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Tytuł artykułu

A Bayesian virtual metrology for quality inspection of mobile repeater systems

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Języki publikacji
EN
Abstrakty
EN
The technology of wideband code division multiple access (WCDMA) has been applied to band selective interference cancellation system (ICS) repeaters. To inspect the telecommunication quality of the systems, quality engineers must check the shape of the signals at the corresponding frequency band of the repeaters. However, measuring the signal quality is a repetitive manual task which requires much inspection time and high costs. In the case of small-sized samples, such as the example of an ICS repeater system, Bayesian approaches have been employed to improve the estimation accuracy by incorporating prior information on the parameters of the model in consideration. This research proposes a virtual method of quality inspection for products using a correlation structure of measurement data, mainly in a Bayesian regression framework. The Bayesian regression model derives prior information from historical measurement data to predict measurements of other frequency bandwidths by exploiting the correlation structure of each measurement data. Empirical results show the potential for reducing inspection costs and time by predicting the values of adjoining frequency bandwidths through measured data of a frequency bandwidth in the course of quality inspections of ICS repeater systems.
Twórcy
autor
  • Department of Industrial Engineering Hanyang University, Seoul, Republic of Korea
autor
  • Department of Industrial Engineering Hanyang University, Seoul, Republic of Korea
autor
  • Department of Industrial Engineering Hanyang University, Seoul, Republic of Korea
autor
  • Department of Industrial Engineering Hanyang University 17 Haengdang-dong Seongdong-gu, Seoul, Republic of Korea phone: (+82) 2220-0473
Bibliografia
  • [1] Hoff P.D., A first course in Bayesian statistical methods, Springer, 2009.
  • [2] Perez-Elizalde S., Cuevas J., Cross J., Selection of the bandwidth parameter in a Bayesian kernel regression model for genomic-enabled prediction, Journal of Agricultural, Biological, and Environmental Statistics, 20, 4, 512-532, 2015.
  • [3] Biller C., Adaptive Bayesian regression splines in semiparametric generalized linear models, Journal of Computational and Graphical Statistics, 9, 1, 122-140, 2000.
  • [4] Zougab N., Adjabi S., Kokonendji C., A Bayesian approach to bandwidth selection in univariate associate kernel estimation, Journal of Statistical Theory and Practice, 7, 1, 8-23, 2013.
  • [5] Rossi P.E., Bayesian statistics and marketing, Jonh Wiley & Sons, 2005.
  • [6] Chen P.H., Wu S., Lin J.S., Ko F., Lo H., Wang J., Yu C.H., Liang M.S., Virtual metrology: a solution for wafer to wafer advanced process control, IEEE International Symposium on Semiconductor Manufacturing, 155-157, 2005.
  • [7] Khan A.A., Moyne J.R., Tilbury D.M., An approach for factory-wide control utilizing virtual metrology, IEEE Transactions on Semiconductor Manufacturing, 20, 4, 364-375, 2007.
  • [8] Mun B.M., Lee C.U., Kim J.S., Ku J.R., Kim Z.I., Bae S.J., A Bayesian approach to predict storage reliability for pyrotechnic mechanical device, KSPE Spring Conference, 2015.
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  • [10] Kharchenko P., Silberstein L., Scadden D., Bayesian approach to single-cell differential expression analysis, Nature Methods, 11, 7, 740-744, 2014.
  • [11] Jo B.W., Kim Y.S., Kim Y.G., A fundamental study on analysis of electromotive force and updating of vibration power generating model on subway through the Bayesian regression and correlation analysis, Computational Structural Engineering Institute of Korea, 26, 2, 139-146, 2013.
  • [12] Berger J., Statistical Decision Theory and Bayesian Inference, Springer-Verlag, Berlin, 1985.
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  • [15] Tiao G.C., Zellner A., On the Bayesian estimation of multivariate regression, Journal of the Royal Statistical Society, 26, 2, 277-285, 1964.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5388cc0e-9951-48c8-8c62-d339d5635394
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