The nonlinear resonance response of an electrostatically actuated nanobeam is studied over the near-half natural frequency with an axial capacitor controller. A graphene sensor deformed by the vibrations of the nanobe...The nonlinear resonance response of an electrostatically actuated nanobeam is studied over the near-half natural frequency with an axial capacitor controller. A graphene sensor deformed by the vibrations of the nanobeam is used to produce the voltage signal. The voltage of the vibration graphene sensor is used as a control signal input to a closed- loop circuit to mitigate the nonlinear vibration of the nanobeam. An axial control force produced by the axial capacitor controller can transform the frequency-amplitude curves from nonlinear to linear. The necessary and sufficient conditions for guaranteeing the system stability and a saddle-node bifurcation are studied. The numerical simulations are conducted for uniform nanobeams. The nonlinear terms of the vibration system can be transformed into linear ones by applying the critical control voltage to the system. The nonlinear vibration phenomena can be avoided, and the vibration amplitude is mitigated evidently with the axial capacitor controller.展开更多
A novel nonlinear mirror structure which can increase the optical signal-to-noise ratio of a distributed fiber Raman temperature sensor is proposed, and 6 dB improvement of the optical signal-to-noise ratio is obtaine...A novel nonlinear mirror structure which can increase the optical signal-to-noise ratio of a distributed fiber Raman temperature sensor is proposed, and 6 dB improvement of the optical signal-to-noise ratio is obtained. With the assistance of the nonlinear mirror, we demonstrate that the spatial resolution of the sensor is improved from 3 m to 1 m, and the temperature accuracy is improved from ±0.6℃ to ±0.2℃. The theoretical analysis and the experimental data are in good agreement.展开更多
This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchrono...This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.展开更多
In order to solve the problem that the traditional radial basis function (RBF) neural network is easy to fall into local optimal and slow training speed in the data fusion of multi water quality sensors, an optimizati...In order to solve the problem that the traditional radial basis function (RBF) neural network is easy to fall into local optimal and slow training speed in the data fusion of multi water quality sensors, an optimization method of RBF neural network based on improved cuckoo search (ICS) was proposed. The method uses RBF neural network to construct a fusion model for multiple water quality sensor data. RBF network can seek the best compromise between complexity and learning ability, and relatively few parameters need to be set. By using ICS algorithm to find the best network parameters of RBF network, the obtained network model can realize the non-linear mapping between input and output of data sample. The data fusion processing experiment was carried out based on the data released by Zhejiang province surface water quality automatic monitoring data system from March to April 2018. Compared with the traditional BP neural network, the experimental results show that the RBF neural network based on gradient descent (GD) and genetic algorithm (GA), the new method proposed in this paper can effectively fuse the water quality data and obtain higher classification accuracy of water quality.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.51275280 and51575325)
文摘The nonlinear resonance response of an electrostatically actuated nanobeam is studied over the near-half natural frequency with an axial capacitor controller. A graphene sensor deformed by the vibrations of the nanobeam is used to produce the voltage signal. The voltage of the vibration graphene sensor is used as a control signal input to a closed- loop circuit to mitigate the nonlinear vibration of the nanobeam. An axial control force produced by the axial capacitor controller can transform the frequency-amplitude curves from nonlinear to linear. The necessary and sufficient conditions for guaranteeing the system stability and a saddle-node bifurcation are studied. The numerical simulations are conducted for uniform nanobeams. The nonlinear terms of the vibration system can be transformed into linear ones by applying the critical control voltage to the system. The nonlinear vibration phenomena can be avoided, and the vibration amplitude is mitigated evidently with the axial capacitor controller.
基金supported by the National Natural Science Foundation of China under Grant No.60377021partially supported by Program for New Century Excellent Talents in University under Grant No. NCET-07-0152Sichuan Scientific Funds for Young Researchers under Grant No. 08ZQ026-012.
文摘A novel nonlinear mirror structure which can increase the optical signal-to-noise ratio of a distributed fiber Raman temperature sensor is proposed, and 6 dB improvement of the optical signal-to-noise ratio is obtained. With the assistance of the nonlinear mirror, we demonstrate that the spatial resolution of the sensor is improved from 3 m to 1 m, and the temperature accuracy is improved from ±0.6℃ to ±0.2℃. The theoretical analysis and the experimental data are in good agreement.
基金supported by the National Natural Science Foundation of China(6147322711472222)+3 种基金the Fundamental Research Funds for the Central Universities(3102015ZY001)the Aerospace Technology Support Fund of China(2014-HT-XGD)the Natural Science Foundation of Shaanxi Province(2015JM6304)the Aeronautical Science Foundation of China(20151353018)
文摘This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.
文摘In order to solve the problem that the traditional radial basis function (RBF) neural network is easy to fall into local optimal and slow training speed in the data fusion of multi water quality sensors, an optimization method of RBF neural network based on improved cuckoo search (ICS) was proposed. The method uses RBF neural network to construct a fusion model for multiple water quality sensor data. RBF network can seek the best compromise between complexity and learning ability, and relatively few parameters need to be set. By using ICS algorithm to find the best network parameters of RBF network, the obtained network model can realize the non-linear mapping between input and output of data sample. The data fusion processing experiment was carried out based on the data released by Zhejiang province surface water quality automatic monitoring data system from March to April 2018. Compared with the traditional BP neural network, the experimental results show that the RBF neural network based on gradient descent (GD) and genetic algorithm (GA), the new method proposed in this paper can effectively fuse the water quality data and obtain higher classification accuracy of water quality.
