Let P∈C^(n×n)be a Hermitian and{k+1}-potent matrix,i.e.,P^(k+1)=P=P^(*),where(·)^(*)stands for the conjugate transpose of a matrix.A matrix X∈C^(n×n)is called{P,k+1}-reflexive(anti-reflexive)if PXP=X(...Let P∈C^(n×n)be a Hermitian and{k+1}-potent matrix,i.e.,P^(k+1)=P=P^(*),where(·)^(*)stands for the conjugate transpose of a matrix.A matrix X∈C^(n×n)is called{P,k+1}-reflexive(anti-reflexive)if PXP=X(P XP=-X).The system of matrix equations AX=C,XB=D subject to{P,k+1}-reflexive and anti-reflexive constraints are studied by converting into two simpler cases:k=1 and k=2,the least squares solution and the associated optimal approximation problem are also considered.展开更多
In this paper,we investigate the{P,Q,k+1}-reflexive and anti-reflexive solutions to the system of matrix equations AX=C,XB=D and AXB=E.We present the necessary and sufficient conditions for the system men-tioned above...In this paper,we investigate the{P,Q,k+1}-reflexive and anti-reflexive solutions to the system of matrix equations AX=C,XB=D and AXB=E.We present the necessary and sufficient conditions for the system men-tioned above to have the{P,Q,k+1}-reflexive and anti-reflexive solutions.We also obtain the expressions of such solutions to the system by the singular value decomposition.Moreover,we consider the least squares{P,Q,k+1}-reflexive and anti-reflexive solutions to the system.Finally,we give an algorithm to illustrate the results of this paper.展开更多
Let P∈C^(m×m)and Q∈C^(n×n)be Hermitian and{k+1}-potent matrices,i.e.,P k+1=P=P∗,Qk+1=Q=Q∗,where(·)∗stands for the conjugate transpose of a matrix.A matrix X∈C m×n is called{P,Q,k+1}-reflexive(an...Let P∈C^(m×m)and Q∈C^(n×n)be Hermitian and{k+1}-potent matrices,i.e.,P k+1=P=P∗,Qk+1=Q=Q∗,where(·)∗stands for the conjugate transpose of a matrix.A matrix X∈C m×n is called{P,Q,k+1}-reflexive(anti-reflexive)if P XQ=X(P XQ=−X).In this paper,the least squares solution of the matrix equation AXB=C subject to{P,Q,k+1}-reflexive and anti-reflexive constraints are studied by converting into two simpler cases:k=1 and k=2.展开更多
针对耦合神经P系统利用脉冲机制实现区域生长依赖于初始种子点选择的问题,提出一种自适应区域生长耦合神经P系统(adaptive region growing coupled neural P systems,ARGCNP)的图像分割方法。该方法利用金豺优化算法(golden jackal opti...针对耦合神经P系统利用脉冲机制实现区域生长依赖于初始种子点选择的问题,提出一种自适应区域生长耦合神经P系统(adaptive region growing coupled neural P systems,ARGCNP)的图像分割方法。该方法利用金豺优化算法(golden jackal optimization,GJO)的全局搜索能力,通过引入四种策略提升GJO的全局寻优性能,从而在图像中寻找最佳阈值点,以优化区域生长中的种子点选择。在实验中,首先通过CEC2017测试函数对改进后的GJO进行性能测试,结果表明改进后的GJO在测试函数上整体性能第一;随后将ARGCNP应用于分割彩色图像和医学图像,以峰值信噪比等三个指标对分割效果进行量化评价,分割结果显示该方法能够提升分割精度及分割结果的稳定性,证明ARGCNP在应用场景下具有的优势,能够满足图像分割需求。展开更多
目的:分析心电图P波参数对高血压心房颤动(Atrial fibrillation,AF)患者消融术后复发的预测价值。方法:回顾性分析2023年1月至2024年10月我院首次接受消融术的87例高血压AF患者资料,参考术后12 m内是否复发分复发组(21例)和非复发组(66...目的:分析心电图P波参数对高血压心房颤动(Atrial fibrillation,AF)患者消融术后复发的预测价值。方法:回顾性分析2023年1月至2024年10月我院首次接受消融术的87例高血压AF患者资料,参考术后12 m内是否复发分复发组(21例)和非复发组(66例),比较两组P波终末电势(P-wave terminal force in lead V1,PtfV1)、P波离散度(P-wave dispersion,Pd)、P波变异(P-wave variation,Pv)及最大P波时限(Maximum P-wave duration,Pmax),分析上述指标与术后复发的关系及预测价值。结果:与非复发组对比,复发组Pd、Pv、Pmax均更高(P<0.05),但对比两组PtfV1,差异无统计学意义(P>0.05);多因素Logistic回归分析显示,Pd、Pv、Pmax与高血压AF患者术后复发显著相关(P<0.05);绘制受试者工作特征曲线结果显示,Pd、Pv、Pmax三者联合预测高血压AF患者复发的曲线下面积为0.840,均高于单一指标预测(P<0.05)。结论:Pd、Pv、Pmax三者联合在高血压AF患者术后复发中预测价值较高。展开更多
基金Supported by the Education Department Foundation of Hebei Province(QN2015218)Supported by the Natural Science Foundation of Hebei Province(A2015403050)
文摘Let P∈C^(n×n)be a Hermitian and{k+1}-potent matrix,i.e.,P^(k+1)=P=P^(*),where(·)^(*)stands for the conjugate transpose of a matrix.A matrix X∈C^(n×n)is called{P,k+1}-reflexive(anti-reflexive)if PXP=X(P XP=-X).The system of matrix equations AX=C,XB=D subject to{P,k+1}-reflexive and anti-reflexive constraints are studied by converting into two simpler cases:k=1 and k=2,the least squares solution and the associated optimal approximation problem are also considered.
