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A Self-Optimizing Cumulative Inversion Based on Combination Weight Method for Particle Size Distribution

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Electrical, Information Engineering and Mechatronics 2011

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 138))

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Abstract

Using cumulative inversion to get particle size distribution based on dynamic light scattering is a very important and popular method. However, noise, dust and drifts can interfere with this information heavily. In this paper, after the introduction of fundamentals of dynamic light scattering and the method of conventional cumulative inversion, a self-optimizing cumulative inversion based on the minimum weighted mean combined with fitting standard deviation and fitting correlation coefficient is proposed. By contrasting it with the cumulative inversion of Brookhaven Instrument in simulation experiments according to four noise factors, the results show the validity and feasibility of the proposed approach.

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Author information

Authors and Affiliations

  1. Department of Electronics, School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, 510006, China

    Jian Qiu & Peng Han

Authors
  1. Jian Qiu
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  2. Peng Han
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Corresponding author

Correspondence to Jian Qiu .

Editor information

Editors and Affiliations

  1. , School of Electrical Engineering and, Henan Polytechnic University, Jiaozuo, China, People's Republic

    Xudong Wang

  2. , School of Electrical Engineering and, Henan Polytechnic University, Jiaozuo, China, People's Republic

    Fuzhong Wang

  3. , College of Elementary Education, Chongqing Normal University, Chongqing, China, People's Republic

    Shaobo Zhong

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Qiu, J., Han, P. (2012). A Self-Optimizing Cumulative Inversion Based on Combination Weight Method for Particle Size Distribution. In: Wang, X., Wang, F., Zhong, S. (eds) Electrical, Information Engineering and Mechatronics 2011. Lecture Notes in Electrical Engineering, vol 138. Springer, London. https://doi.org/10.1007/978-1-4471-2467-2_13

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  • DOI: https://doi.org/10.1007/978-1-4471-2467-2_13

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2466-5

  • Online ISBN: 978-1-4471-2467-2

  • eBook Packages: EngineeringEngineering (R0)

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