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Personnel

Timothy M. (Tim) Young
Professor
Process analytics

Groups and Facilities


1 mention

Forest Products 
Drying, wood composites, manufacturing and sensors.


   

Recent Publications

Predicting and correlating the strength properties of wood composite process parameters by use of boosted regression tree models.  Carty, D., T. M. Young, F. M. Guess, and A. Petutschnigg.  2016.  4th Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2016) 1 4 June.

Case studies: a study of missing data imputation in predictive modeling of a wood composite manufacturing process.  Zeng, Y, T. M. Young, D. J. Edwards, F. M. Guess, and C. -H. Chen.  2016.  Journal of Quality Technology, 48(3):284-296.

Predicting the strength properties of wood composites using boosted regression trees Carty, D. M., T. M. Young, R. L. Zaretzki, F. M. Guess, and A. Petutschnigg.  2015.  Forest Products Journal, 65(7/8):365-371.

Real-time process modeling of oriented strand board panels.  Andre, N. O., and T. M. Young.  2014.  PTF BPI 2014. 3rd Intern. Conf. Processing Tech. for the Forest and Bio-based Products Industries, Salzburg/Kuchl, Austria, 24-26 September 2014. p. 393-400.

Improving estimates of critical percentile by induced censoring Edwards, D. J., F. M. Guess, R. V. Leon, T. M. Young, and K. A. Crookston.  2014.  Reliability Engineering and System Safety, 123: 47-56.

Quantifying the natural variation of formaldehyde emissions for wood composite panels.  Otjen J., and T. M. Young.  2014.  Panel & Engineered Lumber International Conference & Expo, March 20. Atlanta, GA.

Quantifying the natural variation of formaldehyde emissions for wood composite panels.  Otjen J., N. O. Andre, and T. M. Young.  2014.  68th Forest Products Society International Convention, August 13. Quebec City Canada.

Quantifying natural variation of formaldehyde emissions for wood composite panels.  Young, T. M., J. Otjen, and N. O. Andre.  2014.  PTF BPI 2014. 3rd Intern. Conf. Processing Tech. for the Forest and Bio-based Products Industries, Salzburg/Kuchl, Austria, 24-26 September 2014. p. 461-465.

Predicting key reliability response with limited response data Young, T. M., N. E. Clapp, Jr., F. M. Guess, and C. H. Chen.  2014.  Quality Engineering, 26(2):223-232.

Real-time process modeling of particleboard manufacture using variable selection and regression methods ensemble Andre, N. O., and T. M. Young.  2013.  European Journal of Wood and Wood Products (Eur. J. Wood Prod. Holz als Roh- und Werkstoff), 71(3): 361-370.

Kernel ridge regression with lagged dependent variable: applications to prediction of internal bond strength in a medium density fiberboard process Kim, N., Jeong, Y.S., Jeong, M.K., and T. M. Young.  2012.  IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 42(6):1011-102.

An analysis of boosted regression trees to predict the strength properties of wood composites Carty, D.  2011.  An analysis of boosted regression trees to predict the strength properties of wood composites.