Multivariate filter banks with a polyphase matrix built by matrix factorization （lattice structure） were proposed to obtain orthonormal wavelet basis. On the basis of that, we propose a general method of constructin...Multivariate filter banks with a polyphase matrix built by matrix factorization （lattice structure） were proposed to obtain orthonormal wavelet basis. On the basis of that, we propose a general method of constructing filter banks which ensure second and third accuracy of its corresponding scaling function. In the last part, examples with second and third accuracy are given.展开更多
A novel double coupling planar structure(a single microstrip line coupled with two resonators coincidental)is presented for the first time.Based on this structure,a six-pole parallel-coupled high-temperature supercond...A novel double coupling planar structure(a single microstrip line coupled with two resonators coincidental)is presented for the first time.Based on this structure,a six-pole parallel-coupled high-temperature superconducting bandpass filter with both linear phase response and quasi-elliptic function response is designed.The filter has a 40 MHz passband with a center frequency of 2,000 MHz.Its dimension is18.67 mm 9 25.96 mm.At 77 K,minimum insertion loss is0.26 dB in passband.The group delay variation is\3 ns over70%of the filter bandwidth.展开更多
目的:探讨机器学习模型与逐步线性回归(Stepwise linear regression,SLR)模型在亚急性期脑卒中患者康复后功能结局预测中的价值。方法:选取中国人民解放军联勤保障部队第九四五医院2013年1月~2023年12月收治的亚急性期脑卒中患者1046例...目的:探讨机器学习模型与逐步线性回归(Stepwise linear regression,SLR)模型在亚急性期脑卒中患者康复后功能结局预测中的价值。方法:选取中国人民解放军联勤保障部队第九四五医院2013年1月~2023年12月收治的亚急性期脑卒中患者1046例为研究对象,取患者一般资料以及入院时功能独立性量表(Functional Independence Measure,FIM)评分构建SLR、回归树(Regression trees.RT)、集成学习(Ensemble learning,EL)、人工神经网络(Artificial neural network,ANN)、支持向量回归(Support vector regression,SVR)以及高斯过程回归(Gaussian process regression,GPR)预测模型,并采用10折交叉验证,比较各模型实际与预测出院FIM评分以及FIM增益的决定系数(R^(2))、均方根误差(Root Mean Squared Error,RMSE)。结果:机器学习模型(R^(2):RT=0.75,EL=0.78,ANN=0.81,SVR=0.80,GPR=0.81)在预测FIM运动评分方面优于SLR(0.70)。机器学习模型对FIM增益总分的预测准确性(R^(2):RT=0.48,EL=0.51,ANN=0.50,SVR=0.51,GPR=0.54)也优于SLR(0.22)。结论:机器学习模型在预测FIM预后方面优于SLR:仅包含患者一般信息和入院FIM评分的机器学习模型的预测准确性优于既往研究,同时GPR对FIM预后的预测准确性最高。展开更多
基金Supported by Prof. Y.Xu under his grant in Program of "One Hundred Distinguished Chinese Scientists" of the Chinese Academy of Sciencesthe National Natural Science Foundation of China(No.10371122)Postgraduate Innovation Fund of the Chinese Academ
文摘Multivariate filter banks with a polyphase matrix built by matrix factorization （lattice structure） were proposed to obtain orthonormal wavelet basis. On the basis of that, we propose a general method of constructing filter banks which ensure second and third accuracy of its corresponding scaling function. In the last part, examples with second and third accuracy are given.
文摘A novel double coupling planar structure(a single microstrip line coupled with two resonators coincidental)is presented for the first time.Based on this structure,a six-pole parallel-coupled high-temperature superconducting bandpass filter with both linear phase response and quasi-elliptic function response is designed.The filter has a 40 MHz passband with a center frequency of 2,000 MHz.Its dimension is18.67 mm 9 25.96 mm.At 77 K,minimum insertion loss is0.26 dB in passband.The group delay variation is\3 ns over70%of the filter bandwidth.
