• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2942-2953
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• 1996

This paper studies the effect of intersymbol interference due to sampling-time jitter on the worst-case bit error probability in a digital modultation over an additive white Gaussian noise channel, with the squared-root raised-cosine filters in the transmitter and the receiver. It derives approximation formulas using the Taylor series approximations. the principal results of this paper is the relationship between the worst-casse bit error probability, the degree of jitter, the roll factor of the raised cosine filter, and other quantities. Numerical results show, as expected, that the intersymbol interference decreases as the roll-off factor increases and the jitter decreases. They also show that the approximation formulas are accurate for smally intersymbol interference, i.e., for large roll-noise ratio $E_{b/}$ $N_{0}$.leq.7 dB and begin to lose accuracy for larger signal-to-noise ratio.o.o.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.75-78
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• 1995

In this paper it is concerned to develop control method using jar-test results in order to predict the optimum dosage of coaglant, PAC(PoliAluminum Chloride). Considering the relations with the reactions with the reaction of coagulation and flocculation, the five independent variables ( e, g, turbidity of raw water, water turbidity in flocculators, temperature, pH, and alkalynity) are selected out of parameters and they are put into calculation to develop a neural network model for PAC dosing process in water purification system. This model is utilized to predict optimum dosage of PAC. That is, the optimum dosage of PAC is searched in neural network model for PAC dosing process to minimize the water turbidity in flocculators. This searching is implemented by means of expert heuristics. The efficacy of the proposed contorl schemem and feasibility of acquired neural network model for PAC dosing contorl in water purification system is evaluated by means of computer simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.360-365
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• 2005

본 논문에서는 콘크리트 표면 균열 영상에서 균열을 효율적으로 추출하기 위한 화상처리 기법과 ART1 기반 RBF 네트워크를 제안하여 균열의 방향성을 인식한다. 본 논문에서 사용된 화상처리 기법으로는 균열 영상의 빛을 보정하기 위한 모폴로지 기법인 채움(Closing)연산을 적용하고 Sobel 마스크를 적용하여 균열 영상의 에지를 추출한 후 반복 이진화를 적용하여 균열 영상을 이진화한다. 이진화 된 영상에 두 차례에 걸쳐 잡음제거를 수행하여 콘크리트 표면 균열 영상으로부터 균열을 추출한다. 본 논문에서는 추출된 균열을 ART1 기반 RBF 네트워크에 적용하여 균열의 방향성(횡방향, 종방향, $-45^{\circ}$방향, $45^{\circ}$방향)을 자동으로 인식할 수 있는 방법을 제안한다. 제안된 ART1 기반 RBF 네트워크는 입력층과 중간층으로의 학습은 ART1을 적용하고 중간층과 출력층 간의 학습은 Delta 학습 방법을 적용한다. 실제 콘크리트 균열 영상을 적용하여 실험한 결과, 콘크리트 표면 균열 영상에서 효율적으로 균열을 추출할 수 있었고 제안된 ART1 기반 RBF 네트워크가 추출된 균열의 방향성 인식에 효율적인 것을 확인하였다.