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- W4210930607 abstract "To significantly reduce the storage space of collected road roughness signals and improve the collection rate, the work investigates the compressive sampling and reconstruction of road roughness signals based on compressive sensing theory. Moreover, to overcome the limitations of the classical signal reconstruction method in the case of unknown sparsity, two sparsity adaptive compressive signal reconstruction methods namely those based on the improved simulated annealing (I-SA) algorithm and the golden section method (GSM) are respectively proposed and compared. Both simulated and measured road roughness signals are used to verify the validity of the novel reconstruction methods and the feasibility of road roughness compression and collection. The research results show that the proposed ISA-SPA method (the sparsity adaptive reconstruction method based on the I-SA) completes the sparsity matching optimization with high reconstruction precision (R2 = 0.9884), but has a high time consumption (about 16.09 s). Moreover, the proposed SA-GSM-SPA method (the sparsity adaptive reconstruction method based on the GSM) has a fast rate of calculation while inheriting the good sparsity estimation result and high reconstruction precision of the ISA-SPA method." @default.
- W4210930607 created "2022-02-09" @default.
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- W4210930607 date "2022-05-01" @default.
- W4210930607 modified "2023-10-18" @default.
- W4210930607 title "Two novel reconstruction methods of sparsity adaptive adjustment for road roughness compressive signal based on I-SA and GSM" @default.
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- W4210930607 doi "https://doi.org/10.1016/j.ymssp.2022.108915" @default.
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