• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.303-306
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• 2002

인공위성영상을 이용하여 벡터 지도 생성은 지형에 따른 건물, 도로, 농지 등에 관한 벡터를 추출하는 작업이 필요하다. 이 작업의 정확도는 지도의 정확도와 상관관계가 있기 때문에 건물 추출의 정확성이 달라진다. 따라서 건물추출의 정확성을 향상시키기 위해 본 연구에서는 위성영상에서 건물 추출 알고리즘을 제안하였다. 이 알고리즘은 인공신경망을 이용하여 건물의 그림자를 추적하고 이를 중심으로 건물위치와 외형을 추정하는 알고리즘을 제안하고 실험하였으며, 양호한 결과를 얻었다.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.11
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• pp.124-131
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• 2001

Fault diagnosis of an assembled small motor relies usually on human experts hearing ability. The quality of diagnosis depends, however, heavily on physical conditions of the human experts. A fault diagnosis system for assembled small motors is developed using artificial neural network (ANN) in this paper. It is consisted of sound sampling device and fault diagnosis software package. Six parameters are defined to characterize the sampled sound waves. The Levenberg-Marquardt Backpropagation (LMBP) Algorithm is used to diagnose the fault of assembled small motors. Experimental results for more than two hundred small motors verify the performance of the developed system.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.361-367
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• 2016

The intruder detection system using digital PIR sensor has the problem that it can't recognize human correctly. In this paper, we suggest a new intruder detection system based on analog PIR sensor to get around the drawbacks of the digital PIR sensor. The analog type PIR sensor emits the voltage output at various levels whereas the output of the digitial PIR sensor is binary. The signal captured using analog PIR sensor is sampled, and its frequency feature is extracted using FFT or MFCC. The extracted features are used for the input of neural networks. After neural network is trained using various human and pet's intrusion data, it is used for classifying human and pet in the intrusion situation.

• Proceedings of the KWS Conference
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• 1997.10a
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• pp.210-215
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• 1997

• Proceedings of the Korean Society of Environment and Ecology Conference
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• 2006.10a
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• pp.211-213
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• 2006

• Proceedings of the KWS Conference
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• 1995.04a
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• pp.126-129
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• 1995

• 전기의세계
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• v.38 no.2
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• pp.53-65
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• 1989

공학적인 측면에서 볼때 20세기 초반이 자동차나 비행기와 같은 수송수단의 개발시기였다면 20세기 후반은 컴퓨터시대였다고 할 수 있을 것이다. 전문가들 중에서 다가오는 21세기는 인공지능소자의 개발시대가 될것이라고 전망하는 사람이 많다. 아마도 그리 멀지 않은 때에 사람과 비슷한 인지기능과 지능을 갖는 로봇트가 위성탐사를 위해 제작되어지리라 기대해 봄직하다.

• Proceedings of the KWS Conference
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• 2004.05a
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• pp.21-23
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• 2004

파이프용접에서 특정용접을 하기 위한 최적의 용접조건 선정하는 작업은 대개 많은 시간과 비용을 요구한다. 최근에 인공지능(AI) 기술을 이용하여 용접변수를 결정하기 위해서는 생산성, 용접결함 등 여러 가지 요소를 고려해야 한다고 주장한다. (중략)

• 유체기계공업학회:학술대회논문집
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• 2002.12a
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• pp.346-353
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• 2002

The steam generator feedwater flow rate in a nuclear power plant was estimated by means of artificial neural networks with the wavelet analysis for enhanced information extraction. The fouling of venturi meters, used for steam generator feedwater flow rate in pressurized water reactors, may result in unnecessary plant power derating. The backpropagation network was used to generate models of signals for a pressurized water reactor. Multiple-input single-output heteroassociative networks were used for evaluating the feedwater flow rate as a function of a set of related variables. The wavelet was used as a low pass filter eliminating the noise from the raw signals. The results have shown that possible fouling of venturi can be detected by neural networks, and the feedwater flow rate can be predicted as an alternative to existing methods. The research has also indicated that the decomposition of signals by wavelet transform is a powerful approach to signal analysis for denoising.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.27-33
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• 1997

A neural network approach has been developed to determine the depth of a surface breaking crack in a steel plate from ultrasonic backscattering data. The network is trained by the use of feedforward three-layered network together with a back-scattering algorithm for error correction. The signal used for crack insonification is a mode converted 70$^{\circ}$transverse wave. A numerical analysis of back scattered field is carried out based on elastic wave theory, by the use of the boundary element method. The numerical data are calibrated by comparison with experimental data. The numerical analysis provides synthetic data for the training of the network. The training data have been calculated for cracks with specified increments of the crack depth. The performance of the network has been tested on other synthetic data and experimental data which are different from the training data.