Zhuoliang Zhang,∗,∗∗ Chao Zhou,∗ Zhangming Du,∗,∗∗ Lu Deng,∗∗∗ Zhiqiang Cao,∗ Shuo Wang,∗ Long Cheng,∗ and Sai Deng∗


  1. [1] D. Lixin, F. Arai, and T. Fukuda, Destructive constructions of nanostructures with carbon nanotubes through nanorobotic manipulation, 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 a scanning electron microscope, IEEE/ASME Transactions on Mechatronics, 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 testing of silicon nanowires using electrostatically actuated tensile stages, Journal of Microelectromechanical Systems, 19(3), 2010, 663–674.
  4. [4] J. Wang and S. Guo, Development of a precision parallel micromechanism for nano tele-operation, International Journal of Robotics and Automation, 23(1), 2008, 56–63.
  5. [5] H. Zhang, Z. Wang, H. Yan, F. Yang, and X. Zhou, Adaptive event-triggered transmission scheme and H∞ filtering codesign over a filtering network with switching topology, IEEE Transactions on Cybernetics, 49(12), 2019, 4296–4307.
  6. [6] H. Yan, P. Li, H. Zhang, X. Zhan and F. Yang, Event-triggered distributed fusion estimation of networked multisensor systems with 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, Distributed H∞ filtering for switched repeated scalar nonlinear systems with randomly occurred sensor nonlinearities and asynchronous switching, IEEE Transactions on Systems, Man, and Cybernetics: 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 SEM correlative microscopy for nanomanipulation of subcellular structures, 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 of a linear array of coupled gold nanocubes: Direct visualization of 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 nanomanipulation system 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 method inside scanning electron microscopes, 2016 IEEE International Conf. 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 measurement of 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 correlated local estimates under non-gaussian channel noise, International Journal of Robotics and Automation, 25(2), 2010, 155–161.
  15. [15] S. Saeedi, L. Paull, M. Trentini, and H. Li, Occupancy grid map merging for multiple robot simultaneous localization and mapping, 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 novel autonomous localization technique of subsea in-pipe robot, International Journal of Robotics and Automation, 25(2), 2010, 102–108. 7
  17. [17] M. Perrollaz, R. Labayrade, D. Gruyer, A. Lambert, and D. Aubert, Proposition of generic validation criteria using stereovision for on-road obstacle detection, International Journal of Robotics and Automation, 29(1), 2014, 32–43.
  18. [18] A.T. Alouani and T.R. Rice, On optimal synchronous and asynchronous track fusion, Optical Engineering, 37(2), 1998, 427–433.
  19. [19] X. Lin, Y. Bar-Shalom, and T. Kirubarajan, Multisensor multitarget 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 asynchronous fusion 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 filter with improved state block and neural network for nanometer displacement, 2018 IEEE International Conf. on Mechatronics and Automation (ICMA), Changchun, China, 2018, 539–544.
  22. [22] L.P. Yan, B.S. Liu, and D.H. Zhou, The modeling and estimation of 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 Kalman filter, IEEE Transactions on Automatic Control, 13(6), 1968, 699–702.
  24. [24] L. Yu, S. Wang, and K.K. Lai, An integrated data preparation scheme for neural network data analysis, IEEE Transactions on Knowledge & Data Engineering, 18(2), 2005, 217–230.
  25. [25] X.X. Wu and J.G. Liu, A new early stopping algorithm for improving neural network generalization, International Conf. on Intelligent Computation Technology & Automation, Changsha, Hunan, China, 2009, 15–18.
  26. [26] E. Phaisangittisagul, An analysis of the regularization between L2 and dropout in single hidden layer neural network, International Conf. on Intelligent Systems, Bangkok, Thailand, 2016, 174–179.
  27. [27] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradientbased learning applied to document recognition, Proceedings of the IEEE, 86(11), 1998, 2278–2324.

Important Links:

Go Back