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정길도,김형주,김용준,한병성,김명순 한국임상수의학회 1996 한국임상수의학회지 Vol.13 No.2
In this paper the development of an automatic body temperature measuring system which can be attached to the milking machine has been studied. Since the disease is highly related to the body temperature of the cattle, early detection of the abnormal temperature would prevent the severe problems which may occur in dairy farms. The electronic component AD590 is used as a temperature sensor for the system. The device is highly robust against the noise since the output signal is the current. So it can be applied to the long distance sensing. The resolution of the signal is $0.1{\circ}C$ and the current is 10 mV. Also the A/D converter is designed for interfacing the sensor with a computer. A temperature measuring experiment using the developed system has been done for measuring the temperature of human beings and the system was proven to be useful for measuring the body temperature of the dairy cattle properly.
Gil-do Jeong(정길도),Mynul(마이눌),Amkee Kim(김엄기) 대한기계학회 2006 대한기계학회 춘추학술대회 Vol.2006 No.11
The solid particle erosion behaviour of unidirectional carbon fiber reinforced plastic (CFRP) composites was investigated. The erosive wear of these composites was evaluated at different impact angles (30°,45°,60°,90°), different impact velocities (40, 55, 60,70㎧) and at three different fiber orientations (0°,45°,90°). The erodent used was silica sand with the size range 50-100㎛ of irregular shapes. The result showed ductile erosion behaviour with maximum erosion rate at 30° impingement angle. The Fiber orientations had a significant influence on erosion. The erosion rate displayed a strong dependence on impact velocity which followed the power law E∝V<SUP>n</SUP> for all materials. Based on three factors impact velocity(V), impact angle(α) and fiber orientation angle(β) a general method was proposed to predict the erosion rate of unidirectional fiber reinforced composites.
Empirical Modeling of a Steam Generator Using a Neural Networks
Chong, Kil-To,Kim, Sung-Joong 全北大學校 1995 論文集 Vol.40 No.-
다중입력 다중출력 시스템의 동역학적 특성을 규명할 수 있는 실험적인 모델을 개발하였다. 모델 구조로써 회귀성 다층퍼셉트론을 이용하였으며, 증기 원동기에 적용하여 보았다. 회귀성 다층퍼셉트론은 동적 신경회로망으로, 복합공정시스템의 입·출력 모델링에 있어 매우 효과적이다. 동역학적 학습 알고리즘을 회귀성 다층 퍼셉트론을 학습시키기 위해 이용하였다. 그 결과 정적 학습 알고리즘의 학습 시 수렴속도에 비해 커다란 효과를 가져왔다. 증기 원동기 모델을 개발과정에서 학습 및 검증 데이터에 대한 구동기, 프로세서 및 센서의 외란의 영향을 검토해 보았다. 결과적으로 신경회로망은 학습과 검증 시 데이터에 외란이 존재하더라도 매우 효과적임을 알 수 있었다.