• Title/Summary/Keyword: 2nd-Degree Polynomial Regression Method

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Prediction and Design of Edge Shape of Initial Strip for Thick Tube Roll Forming using Finite Element Method (유한요소해석을 이용한 후육관 롤포밍에서의 초기소재 에지 형상 예측과 설계)

  • Kim, Nak-Su;Lee, Seung-Yun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.4
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    • pp.644-652
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    • 2002
  • Increasing demands for Electric Resistance Welded pipes of high quality with thick wall require c lose investigations in edge deformation by slitting, strip deformation during break down farming, and difference of circumferential length. In order to obtain good quality of a welding zone, it is necessary to predict the edge shape of the initial strip. The modeling of the multi-pass thick tube roll forming process with rigid plastic finite element method ultra the edge shape prediction of an initial strip with 2nd-degree polynomial regression method are presented. Edge shapes of initial strip have been analyzed by the finite element method and designed by the regression method to satisfy the requirements in target fin pass. It is concluded that the proposed edge design method results in optimal edge shapes sat string the design requirements.

Number of sampling leaves for reflectance measurement of Chinese cabbage and kale

  • Chung, Sun-Ok;Ngo, Viet-Duc;Kabir, Md. Shaha Nur;Hong, Soon-Jung;Park, Sang-Un;Kim, Sun-Ju;Park, Jong-Tae
    • Korean Journal of Agricultural Science
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    • v.41 no.3
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    • pp.169-175
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    • 2014
  • Objective of this study was to investigate effects of pre-processing method and number of sampling leaves on stability of the reflectance measurement for Chinese cabbage and kale leaves. Chinese cabbage and kale were transplanted and cultivated in a plant factory. Leaf samples of the kale and cabbage were collected at 4 weeks after transplanting of the seedlings. Spectra data were collected with an UV/VIS/NIR spectrometer in the wavelength region from 190 to 1130 nm. All leaves (mature and young leaves) were measured on 9 and 12 points in the blade part in the upper area for kale and cabbage leaves, respectively. To reduce the spectral noise, the raw spectral data were preprocessed by different methods: i) moving average, ii) Savitzky-Golay filter, iii) local regression using weighted linear least squares and a $1^{st}$ degree polynomial model (lowess), iv) local regression using weighted linear least squares and a $2^{nd}$ degree polynomial model (loess), v) a robust version of 'lowess', vi) a robust version of 'loess', with 7, 11, 15 smoothing points. Effects of number of sampling leaves were investigated by reflectance difference (RD) and cross-correlation (CC) methods. Results indicated that the contribution of the spectral data collected at 4 sampling leaves were good for both of the crops for reflectance measurement that does not change stability of measurement much. Furthermore, moving average method with 11 smoothing points was believed to provide reliable pre-processed data for further analysis.