• Communications of the Korean Mathematical Society
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• v.33 no.3
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• pp.1025-1037
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• 2018

We consider an ill-posed problem for the heat equation $u_{xx}=u_t$ in the quarter plane {x > 0, t > 0}. We propose a new method to compute the heat flux $h(t)=u_x(1,t)$ from the boundary temperature g(t) = u(1, t). The operator $g{\mapsto}h=Hg$ is unbounded in $L^2({\mathbb{R}})$, so we approximate h(t) by $h_{\delta}(t)=u_x(1+{\delta},\;t)$, ${\delta}{\rightarrow}0$. When noise is present, the data is $g_{\epsilon}$ leading to a corresponding heat $h_{{\delta},{\epsilon}}$. We obtain an estimate of the error ${\parallel}h-h_{{\delta},{\epsilon}}{\parallel}$, as well as the error when $h_{{\delta},{\epsilon}}$ is approximated by the trapezoidal rule. With an a priori choice rule ${\delta}={\delta}({\epsilon})$ and ${\tau}={\tau}({\epsilon})$, the step size of the trapezoidal rule, the main theorem gives the error of the heat flux as a function of noise level ${\epsilon}$. Numerical examples show that the proposed method is effective and stable.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.222-226
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• 2007

In this paper we composed the dual fuzzy rules using each region of specific points and $e-{\Delta}e$ phase plane In order to make dual fuzzy rule base. We composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. proposed method is alternately use at specific points of $e-{\Delta}e$ phase plane with two fuzzy control rules that is one control rule occruing the steady state error in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Also, two fuzzy control rules in the $e-{\Delta}e$ phase plane decide the change time according to response characteristics of plants. In order to confirm thef proposed algorithm. As the results of experiments through the hydraulic servo motor control system with a DSP processor, We verified that proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1149-1152
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• 1999

In this paper we analyzed each region of specific points and ｅ-Δｅphase plane in order to make fuzzy rule base. After we composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. The composed method are converged more rapidly than single rule base in convergence region. Proposed method is alternately use at specific points of ｅ-Δｅphase plane with two fuzzy control rules, that is one control rule occruing the steady state error used in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Two fuzzy control rules in the ｅ-Δｅ phase plane decide the change time according to response characteristics of plants. As the results of simulation through the second order plant and the delay time plan, Proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• The Korean Journal of Financial Management
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• v.12 no.2
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• pp.1-23
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• 1995

본 연구의 목적은 특정 금융기관의 주거래기업들에 대한 부실예측을 위해 주거래기업들을 잠식, 도산, 그리고 건전기업과 같이 세집단으로 구분하여 예측하고자 하며, 기업부실 예측력에 영향을 미치는 세 가지 요인으로서 표본구성, 투입 변수, 분석 기법의 관점에서 다음을 살펴보는 것이다. 첫째, 기업부실예측에서 전통적인 delta learning rule과 sigmoid함수를 사용한 역전파학습(신경망 I)과 이들의 변형형태인 normalized cumulative delta learning rule과 hyperbolic tangent함수를 사용한 역전파 학습(신경망 II)과의 예측력의 차이를 살펴보고 또한 이러한 두가지 신경망기법의 예측력을 MDA(다변량판별분석) 결과와 비교하여 신경망기법에 대한 예측력의 유용성을 살펴보고자 한다. 둘째, 세집단분류문제에서는 잠식, 도산, 건전기업의 구성비율이 위의 세가지 예측기법의 결과에 어떠한 영향을 미치는지를 살펴보고자 한다. 세째, 투입 변수선정은 기존연구 또는 이론을 바탕으로 연구자의 판단에 의해 선택하는 방법과 다수의 변수를 가지고 통계적기법에 의해 좋은 판별변수의 집합을 찾는 것이다. 본 연구에서는 이러한 방법들에 의해 선정된 투입변수들이 세가지 예측기법의 결과에 어떠한 영향을 미치는지를 살펴보고자 한다. 이러한 관점에서 본 연구의 실증분석 결과를 요약하면 다음과 같다. 1) 신경망기법이 두집단에서와 같이 세집단 분류문제에서도 MDA보다는 더 높은 예측력을 보였다. 2) 잠식과 도산기업의 수는 비슷하게 그리고 건전기업의 수는 잠식과 도산기업을 합한 수와 비슷하게 표본을 구성하는 것이 예측력을 향상하는데 도움이 된다고 할 수 있다. 3) 속성별로 고르게 투입변수로 선정한 경우가 그렇지 않은 경우보다 더 높은 예측력을 보였다. 4) 전통적인 delta learning rule과 sigmoid함수를 사용한 역전파학습 보다는 normalized cumulative delta learning rule과 hyperbolic tangent함수를 사용한 역전파 학습이 더 높은 예측력을 보였다. 이러한 현상은 두집단문제에서 보다 세집단문제에서 더 큰 차이를 나타내고 있다.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.71-74
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• 1995

