A Prototype Design of Scanning Mirror Used in MEMS and Its Experimental Verification

Author(s): Xianquan Luo*, Junwei Lv

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 13 , Issue 6 , 2020

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Background: The MEMS scanning mirror prototype is a spatial scanning device, which has advantages such as light mass, low drive voltage, large scanning and high angular measurement accuracy.

Methods: The MEMS scanning mirror prototype uses the piezoelectric driving principle to drive the micro-structure to realize two-axis scanning. The corner of the MEMS scanning mirror is measured by using a piezoelectric resistance sensor. In the paper, the damping properties of MEMS scanning mirrors have been studied, which deduce the damping force formula of MEMS scanning mirrors. Moreover, the influence of different sizes and structures of MEMS scanning mirrors on the damping force and the amplitude of scanning mirror angles are analyzed, and a structural optimization design method to reduce the driving voltage of MEMS scanning mirrors is proposed.

Results: The theoretical analysis, design and testing of piezoelectric driven MEMS scanning microscopes have been carried out.

Conclusion: Through related experiments, it is verified that the maximum scanning range and the precision index of angle measurement meet the requirements of the index.

Keywords: Scanning mirror, micro-electro-mechanical system (MEMS), experimental verification, design, rectangular scanning, probability.

J.C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated cluster", J. Cybern., vol. 3, pp. 32-57, 1973.
J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms., Plenum: New York, 1981.
A.R.T. Donders, "G.J.M.G Van Der Heijden, T Stijnen, K. G. Moons, “A gentle introduction to imputation of missing values", J. Clin. Epidemiol., vol. 59, pp. 1087-1091, 2006.
[http://dx.doi.org/10.1016/j.jclinepi.2006.01.014] [PMID: 16980149]
K.I. Penny, and T. Chesney, "Imputation methods to deal with missing values when data mining trauma injury data", In: 28th International Conference on Information Technology Interfaces, 2006pp. 213-218
K.I. Penny, and T. Chesney, "A comparison of missing value imputation methods for classifying patient outcome following trauma injury",
P. Hayati Rezvan, K.J. Lee, and J.A. Simpson, "The rise of multiple imputation: A review of the reporting and implementation of the method in medical research", BMC Med. Res. Methodol., vol. 15, p. 30, 2015.
[http://dx.doi.org/10.1186/s12874-015-0022-1] [PMID: 25880850]
A. Morisot, F. Bessaoud, P. Landais, X. Rébillard, B. Trétarre, and J.P. Daurès, "Prostate cancer: net survival and cause-specific survival rates after multiple imputation", BMC Med. Res. Methodol., vol. 15, p. 54, 2015.
[http://dx.doi.org/10.1186/s12874-015-0048-4] [PMID: 26216355]
P. Kalyani, ""Approaches to partition medical data using clustering algorithms", Int. J. Comput. Appl., vol. 49, 2012",
T.R. Sullivan, K.J. Lee, P. Ryan, and A.B. Salter, "Multiple imputation for handling missing outcome data when estimating the relative risk", BMC Med. Res. Methodol., vol. 17, no. 1, p. 134, 2017.
[http://dx.doi.org/10.1186/s12874-017-0414-5] [PMID: 28877666]
H. Junninen, H. Niska, K. Tuppurainen, J. Ruuskanen, and M. Kolehmainen, "Methods for imputation of missing values in air quality data sets", Atmos. Environ., vol. 38, pp. 2895-2907, 2004.
