• 한국지능시스템학회논문지
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• 제10권5호
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• pp.449-458
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• 2000

본 논문에서는 원유정제공정에서의 조성을 효율적으로 예측하기 이한 신경회로망을 이용한 조성식별방법을 제시한다. 제시한 신경회로망을 이용한 조성식별기(FINN)는 학습모드와 예측모드로 구성된다. 또한 Borland C++(3.0)빌드로 신경회로망 원료자동분석 소프트센서 시스템을 구현하였다. 그리고 시뮬레이션을 통해 제안한 신경회로망을 이용한 조성식별방밥의 유용성을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 한국정밀공학회지
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• 제15권4호
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• pp.105-110
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• 1998

In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.

• 대한기계학회논문집
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• 제19권8호
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• pp.1907-1914
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• 1995

An on-line measurement of the workpiece hardened depth in laser surface hardening processes is very much difficult to achieve, since the hardening process occurs in depth wise direction. In this paper, the hardened depth is estimated using a multilayered neural network. Input data of the neural network are the surface temperatures at arbitrary chosen five surface points, laser power and traveling speed of laser beam torch. To simulate the actual hardening process, a finite difference method(FDM) is used to model the process. Since this model yields the calculation results of the temperature distribution around the workpiece volume in the vicinity of the laser torch, this model is used to obtain the network's training data and laser to evaluate the performance of the neural network estimator. The simulation results show that the proposed scheme can be used to estimate the hardened depth with reasonable accuracy.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제22권2호
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• pp.115-123
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• 2018

This paper discusses extended Kalman filter method for solving learning problems of multilayered neural networks. A lot of learning algorithms for deep layered network are sincerely suffered from complex computation and slow convergence because of a very large number of free parameters. We consider an efficient learning algorithm for deep neural network. Extended Kalman filter method is applied to parameter estimation of neural network to improve convergence and computation complexity. We discuss how an efficient algorithm should be developed for neural network learning by using Extended Kalman filter.

• Journal of Electrical Engineering and Technology
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• 제3권4호
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• pp.468-475
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• 2008

This paper presents a method for estimating the transient stability status of the power system using radial basis function(RBF) neural network with a fast hybrid training approach. A normalized transient energy margin(${\Delta}V_n$) has been obtained by the potential energy boundary surface(PEBS) method along with a time-domain simulation technique, and is used as an output of the RBF neural network. The RBF neural network is then trained to map the operating conditions of the power system to the ${\Delta}V_n$, which provides a measure of the transient stability of the power system. The proposed approach has been successfully applied to the 10-machine 39-bus New England test system, and the results are given.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.28-33
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• 2000

The monitoring of the chatter vibration is necessarily required to do automatic manufacturing system. To this study, we constructed a sensing system using tool dynamometer in order to the chatter vibration on cutting process. And a approach to a neural network using the feature of principal cutting force signals is proposed. with the error back propagation training process, the neural network memorized and classified the feature of principal cutting force signals. As a result, it is shown by neural network that the chatter vibration can be monitored effectively.

• 한국정밀공학회지
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• 제16권10호
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• pp.25-34
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• 1999

In this paper, an algorithm is presented to recognize lane and obstacles based on highway road image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, edge detection, and identification of lanes. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction and the presence of absence of an obstacle. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing lane and obstacles. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning of assistance system

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집A
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• pp.956-963
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• 2001

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is predicted using the artificial neural network. The neural network has trained training examples of stress ratio, normalized eccentricity, off-centered direction and stress error using backpropagation loaming process. The prediction results of the error using the trained neural network are good agreement with FE analyzed ones.

• 한국전자통신학회논문지
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• 제5권2호
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• pp.145-151
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• 2010

본 논문에서는 두 개의 음으로부터 전달되어온 음장의 상태를 구하여 역 필터를 구성하는 적응필터 및 신경회로망을 사용한 음장의 역 필터링 시스템을 제안한다. 본 논문에서는 최소 2승 평균법을 사용하여 FIR 필터의 계수를 계산하여 이를 갱신함으로써 역 필터링을 구축하는 방법을 사용한다. 본 논문에서 제안한 신경회로망 및 적응필터의 기법에 의하여 비선형 왜곡이 있는 간단한 파형이 학습 가능한 것을 실험 결과로부터 확인할 수 있었다.