By John G. Webster, Halit Eren
ISBN-10: 1439848939
ISBN-13: 9781439848937
The second one version of the bestselling dimension, Instrumentation, and Sensors guide brings jointly all points of the layout and implementation of size, instrumentation, and sensors. Reflecting the present cutting-edge, it describes using tools and methods for appearing sensible measurements in engineering, physics, chemistry, and the lifestyles sciences and discusses processing systems, Read more...
summary: the second one variation of the bestselling size, Instrumentation, and Sensors guide brings jointly all elements of the layout and implementation of size, instrumentation, and sensors. Reflecting the present state-of-the-art, it describes using tools and strategies for acting functional measurements in engineering, physics, chemistry, and the lifestyles sciences and discusses processing platforms, computerized info acquisition, relief and research, operation features, accuracy, mistakes, calibrations, and the incorporation of criteria for keep an eye on reasons
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J. Chang, Emission monitoring using multivariate soft sensors, in Proceedings of the American Control Conference, Vol. 1, Seattle, WA, 1995. 9. B. Lin, B. Recke, J. Knudsen, and S. B. Jorgensen, A systematic approach for soft sensor development, Computers and Chemical Engineering, 31(5), 419–425, 2007. 10. H. Kaneko, M. Arakawa, and K. Funatsu, Development of a new soft sensor method using independent component analysis and partial least squares, AIChE Journal, 55(1), 87–98, 2009. 11. Y. Rotem, A.
L. Fortuna, S. Graziani, and M. Xibilia, Comparison of soft-sensor design methods for industrial plants using small data sets, IEEE Transactions on Instrumentation and Measurement, 58(8), 2009. 14. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998. 15. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Upper Saddle River, NJ: Prentice Hall, 1997. 16. W. Zhong, D. Pi, and Y. Sun, SVM based soft sensor for antibiotic fermentation process, in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol.
1 Var. 2 Var. 3 Common issues found in industrial data sets: (a) missing values; (b) outliers; (c) data colinearity; (d) measurement noise. that some of the sensors occasionally will fail. A possible cause of missing data is related to the transmission of the data between the sensors and the database, errors in the database, problems in accessing the database, etc. Since most of the techniques applied to data-driven soft sensing cannot deal with missing data, a strategy for their replacement usually has to be implemented.
Measurement, instrumentation, and sensors handbook. Electromagnetic, optical, radiation, chemical, and biomedical measurement by John G. Webster, Halit Eren
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