在窄带主动噪声控制(Active noise control,ANC)系统中,参考信号频率失调(Frequency mismatch,FM)和噪声信号非平稳将会使系统性能下降,甚至失效.本文提出一种基于动量最小均方的改进FM补偿算法,通过在代价函数中引入加权累加的平方误差...在窄带主动噪声控制(Active noise control,ANC)系统中,参考信号频率失调(Frequency mismatch,FM)和噪声信号非平稳将会使系统性能下降,甚至失效.本文提出一种基于动量最小均方的改进FM补偿算法,通过在代价函数中引入加权累加的平方误差,提升系统的追踪和收敛能力.并分别与基于滤波-X最小均方(Filtered-X least mean square,FXLMS)、滤波-X递归最小二乘(Filtered-X recursive least square,FXRLS)和变步长滤波-X最小均方(Variable step-size filtered-X least mean square,VSS-FXLMS)算法的主控制系统结合,共同完成系统综合性能的提高.大量仿真分析表明,新的FM补偿算法在非平稳的FM和离散傅里叶系数翻转的条件下仍能保持较高的追踪能力和合理的残余误差.展开更多
Background and aims:Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics.We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangi...Background and aims:Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics.We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography(ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models(MLMs).Methods:Clinical data of consecutive patients undergoing first-ever ERCP for suspected chol-edocholithiasis from 2015 to 2019 were abstracted from a prospectively-maintained database.Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis.MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound(US)imaging only as well as using all available noninvasive imaging(US,computed tomography,and/or magnetic reso-nance cholangiopancreatography).The diagnostic performance of American Society for Gastrointestinal Endoscopy(ASGE)“high-likelihood”criteria was compared to MLMs.Results:We identified 270 patients(mean age 46 years,62.2%female,73.7%Hispanic/Latino,59%with noninvasive imaging positive for choledocholithiasis)with native papilla who underwent ERCP for suspected choledocholithiasis,of whom 230(85.2%)were found to have ERCP-confirmed chol-edocholithiasis.Logistic regression identified choledocholithiasis on noninvasive imaging(odds ratio(OR)¼3.045,P¼0.004)and common bile duct(CBD)diameter on noninvasive imaging(OR¼1.157,P¼0.011)as predictors of ERCP-confirmed choledocholithiasis.Among the various MLMs trained,the random forest-based MLM performed best;sensitivity was 61.4%and 77.3%and specificity was 100%and 75.0%,using US-only and using all available imaging,respectively.ASGE high-likelihood criteria demonstrated sensitivity of 90.9%and specificity of 25.0%;using cut-points achieving this specificity,MLMs achieved sensitivity up to 97.7%.Conclusions:MLMs using age,sex,race/ethnicity,presence of diabetes,fever,body mass index(BMI),total bilirubin,maximum CBD diameter,and choledocholithiasis on pre-ER展开更多
针对原始加入动量项最小均方MLMS(momentum least mean square)算法在低信噪比情况下,容易产生稳态失调,提出一种引入动态因子的改进MLMS算法。该算法采用动态因子来控制步长对瞬时误差信号的敏感性,并且采用当前误差信号e(n)和上一次...针对原始加入动量项最小均方MLMS(momentum least mean square)算法在低信噪比情况下,容易产生稳态失调,提出一种引入动态因子的改进MLMS算法。该算法采用动态因子来控制步长对瞬时误差信号的敏感性,并且采用当前误差信号e(n)和上一次误差信号e(n-1)的自相关估计来调整步长迭代,增强了算法对噪声的抗干扰性,提高了谐波检测的精度。该算法在稳态精度上优于原始算法,MATLAB仿真结果验证了该算法的有效性和可行性。展开更多
According to the exact expression of the maladjustment, an equation for calculating the boundary of step-size in MLMS algorithm is derived and the relationship between the convergence rate and step-size is discussed i...According to the exact expression of the maladjustment, an equation for calculating the boundary of step-size in MLMS algorithm is derived and the relationship between the convergence rate and step-size is discussed in detail. It is shown that the threshold of the step-size is constrained by maladjustment. Three different properties are presented between the LMS and MLMS algorithms based on comparison. It is indicated that MLMS does not differ significantly from LMS when the given maladjustment is small.展开更多
