53^{\circ}C$. In agreement with the theoretical result of MELTSCAN program (Brossette and Wallet, 1994) the mobilities of mutated DNA fragments were shown to be nearly distinguished on the temperature gradient gel. In contrast to the above result the heteroduplex analysis under the temperature gradient condition was shown to detect the mutated DNA fragments through the heteroduplex formation between strands of mutated DNA and wild-type DNA.

• 무역학회지
• /
• 제46권2호
• /
• pp.281-299
• /
• 2021

This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.

• 대한수학회논문집
• /
• 제13권1호
• /
• pp.195-209
• /
• 1998

In this paper we present a new iteration method for solving optimization problems governed by partial differential equations. We generalize the existing methods such as simple gradient methods and pseudo-time methods to get an efficient iteration method. Numerical tests show that the convergence of the new iteration method is much faster than those of the pseudo-time methods especially when the parameter $\sigma$ in the cost functional is small.

• 한국경영과학회지
• /
• 제13권2호
• /
• pp.25-33
• /
• 1988

An efficient heuristic solution procedure is developed for the minimum location problems. The gradient direction method and modified gradient approach are developed due to the differentiability of the objective functions. Suboptimal step size is obtained analytically. A Modified Gradient Procedure (NGP) is presented and compared with the hyperboloid approximation procedure (HAP) which is one of the best known methods.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제2권1호
• /
• pp.65-69
• /
• 2002

The main drawback of “classical”fuzzy systems is the inability to design and maintain their database. To overcome this disadvantage many types of extensions adding the adaptivity property to those systems were designed. This paper deals with one of them a new hybrid adaptation structure, called gradient-incremental adaptive fuzzy controller connecting gradient-descent methods with the so-called self-organizing fuzzy logic controller designed by Procyk and Mamdani. The aim is to incorporate the advantages of both Principles. This controller was implemented and tested on the system of LEGO robots. The results and comparison to a ‘classical’(non-adaptive) fuzzy controller designed by a human operator are also shown here.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제11권4호
• /
• pp.39-46
• /
• 2007

One of well known and much studied nonlinear matrix equations is the matrix polynomial which has the form G(X)=$A_0X^m+A_1X^{m-1}+{\cdots}+A_m$ where $A_0$, $A_1$, ${\cdots}$, $A_m$ and X are $n{\times}n$ real matrices. We show how the minimization methods can be used to solve the matrix polynomial G(X) and give some numerical experiments. We also compare Polak and Ribi$\acute{e}$re version and Fletcher and Reeves version of conjugate gradient method.

• 한국산학기술학회논문지
• /
• 제21권2호
• /
• pp.189-194
• /
• 2020

본 논문에서는 신경망을 학습하는 데 가장 많이 사용되고 있는 경사하강법에 대해 분석하였다. 학습이란 손실함수가 최소값이 되도록 매개변수를 갱신하는 것이다. 손실함수는 실제값과 예측값의 차이를 수치화 해주는 함수이다. 경사하강법은 오차가 최소화되도록 매개변수를 갱신하는데 손실함수의 기울기를 사용하는 것으로 현재 최고의 딥러닝 학습알고리즘을 제공하는 라이브러리에서 사용되고 있다. 그러나 이 알고리즘들은 블랙박스형태로 제공되고 있어서 다양한 경사하강법들의 장단점을 파악하는 것이 쉽지 않다. 경사하강법에서 현재 대표적으로 사용되고 있는 확률적 경사하강법(Stochastic Gradient Descent method), 모멘텀법(Momentum method), AdaGrad법 그리고 Adadelta법의 특성에 대하여 분석하였다. 실험 데이터는 신경망을 검증하는 데 널리 사용되는 MNIST 데이터 셋을 사용하였다. 은닉층은 2개의 층으로 첫 번째 층은 500개 그리고 두 번째 층은 300개의 뉴런으로 구성하였다. 출력 층의 활성화함수는 소프트 맥스함수이고 나머지 입력 층과 은닉 층의 활성화함수는 ReLu함수를 사용하였다. 그리고 손실함수는 교차 엔트로피 오차를 사용하였다.

• 대한조선학회논문집
• /
• 제33권3호
• /
• pp.94-102
• /
• 1996

유한변형문제에서 변형구배텐서를 탄소성 성분으로 분해하기 위한 가산분해와 곱분해방법에 대해서 설명하고, 이 두 방법에서 파생되는 역학량들의 의미와 그 차이점을 보였다. 변형구배에 대한 기존의 곱분해와 가산분해로 얻어지는 변형속도구배는 가산적으로 표현되지 않으며, 소성변형속도구배는 탄성변형의 영향을 받고 있다. 본 연구에서는 공축소성 가정을 도입하고, 수정된 곱분해를 통하여 소성변형속도구배가 탄성변형에 영향을 받지 않는 가산적인 변형속도 구배를 얻었다.