基金supported by the National Natural Science Foundation of China(11571220)
文摘In this paper,we investigate the{P,Q,k+1}-reflexive and anti-reflexive solutions to the system of matrix equations AX=C,XB=D and AXB=E.We present the necessary and sufficient conditions for the system men-tioned above to have the{P,Q,k+1}-reflexive and anti-reflexive solutions.We also obtain the expressions of such solutions to the system by the singular value decomposition.Moreover,we consider the least squares{P,Q,k+1}-reflexive and anti-reflexive solutions to the system.Finally,we give an algorithm to illustrate the results of this paper.
基金Supported by the Education Department Foundation of Hebei Province(Grant No.QN2015218).
文摘Let P∈C^(m×m)and Q∈C^(n×n)be Hermitian and{k+1}-potent matrices,i.e.,P k+1=P=P∗,Qk+1=Q=Q∗,where(·)∗stands for the conjugate transpose of a matrix.A matrix X∈C m×n is called{P,Q,k+1}-reflexive(anti-reflexive)if P XQ=X(P XQ=−X).In this paper,the least squares solution of the matrix equation AXB=C subject to{P,Q,k+1}-reflexive and anti-reflexive constraints are studied by converting into two simpler cases:k=1 and k=2.
文摘针对耦合神经P系统利用脉冲机制实现区域生长依赖于初始种子点选择的问题,提出一种自适应区域生长耦合神经P系统(adaptive region growing coupled neural P systems,ARGCNP)的图像分割方法。该方法利用金豺优化算法(golden jackal optimization,GJO)的全局搜索能力,通过引入四种策略提升GJO的全局寻优性能,从而在图像中寻找最佳阈值点,以优化区域生长中的种子点选择。在实验中,首先通过CEC2017测试函数对改进后的GJO进行性能测试,结果表明改进后的GJO在测试函数上整体性能第一;随后将ARGCNP应用于分割彩色图像和医学图像,以峰值信噪比等三个指标对分割效果进行量化评价,分割结果显示该方法能够提升分割精度及分割结果的稳定性,证明ARGCNP在应用场景下具有的优势,能够满足图像分割需求。
文摘目的:分析心电图P波参数对高血压心房颤动(Atrial fibrillation,AF)患者消融术后复发的预测价值。方法:回顾性分析2023年1月至2024年10月我院首次接受消融术的87例高血压AF患者资料,参考术后12 m内是否复发分复发组(21例)和非复发组(66例),比较两组P波终末电势(P-wave terminal force in lead V1,PtfV1)、P波离散度(P-wave dispersion,Pd)、P波变异(P-wave variation,Pv)及最大P波时限(Maximum P-wave duration,Pmax),分析上述指标与术后复发的关系及预测价值。结果:与非复发组对比,复发组Pd、Pv、Pmax均更高(P<0.05),但对比两组PtfV1,差异无统计学意义(P>0.05);多因素Logistic回归分析显示,Pd、Pv、Pmax与高血压AF患者术后复发显著相关(P<0.05);绘制受试者工作特征曲线结果显示,Pd、Pv、Pmax三者联合预测高血压AF患者复发的曲线下面积为0.840,均高于单一指标预测(P<0.05)。结论:Pd、Pv、Pmax三者联合在高血压AF患者术后复发中预测价值较高。