文摘在窄带主动噪声控制(Active noise control,ANC)系统中,参考信号频率失调(Frequency mismatch,FM)和噪声信号非平稳将会使系统性能下降,甚至失效.本文提出一种基于动量最小均方的改进FM补偿算法,通过在代价函数中引入加权累加的平方误差,提升系统的追踪和收敛能力.并分别与基于滤波-X最小均方(Filtered-X least mean square,FXLMS)、滤波-X递归最小二乘(Filtered-X recursive least square,FXRLS)和变步长滤波-X最小均方(Variable step-size filtered-X least mean square,VSS-FXLMS)算法的主控制系统结合,共同完成系统综合性能的提高.大量仿真分析表明,新的FM补偿算法在非平稳的FM和离散傅里叶系数翻转的条件下仍能保持较高的追踪能力和合理的残余误差.
基金J.H.Tabibian was supported in part by the United States National Center for Advancing Translational Sciences grant UL1 TR000135.
文摘Background and aims:Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics.We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography(ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models(MLMs).Methods:Clinical data of consecutive patients undergoing first-ever ERCP for suspected chol-edocholithiasis from 2015 to 2019 were abstracted from a prospectively-maintained database.Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis.MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound(US)imaging only as well as using all available noninvasive imaging(US,computed tomography,and/or magnetic reso-nance cholangiopancreatography).The diagnostic performance of American Society for Gastrointestinal Endoscopy(ASGE)“high-likelihood”criteria was compared to MLMs.Results:We identified 270 patients(mean age 46 years,62.2%female,73.7%Hispanic/Latino,59%with noninvasive imaging positive for choledocholithiasis)with native papilla who underwent ERCP for suspected choledocholithiasis,of whom 230(85.2%)were found to have ERCP-confirmed chol-edocholithiasis.Logistic regression identified choledocholithiasis on noninvasive imaging(odds ratio(OR)¼3.045,P¼0.004)and common bile duct(CBD)diameter on noninvasive imaging(OR¼1.157,P¼0.011)as predictors of ERCP-confirmed choledocholithiasis.Among the various MLMs trained,the random forest-based MLM performed best;sensitivity was 61.4%and 77.3%and specificity was 100%and 75.0%,using US-only and using all available imaging,respectively.ASGE high-likelihood criteria demonstrated sensitivity of 90.9%and specificity of 25.0%;using cut-points achieving this specificity,MLMs achieved sensitivity up to 97.7%.Conclusions:MLMs using age,sex,race/ethnicity,presence of diabetes,fever,body mass index(BMI),total bilirubin,maximum CBD diameter,and choledocholithiasis on pre-ER
文摘针对原始加入动量项最小均方MLMS(momentum least mean square)算法在低信噪比情况下,容易产生稳态失调,提出一种引入动态因子的改进MLMS算法。该算法采用动态因子来控制步长对瞬时误差信号的敏感性,并且采用当前误差信号e(n)和上一次误差信号e(n-1)的自相关估计来调整步长迭代,增强了算法对噪声的抗干扰性,提高了谐波检测的精度。该算法在稳态精度上优于原始算法,MATLAB仿真结果验证了该算法的有效性和可行性。
文摘According to the exact expression of the maladjustment, an equation for calculating the boundary of step-size in MLMS algorithm is derived and the relationship between the convergence rate and step-size is discussed in detail. It is shown that the threshold of the step-size is constrained by maladjustment. Three different properties are presented between the LMS and MLMS algorithms based on comparison. It is indicated that MLMS does not differ significantly from LMS when the given maladjustment is small.