N.M. Noor, M.M.A.B. Abdullah, A.S. Yahaya, and N.A. Ramli, "Comparison of linear interpolation method and mean method to replace the missing values in environmental data set", Mater. Sci. Forum, vol. 803, pp. 278-281, 2015.
N.A. Zainuri, A.A. Jemain, and N. Muda, "A comparison of various imputation methods for missing values in air quality data", Sains Malays., vol. 44, pp. 449-456, 2015.
H. Li, X. Deng, and E. Smith, "Missing data imputation for paired stream and air temperature sensor data", Environmet., vol. 28, 2017.e2426
J. Han, J. Pei, and M. Kambe, Data mining concepts and techniques., Elsevier, 2011.
P. Li, Z. Chen, Y. Hu, Y. Leng, and Q. Li, "A weighted fuzzy c-means clustering algorithm for incomplete big sensor data", In: , China Conference on Wireless Sensor Networks, 2017, pp. 55-63
S.C. Chapra, and R.P. Canale, Numerical Methods for Engineers., McGraw-Hill Higher Education: Boston, 2010.
R.J. Little, and D.B. Rubin, Statistical Analysis with Missing Data., John Wiley & Sons, 2010.
Z. Jia, Z. Yu, and C. Zhang, "Fuzzy c-means clustering algorithm based on incomplete data", In: IEEE Int. Conf. Inform. Acquisit., 2006pp. 600-604
M. Sarkar, and T.Y. Leong, "Fuzzy K-means clustering with missing values", In: Proceedings of the AMIA Symposium, 2001, pp. 588-592
K.L. Wagstaff, and V.G. Laidler, "Making the most of missing values: Object clustering with partial data in astronomy," In:, Astronomical Data Analysis Software and Systems XIV . vol. 347. 2005, pp. 172.
B. Twala, M. Cartwright, and M. Shepperd, "Comparison of various methods for handling incomplete data in software engineering databases", In: International Symposium on Empirical Software Engineering, 2005, pp. 105-114
J.T. Chi, E.C. Chi, and R.G. Baraniuk, "k-pod: A method for k-means clustering of missing data", Am. Stat., vol. 70, pp. 91-99, 2016.
K. Honda, R. Nonoguchi, A. Notsu, and H. Ichihashi, "PCA-guided k-means clustering with incomplete data", IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011pp. 1710-1714
T. Karkkainen, and S. Ayramo, "Robust clustering methods for incomplete and erroneous data", In: WIT Transact. Inform. Commun. Technol., Vol. 33., 2004.
R.J. Hathaway, and J.C. Bezdek, "Fuzzy c-means clustering of incomplete data", IEEE Trans. Syst. Man Cybern. B Cybern., vol. 31, no. 5, pp. 735-744, 2001.
[http://dx.doi.org/10.1109/3477.956035] [PMID: 18244838]
D.Q. Zhang, and S.C. Chen, "Clustering incomplete data using kernel-based fuzzy c-means algorithm", Neural Process. Lett., vol. 18, pp. 155-162, 2003.
T. Li, L. Zhang, W. Lu, H. Hou, X. Liu, W. Pedrycz, and C. Zhong, "Interval kernel Fuzzy C-means clustering of incomplete data", Neurocomputing, vol. 237, pp. 316-333, 2017.
H. Timm, C. Döring, and R. Kruse, "Differentiated treatment of missing values in fuzzy clustering", Int. Fuzzy Syst. Assoc. World Cong., 2003, pp. 354-361
H. Timm, C. Doring, and R. Kruse, "Different approaches to fuzzy clustering of incomplete datasets", Int. J. Approx. Reason., vol. 35, pp. 239-249, 2004, .
L. Himmelspach, and S. Conrad, "Fuzzy clustering of incomplete data based on cluster dispersion", Int. Conf. Inform. Process. Manage. Uncertain. Knowl.Based Syst., 2010, pp. 59-68
L. Himmelspach, and S. Conrad, "Clustering approaches for data with missing values: Comparison and evaluation", In: 2010 Fifth International Conference on Digital Information Management (ICDIM), 2010, pp. 19-28
K. Siminski, "Clustering with missing values", Fundam. Inform., vol. 123, pp. 331-350, 2013.
Q. Zhang, and Z. Chen, "A distributed weighted possibilistic c means algorithm for clustering incomplete big sensor data", Int. J. Distribut. Sensors. Netw., vol. 5, no. 10, pp. 430814, 2014., .