In this paper, a new PID fuzzy controller(FC) based on parallel operation of PI and PD fuzzy control is presented. First, two fuzzy rule bases are constructed by separating the linguistic control rule for PID FC into two parts : one is e-.DELTA.e part, and the other is .DELTAL.$^{2}$e-.DELTA.e part. And then two FCs employing these rule bases indivisually are synthesized and run in parallel. The incremental control input is determined by taking weighted mean of the outputs of two FCs. The proposed PID FC improves the transient response of the system and gives better performance than the conventional PI FC.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.404-407
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• 1993

In this paper, a new PID fuzzy controller(FC) is presented. The linguistic control rules of PID FC is separated into two parts : one is e-.DELTA.e part, and the other is .DELTA.$^{2}$e - .DELTA.e part. And then two FCs employing these rule base individually are synthesized. The control input to the process is decided by taking weighted mean of the outputs of two FCs. The proposed PID FC improve the transient response of the system and gives better performance than the conventional PI FC.

• Journal of Industrial Technology
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• v.13
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• pp.43-48
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• 1993

In this paper, a new PID fuzzy controller (FC) is presented. The linguistic control rules of PID FC is separated into two parts : one is $e-{\Delta}e$ part, and the other is ${\Delta}^2e-{\Delta}e$ part. And then two FCs employing these rule base indivisually are synthesized. The control input to the process is decided by taking weighted mean of the outputs of two FCs. The proposed PID FC improve the transient response of the system and gives better performance than the conventional PI FC.

• Journal of Ocean Engineering and Technology
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• v.9 no.2
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• pp.98-111
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• 1995

High temperature low cycle fatigue shows that cycle-dependent crack growth owing to cyclic plastic deformation occurred simultaneosly with time-dependent crack growth owing to intergranular deformation. Consequently, to estimate crack growth rate uniquely, many to investigators have proposed various kinds of parameters and theories but these could not produce satisfactory results. Therefore the goal of this study is focused on prediction of crack growth rate using predominant damage rule, linear cumulative damage rule and transitional parameter ${\Delta}J_c/{\Delta}J_f$. On the basis of these sinusoidal loading waveform at 600$^{\circ}C$ and 700$^{\circ}C$.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1273-1281
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• 1993

Estimation of daily and seasonal evaportranspiration is essential for water resource planning irrigation feasibility study, and real-time irrigation water management . This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration . A neural network was developed to forecast daily evapotranspiration of the rice crop. It is a three-layer network with input, hidden , and output layers. Back-propagation algorithm with delta learning rule was used to train the neural network. Training neural network wasconducted usign daily actural evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity , and pan evaporation . During the training, neural network parameters were calibrated. The trained network was applied to a set of field data not used in the training . The created response of the neural network was in good agreement with desired values. Evaluating the neural networ performance indicates that neural network may be applied to the estimation of evapotranspiration of the rice crop.

• Journal of Power System Engineering
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• v.5 no.2
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• pp.93-99
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• 2001

This paper presents the automatic recognition system for car number plate. In our country, two types of number plate pattern is used. The one is old type of number plate, the other is new type of number plate. To recognize both new and old type number plates, the system must have flexibility. Therefore, in this paper, automatic recognition system is developed by use of the neural network for good adaptation, good generalization, and modulation. And because the number plate is made of three codes, the multi neural network consists of three networks. Neural network is teamed by GDR(Generalized Delta learning Rule) and it is verified the effectiveness of the method through experimental results.