A FUSION MEASUREMENT METHOD FOR NANO-DISPLACEMENT BASED ON KALMAN FILTER AND NEURAL NETWORK

Zhuoliang Zhang, Chao Zhou, Zhangming Du, Lu Deng, Zhiqiang Cao, ShuoWang, Long Cheng, and Sai Deng

References

  1. [1] D. Lixin, F. Arai, and T. Fukuda, Destructive constructionsof nanostructures with carbon nanotubes through nanoroboticmanipulation, IEEE/ASME Transactions on Mechatronics,9(2), 2004, 350–357.
  2. [2] S. Fatikow, T. Wich, H. Hulsen, T. Sievers, and M. Jahnisch,Microrobot system for automatic nanohandling inside ascanning electron microscope, IEEE/ASME Transactions onMechatronics, 12(3), 2007, 244–252.
  3. [3] D. Zhang, J. Breguet, R. Clavel, V. Sivakov, S. Christiansen,and J. Michler, In situ electron microscopy mechanical testingof silicon nanowires using electrostatically actuated tensilestages, Journal of Microelectromechanical Systems, 19(3),2010, 663–674.
  4. [4] J.Wang and S. Guo, Development of a precision parallel micromechanismfor nano tele-operation, International Journal ofRobotics and Automation, 23(1), 2008, 56–63.
  5. [5] H. Zhang, Z. Wang, H. Yan, F. Yang, and X. Zhou, Adaptiveevent-triggered transmission scheme and H∞ filtering codesignover a filtering network with switching topology, IEEETransactions on Cybernetics, 49(12), 2019, 4296–4307.
  6. [6] H. Yan, P. Li, H. Zhang, X. Zhan and F. Yang, Event-triggereddistributed fusion estimation of networked multisensor systemswith limited information, IEEE Transactions on Systems, Man,and Cybernetics: Systems, 50(12), 2020, 5330–5337.
  7. [7] H. Yan, H. Zhang, F. Yang, C. Huang, and S. Chen, DistributedH∞ filtering for switched repeated scalar nonlinearsystems with randomly occurred sensor nonlinearities and asynchronousswitching, IEEE Transactions on Systems, Man, andCybernetics: Systems, 48(12), 2018, 2263–2270.
  8. [8] Z. Gong, B.K. Chen, J. Liu, C. Zhou, D. Anchel, X. Li, J.Ge, D.P. Bazett-Jones, and Y. Sun, Fluorescence and SEMcorrelative microscopy for nanomanipulation of subcellularstructures, Light: Science & Applications, 3, 2014, e224.
  9. [9] H.-Y. Chen, C.-L. He, C.-Y. Wang, M.-H. Lin, D. Mitsui, M.Eguchi, T. Teranishi, and S. Gwo, Far-field optical imaging ofa linear array of coupled gold nanocubes: Direct visualizationof dark plasmon propagating modes, ACS Nano, 5(10), 2011,8223–8229.
  10. [10] C. Zhou, Z. Gong, B.K. Chen, Z. Cao, J. Yu, C. Ru, M. Tan,S. Xie, and Y. Sun, A closed-loop controlled nanomanipulationsystem for probing nanostructures inside scanning electron microscopes,IEEE/ASME Transactions on Mechatronics, 21(3),2016, 1233–1241.
  11. [11] A.J. Fleming, A review of nanometer resolution position sensors:Operation and performance, Sensors and Actuators A:Physical, 190, 2013, 106–126.
  12. [12] C. Zhou, Y. Wang, L. Deng, Z. Wu, Z. Cao, S. Wang, and M.Tan, A TDC-based nano-scale displacement measure methodinside scanning electron microscopes, 2016 IEEE InternationalConf. on Robotics and Biomimetics (ROBIO), Qingdao, China,2016, 1298–1302.
  13. [13] Z. Du, T. Zhang, L. Deng, C. Zhou, Z. Cao, and S. Wang,A charge-amplifier based self-sensing method for measurementof piezoelectric displacement, 2017 IEEE International Conf.on Mechatronics and Automation (ICMA), Takamatsu, Japan,2017, 1995–1999.
  14. [14] N.-V. Nguyen, G. Shevlyakov, and V. Shin, Fusion of correlatedlocal estimates under non-gaussian channel noise, InternationalJournal of Robotics and Automation, 25(2), 2010,155–161.
  15. [15] S. Saeedi, L. Paull, M. Trentini, and H. Li, Occupancy gridmap merging for multiple robot simultaneous localization andmapping, International Journal of Robotics and Automation,30(2), 2015, 149–157.
  16. [16] Z.W. Wang, Q.X. Cao, N. Luan, and L. Zhang, A novelautonomous localization technique of subsea in-pipe robot,International Journal of Robotics and Automation, 25(2),2010, 102–108.
  17. [17] M. Perrollaz, R. Labayrade, D. Gruyer, A. Lambert, and D.Aubert, Proposition of generic validation criteria using stereovisionfor on-road obstacle detection, International Journal ofRobotics and Automation, 29(1), 2014, 32–43.
  18. [18] A.T. Alouani and T.R. Rice, On optimal synchronous andasynchronous track fusion, Optical Engineering, 37(2), 1998,427–433.
  19. [19] X. Lin, Y. Bar-Shalom, and T. Kirubarajan, Multisensormultitarget bias estimation for general asynchronous sensors,IEEE Transactions on Aerospace & Electronic Systems, 41(3),2005, 899–921.
  20. [20] Y. Hu, Z. Duan, and C. Han, Optimal batch asynchronousfusion algorithm, IEEE International Conf. on Vehicular Electronics& Safety, Shann’xi, China, 2005, 237–240.
  21. [21] Z. Zhang, Z. Du, L. Deng, C. Zhou, Z. Cao, S. Wang, and L.Cheng, A fusion measurement method based on Kalman filterwith improved state block and neural network for nanometerdisplacement, 2018 IEEE International Conf. on Mechatronicsand Automation (ICMA), Changchun, China, 2018, 539–544.
  22. [22] L.P. Yan, B.S. Liu, and D.H. Zhou, The modeling and estimationof asynchronous multirate multisensor dynamic systems,Aerospace Science & Technology, 10(1), 2006, 63–71.
  23. [23] C. Price, An analysis of the divergence problem in the Kalmanfilter, IEEE Transactions on Automatic Control, 13(6), 1968,699–702.
  24. [24] L. Yu, S. Wang, and K.K. Lai, An integrated data preparationscheme for neural network data analysis, IEEE Transactionson Knowledge & Data Engineering, 18(2), 2005, 217–230.
  25. [25] X.X. Wu and J.G. Liu, A new early stopping algorithmfor improving neural network generalization, InternationalConf. on Intelligent Computation Technology & Automation,Changsha, Hunan, China, 2009, 15–18.
  26. [26] E. Phaisangittisagul, An analysis of the regularization betweenL2 and dropout in single hidden layer neural network, InternationalConf. on Intelligent Systems, Bangkok, Thailand, 2016,174–179.
  27. [27] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradientbasedlearning applied to document recognition, Proceedingsof the IEEE, 86(11), 1998, 2278–2324.

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