Process regression models,such as Gaussian process regression model(GPR),have been widely applied to analyze kinds of functional data.This paper introduces a composite of two T-process(CT),where the first one captures...Process regression models,such as Gaussian process regression model(GPR),have been widely applied to analyze kinds of functional data.This paper introduces a composite of two T-process(CT),where the first one captures the smooth global trend and the second one models local details.TheCThas an advantage in the local variability compared to general T-process.Furthermore,a composite T-process regression(CTP)model is developed,based on the composite T-process.It inherits many nice properties as GPR,while it is more robust against outliers than GPR.Numerical studies including simulation and real data application show that CTP performs well in prediction.展开更多
The extended t-process regression model is developed to robustly model functional data with outlier functional curves.This paper applies Bayesian estimation to propose an estimation procedure for the model with indepe...The extended t-process regression model is developed to robustly model functional data with outlier functional curves.This paper applies Bayesian estimation to propose an estimation procedure for the model with independent errors.A Monte Carlo EM method is built to estimate parameters involved in the model.Simulation studies and real examples show the proposed method performs well against outliers.展开更多
作为有源相控阵天线阵面的核心部件,收发组件在整个有源天线阵面中的成本占比最高,因此如何降低收发组件的成本是设计天线阵面需要着重考虑的问题。与多芯片收发组件中常用的低温共烧陶瓷(Low Temperature Co-fired Ceramic,LTCC)多层...作为有源相控阵天线阵面的核心部件,收发组件在整个有源天线阵面中的成本占比最高,因此如何降低收发组件的成本是设计天线阵面需要着重考虑的问题。与多芯片收发组件中常用的低温共烧陶瓷(Low Temperature Co-fired Ceramic,LTCC)多层基板相比,多层印制板在成本方面具有很大优势。文中利用混压多层印制板,结合铝合金封装壳体,研制了一款Ku波段四通道低成本收发组件。该收发组件在工作频带内可以实现6位移相和6位幅度衰减,其通道接收增益≥20 dB,通道发射功率≥10 W。文中针对混压多层印制板热膨胀系数高、布线密度低、金丝键合可靠性差等问题,优化了基板叠层方案,研究了高密度互联通孔制作、基板镀层高可靠键合、低成本气密封装等关键工艺技术,有效提升了产品的可靠性,可为低成本混压多层印制板在多芯片组件中的工程化应用提供参考。展开更多
基金supported by National Natural Science Foundation of China(Grant No.11971457)Anhui Provincial Natural Science Foundation(Grant No.1908085MA06).
文摘Process regression models,such as Gaussian process regression model(GPR),have been widely applied to analyze kinds of functional data.This paper introduces a composite of two T-process(CT),where the first one captures the smooth global trend and the second one models local details.TheCThas an advantage in the local variability compared to general T-process.Furthermore,a composite T-process regression(CTP)model is developed,based on the composite T-process.It inherits many nice properties as GPR,while it is more robust against outliers than GPR.Numerical studies including simulation and real data application show that CTP performs well in prediction.
文摘The extended t-process regression model is developed to robustly model functional data with outlier functional curves.This paper applies Bayesian estimation to propose an estimation procedure for the model with independent errors.A Monte Carlo EM method is built to estimate parameters involved in the model.Simulation studies and real examples show the proposed method performs well against outliers.
基金Project(52001281)supported by the National Natural Science Foundation of ChinaProject(192102210012)supported by Key Scientific and Technological Research Projects in Henan Province,ChinaProject(202300410431)supported by Natural Science Foundation of Henan Province,China。
