Chunlai Yang
Anhui Polytechnic University
21 Papers
10 Citations
Chunlai Yang is an academic researcher from Anhui Polytechnic University. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 2, co-authored 2 publications.
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Papers
Research and design of underwater flow-induced vibration energy harvester based on Karman vortex street
TL;DR: An underwater flow-induced vibration energy harvesting system based on Karman vortex street was proposed and tested and showed that the output voltage increased as the diameter of bluff body and the water velocity increase.
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PMSM sliding mode control based on novel reaching law and extended state observer
Shuaiguo Li,Hai Wang,Henian Li,Chunlai Yang +3 more
TL;DR: In this article , an extended state observer (ESO) based on the hyperbolic tangent function for the sliding mode controller is designed to realize the real-time tracking of the mechanical angular velocity and load disturbance of the motor, which can weaken the influence of the internal parameter perturbation and external load disturbance.
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Vibration Energy Harvester Based on Bilateral Periodic One-Dimensional Acoustic Black Hole
TL;DR: In this paper , a piezoelectric vibration energy harvester (VEH) integrated with the beam of a bilateral periodic 1D acoustic black hole (ABH) is proposed.
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Research on Vibration Energy Harvester Based on Two-Dimensional Acoustic Black Hole
TL;DR: In this article , a two-dimensional acoustic black hole (ABH) was used for broadband vibration energy harvesting and boosted the harvested power, and the optimal parameters of the ABH were obtained, such as the power index, truncation thickness, cross-sectional length, and round table diameter.
NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm
TL;DR: In this article , a model based on feature fusion and long short-term memory network is proposed to mine the spatial and temporal coupling relationship between input variables for improving the prediction accuracy, and the simulation results show that the prediction error of nitrogen oxides concentration at the inlet of selective catalytic reduction denitrification system based on CNN-LSTM model is 15.15% lower than that of traditional LSTM.
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