L. Zhang, W. Lu, X. Liu, W. Pedrycz, and C. Zhong, "Fuzzy c-means clustering of incomplete data based on probabilistic information granules of missing values", Knowl. Base. Syst., vol. 99, pp. 51-70, 2016.
J. Li, S. Song, Y. Zhang, and Z. Zhou, "Robust k-median and k means clustering algorithms for incomplete data", Math. Probl. Eng., pp. 1-8, 2016.
H. Kang, "The prevention and handling of the missing data", Korean J. Anesthesiol., vol. 5, no. 64, p. 402, 2013.
Q. Wang, and J.N.K. Rao, "Empirical likelihood for linear regression models under imputation for missing responses", Can. J. Stat., vol. 29, pp. 597-608, 2001.
H. Toutenburg, C. Heumann, and T. Nittner, "Linear regression models with incomplete categorical covariates", Comput. Stat., vol. 17, pp. 215-232, 2002.
L. Beretta, and A. Santaniello, "Nearest neighbor imputation algorithms: A critical evaluation, , vol. 16, suppl", BMC Med. Inform. Decis. Mak., vol. 16, suppl. 3, . p. 74, 2016.
[http://dx.doi.org/10.1186/s12911-016-0318-z] [PMID: 27454392]
S. Hwang, J.H. Oh, J. Cox, S.J. Tang, and H.F. Tibbals, "Blood detection in wireless capsule endoscopy using expectation maximization clustering", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61441P, . 2006.
A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum likelihood estimation from incomplete data via the EM algorithm", J. R. Stat. Soc. Series B Stat. Methodol., vol. 39, pp. 1-38, 1977.
F.V. Nelwamondo, S. Mohamed, and T. Marwala, "Missing data: A comparison of neural network and expectation maximization techniques", Curr. Sci., vol. 93, no. 11, pp. 1514-1521, 2007.
C. Sammut, and G.I. Webb, Encyclopedia of machine learning., Springer Science & Business Media, 2011.
Y.G. Jung, M.S. Kang, and J. Heo, Clustering performance comparison using K-means and expectation maximization algorithms., 2014.
W. Al-Mudhafer, "Maximum Likelihood & Multiple Imputation of Incomplete Static and Dynamic Reservoir Data", In: 12th EAGE International Conference on Geoinformatics-Theoretical and Applied Aspects, 2013
H. Fang, "MI Fuzzy clustering for incomplete longitudinal data in smart health", Smart Health (Amst), vol. 1-2, pp. 50-65, 2017.
[http://dx.doi.org/10.1016/j.smhl.2017.04.002] [PMID: 28993813]
I.R. White, P. Royston, and A.M. Wood, "Multiple imputation using chained equations: Issues and guidance for practice", Stat. Med., vol. 30, no. 4, pp. 377-399, 2011.
[http://dx.doi.org/10.1002/sim.4067] [PMID: 21225900]
J.K. Dixon, "Pattern recognition with partly missing data", IEEE Trans. Syst. Man Cybern., vol. 9, pp. 617-621, 1979.
K. Bache, and M. Lichman, "UCI Machine Learning Repository Irvine, CA: University of California", School of information and computer science. Available at: .http://archive. ics. uci. edu/ml
Z. Huang, and M.K. Ng, "A fuzzy k-modes algorithm for clustering categorical data", IEEE Trans. Fuzzy Syst., vol. 7, pp. 446-452, 1999.
W.M. Rand, "Objective criteria for the evaluation of clustering methods", J. Am. Stat. Assoc., vol. 66, pp. 846-850, 1971.
L. Hubert, and P. Arabie, "Comparing partitions", J. Classif., vol. 2, pp. 193-218, 1985.
A. Strehl, and J. Ghosh, "Cluster ensembles-a knowledge reuse framework for combining multiple partitions", J. Mach. Learn. Res., vol. 3, pp. 583-617, 2002.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Page: [885 - 893]
Pages: 9
DOI: 10.2174/2352096512666191019153222
Price: $25

Article Metrics

PDF: 14