• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.324-326
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• 1999

현대는 빠른 기술의 발달과 제품의 대량 생산에 의한 가격의 인하로 인해 칼라 스캐너, 칼라 모니터와 칼라 프린터 같은 컴퓨터 주변 칼라 장비들이 널리 보급되었다. 뿐만 아니라 이들 장비들의 성능도 날이 갈수록 향상되고 있다. 그러나 이들 장비간의 칼라 재현 기술과 칼라 일치 문제에는 아직도 왜곡 현상이 남아 있어 이를 해결하기 위한 방법이 많이 연구되고 있다. 신경회로망에 의한 방법은 각 칼라 장비들의 특성을 쉽게 모델링 할 수 있을 뿐만 아니라 별도의 참조 테이블을 구성 할 것도 없이 직접 원하는 칼라 값으로의 매핑이 가능하기 때문에 효율적이다. 여기서는 신경회로망의 오차역전파(Error Back Propagation:EBP) 알고리즘을 이용하여 칼라 스캐너와 칼라 프린터의 모델링 구현과 이를 통합한 통합형 모델을 제시하고 나아가 이를 구현하기 위한 방법과 문제점에 대해 알아본다.

• Structural Engineering and Mechanics
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• v.18 no.3
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• pp.303-314
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• 2004

An artificial neural network (ANN) application is presented for flexural and axial vibration analysis of elastic beams with various support conditions. The first three natural frequencies of beams are obtained using multi layer neural network based back-propagation error learning algorithm. The natural frequencies of beams are calculated for six different boundary conditions via direct solution of governing differential equations of beams and Rayleigh's approximate method. The training of the network has been made using these data only flexural vibration case. The trained neural network, however, had been tested for cantilever beam (C-F), and both end free (F-F) in case the axial vibration, and clamped-clamped (C-C), and Guided-Pinned (G-P) support condition in case the flexural vibrations which were not included in the training set. The results found by using artificial neural network are sufficiently close to the theoretical results. It has been demonstrated that the artificial neural network approach applied in this study is highly successful for the purposes of free vibration analysis of elastic beams.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2875-2878
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• 2003

In this paper, we propose an EEG-based mental state prediction method during a mental tasks. In the experimental task, a subject goes through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining selection time. EEG signals from four subjects were recorded while they performed three mental tasks. Feature vectors defined by these representations were classified with a standard, feed-forward neural network trained via the error back-propagation algorithm. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or cognitive decision discrimination methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.81-87
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• 1994

This paper describes the recognition of the printed Hanguel(Korean Character) using Neural Network. In this study, Neural network is used in only specific classification. Hanguel is classified globally by using template matching. Neural network is learned using the segmented grapheme. The grapheme of Hanguel is segmented using the structural method. Neural network is constructed, which is corresponded to the kind and the shape of graphemes. Each neural network is multi layer perceptron. The learning algorithm is the modified error back propagation using descending epsilon method. With five test character sets, the recognition rate of 94.95% is obtained.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.2
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.186-192
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• 1996

A modified learning rule is introduced for the implementation of feedforward artificial neural networks with on-chip learning circuitry using standard analog CMOS technology. Learning rule, is modified form the EBP (error back propagation) rule which is one of the well-known learning rules for the feedforward rtificial neural nets(FANNs). The employed MEBP ( modified EBP) rule is well - suited for the hardware implementation of FANNs with on-chip learning rule. As a ynapse circuit, a four-quadrant vector-product linear multiplier is employed, whose input/output signals are given with voltage units. Two $2{\times}2{\times}1$ FANNs are implemented with the learning circuitry. The implemented FANN circuits have been simulatied with learning test patterns using the PSPICE circuit simulator and their results show correct learning functions.

• Annual Conference on Human and Language Technology
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• 1989.10a
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• pp.126-131
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• 1989

본 논문에서는 Error Back Propagation 학습을 이용해 한글 문자를 On-Line 인식하는 시스템을 제안한다. Pointing device의 궤적을 추적해 입력 패턴의 특징(feature)을 추출해 신경 회로망 입력으로 준다. 이때 사용하는 특징은 기본 획 (stroke)의 종류 및 획간의 상대적 위치 관계이다. 학습과정에서는 자소의 정의를 읽어 초성, 중성, 종성에 대해 각 획수마다 정의된 신경회로망의 weight를 조정한다. 인식 과정에서는 초성, 중성, 종성의 순으로 에러가 최소인 획수의 신경회로망 출력을 택하여 2 바이트 조합형 코드로 완성한다. 이로써 Intelligent Man-Machine Interface 시스템중 위치 및 크기에 무관한 전필 입력 시스템을 구현한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.154-157
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• 2003

In order for testing faults of combinatorial logic circuit, the authors have developed a new diagnosis method: "Neural Network (NN) fault diagnosis", based on fm error back propagation functions. This method has proved the capability to test gate faults of wider range including so called SSA (single stuck-at) faults, without assuming neither any set of test data nor diagnosis dictionaries. In this paper, it is further shown that what kind of fault models can be detected in the NN fault diagnosis, and the simply modified one can extend to test delay faults, e.g. logic hazard as long as the delays are confined to those due to gates, not to signal lines.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.573-578
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• 2002

In this paper, it Is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-negro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.1
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• pp.1-6
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• 2014

In this paper, controller propose to prevent compressor surge and improve the transient response of the fuel flow control system of turbojet engine. Turbojet engine controller is designed by applying Artificial Neural Network PID control algorithm and make an inference by applying Levenberg-Marquartdt Error Back Propagation Algorithm. Artificial Neural Network inference results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbojet engine for UAV. High Gain Observer is used to estimate to compressor rotation speed of turbojet engine. Using MATLAB to perform computer simulations verified the performance of the proposed controller. Response characteristics pursuant to the gain were analyzed by simulation.