Fe-6.5Si soft magnetic composites(SMCs)with hybrid phosphate-silica insulation coatings have been designed to improve their comprehensive property via chemical coating combining sol-gel method in this work.The microst...Fe-6.5Si soft magnetic composites(SMCs)with hybrid phosphate-silica insulation coatings have been designed to improve their comprehensive property via chemical coating combining sol-gel method in this work.The microstructure and magnetic performance of the Fe-6.5Si SMCs with hybrid phosphate-silica insulation coatings were investigated.The hybrid phosphate-silica coatings with high heat resistance and high withstand pressure,formed on the surface of the Fe-6.5Si ferromagnetic powders,were found stable in the composites.Compared with Fe-6.5Si SMCs coated by single phosphate or single silica,Fe-6.5Si SMCs with hybrid phosphate-silica show much higher permeability and lower core loss.The work provides a new way to optimize the magnetic performance of soft magnetic composites.展开更多
In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ...In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.展开更多
基金Projects(2020GDSYL-20200402008,2018GDASCX-0117)supported by GDAS’Project of Science and Technology Development,ChinaProjects(2015B010136004,2019A1515010886)supported by Science and Technology Planning Project of Guangdong Province of ChinaProject(1920001001392)supported by Key Technology Project of Foshan,China。
文摘Fe-6.5Si soft magnetic composites(SMCs)with hybrid phosphate-silica insulation coatings have been designed to improve their comprehensive property via chemical coating combining sol-gel method in this work.The microstructure and magnetic performance of the Fe-6.5Si SMCs with hybrid phosphate-silica insulation coatings were investigated.The hybrid phosphate-silica coatings with high heat resistance and high withstand pressure,formed on the surface of the Fe-6.5Si ferromagnetic powders,were found stable in the composites.Compared with Fe-6.5Si SMCs coated by single phosphate or single silica,Fe-6.5Si SMCs with hybrid phosphate-silica show much higher permeability and lower core loss.The work provides a new way to optimize the magnetic performance of soft magnetic composites.
基金Project(61101185) supported by the National Natural Science Foundation of ChinaProject(2011AA1221) supported by the National High Technology Research and Development Program of China
文摘In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.
