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- 저자 : Shunming Li (검색결과 2 건)
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Yong Wang,Shunming Li,Chun Cheng,Yuqing Su 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.7
We propose the quasi-zero-stiffness (QZS) vibration isolator as seat suspension to improve vehicle vibration isolation performance. The QZS vibration isolator is composed of vertical spring and two symmetric negative stiffness structures used as stiffness correctors. A vehicle-seat-human coupled model considering the QZS vibration isolator is established as a three degree-of-freedom (DOF) model; it is composed of a quarter car model and a simplified 1 DOF model combined vehicle seat and human body. This model considers the changing mass of the passengers and sets the total mass of the vehicle seat and human body as an uncertain parameter, which investigates the overload and unload conditions in practical engineering. To further improve the vehicle ride comfort, a constrained adaptive backstepping controller law based on the barrier Lyapunov function (BLF) is presented. The dynamic characteristic of the active vehicle-seathuman coupled model under shock excitation was analyzed using numerical method. The results show that the designed controller law can isolate the shock excitation transmitted from the road to the passengers effectively, and both the vehicle and seat suspension strokes remain in the allowed stroke range.
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Adaptive Separation Model for Electromagnetic Pulse Coupling Signals of Engine Digital Controllers
Chen Kai,Wei Minxiang,Cao Jie,Chen Xinda,Li Shunming 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.4
The engine digital controller is vulnerable to attacks of wide-spectrum and uncertain electromagnetic pulses (EMPs). To address this issue, herein, we propose a novel “internal cluster evaluation cluster” clustering integrated signal separation method and establish an adaptive separation model for EMP coupling signals of engine digital controllers. Specifi cally, the proposed method mainly includes generation of base clusterings to form a candidate pool of base clusterings, evaluation of the uncertainty of clusters, and establishing an integration strategy. To verify the model, we adopt two types of EMP coupling signal data for simulation and actual measurement. The proposed method is compared with fi ve typical clustering ensemble methods in terms of their normalized mutual information (NMI) values. For the simulated EMP coupled signal data, the hyperparameter is 0.4, and the NMI values of fi ve sets of data are 0.721, 0.576, 0.329, 0.201, and 0.466. For the measured data, the hyperparameter is 0.8, and the NMI values of fi ve sets of data are 0.738, 0.555, 0.377, 0.511, and 0.485. The NMI values of the two types of EMP coupling signal data of the proposed method are generally better than those of the other classical methods. The results confi rm the superior separation eff ect of the proposed method on EMP coupled signals.
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