• Title/Summary/Keyword: 신경망회로 모델

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Monitoring Systems of a Grinding Trouble Utilizing Neural Networks(2nd Report) (신경망 회로를 이용한 연삭가공의 트러블 검지(II))

  • Kwak, J.S.;Kim, G.H.;Ha, M.K.;Song, J.B.;Kim, H.S.
    • Journal of the Korean Society for Precision Engineering
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    • v.13 no.11
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    • pp.57-63
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    • 1996
  • Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

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An Experimental Study on the Active Control of the Motion of Ship Cabin (모델실험에 의한 객실 운동의 능동제어 연구)

  • Bae, Jong-Gug;Lee, Jeh-Won;Joo, Hae-Ho;Shin, Chan-Bai
    • Journal of the Korean Society for Precision Engineering
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    • v.19 no.9
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    • pp.106-110
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    • 2002
  • A need fer stable and comfortable cabins in the high-speed passenger ships has increased. For active control of the motion of the ship cabin, a few control algorithms have been applied to the three dimensional real models in the vibration basin. Experimental results show that the feedforward neural network with a linear feedback controller is one of the promising control algorithms for this active control.

A Study on Development of a Forecasting Model of Wind Power Generation for Walryong Site (월령단지 풍력발전 예보모형 개발에 관한 연구)

  • Kim, Hyun-Goo;Lee, Yeong-Seup;Jang, Mun-Seok;Kyong, Nam-Ho
    • Journal of the Korean Solar Energy Society
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    • v.26 no.2
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    • pp.27-34
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    • 2006
  • In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.