The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms(ECGs)are easily contaminated by physiological artifacts and external noises,and these morph...The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms(ECGs)are easily contaminated by physiological artifacts and external noises,and these morphological characteristics show significant variations for different patients.A fast patient-specific arrhythmia diagnosis classifier scheme is proposed,in which a wavelet adaptive threshold denoising is combined with quantum genetic algorithm(QAG)based on least squares twin support vector machine(LSTSVM).The wavelet adaptive threshold denoising is employed for noise reduction,and then morphological features combined with the timing interval features are extracted to evaluate the classifier.For each patient,an individual and fast classifier will be trained by common and patient-specific training data.Following the recommendations of the Association for the Advancements of Medical Instrumentation(AAMI),experimental results over the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 98.22%,99.65%and 99.41%for the abnormal,ventricular ectopic beats(VEBs)and supra-VEBs(SVEBs),respectively.Besides the detection accuracy,sensitivity and specificity,our proposed method consumes the less CPU running time compared with the other representative state of the art methods.It can be ported to Android based embedded system,henceforth suitable for a wearable device.展开更多
为提高非视距场景下超宽带(ultra‑wideband,UWB)定位精度,本文提出一种基于误差因子的改进加权最小二乘(weighted least square,WLS)算法.该算法利用测距值和实时信道冲激响应特征训练1维卷积神经网络,实现误差因子的准确预测;基于预测...为提高非视距场景下超宽带(ultra‑wideband,UWB)定位精度,本文提出一种基于误差因子的改进加权最小二乘(weighted least square,WLS)算法.该算法利用测距值和实时信道冲激响应特征训练1维卷积神经网络,实现误差因子的准确预测;基于预测得到的误差因子设计改进WLS算法的加权矩阵,赋予不同基站合理的权重,以改善非视距场景下UWB定位性能.通过实测采集静态和动态定位数据对改进WLS算法进行性能验证.实验结果表明:视距场景下,改进WLS算法与最小二乘(least square,LS)算法、WLS算法定位性能相近;非视距场景下,改进WLS算法明显优于LS算法、WLS算法,能够有效抑制非视距误差.展开更多
A method of multiple outputs least squares support vector regression （LS-SVR） was developed and described in detail, with the radial basis function （RBF） as the kernel function. The method was applied to predict t...A method of multiple outputs least squares support vector regression （LS-SVR） was developed and described in detail, with the radial basis function （RBF） as the kernel function. The method was applied to predict the future state of the power-shift steering transmission （PSST）. A prediction model of PSST was gotten with multiple outputs LS-SVR. The model performance was greatly influenced by the penalty parameter γ and kernel parameter σ2 which were optimized using cross validation method. The training and prediction of the model were done with spectrometric oil analysis data. The predictive and actual values were compared and a fault in the second PSST was found. The research proved that this method had good accuracy in PSST fault prediction, and any possible problem in PSST could be found through a comparative analysis.展开更多
The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant ...The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.展开更多
针对传统矿浆细度检测的离线筛分法效率低且不能及时反馈至上层磨矿系统的问题,为开发出细度自动检测技术,提出一种曲面拟合算法,即:基于最小二乘法改进的移动最小截平方法(MLTS-LS,Moving Least Trimmed Square-Least Square)对矿浆细...针对传统矿浆细度检测的离线筛分法效率低且不能及时反馈至上层磨矿系统的问题,为开发出细度自动检测技术,提出一种曲面拟合算法,即:基于最小二乘法改进的移动最小截平方法(MLTS-LS,Moving Least Trimmed Square-Least Square)对矿浆细度数据进行曲面拟合,以达到快速检测矿浆细度的目的。首先,通过细度检测试验采集矿浆细度三维离散数据;其次,计算分析“Nearest”、“Linear”、“Cubic”、“V4”和传统的最小二乘法的曲面拟合评价指标,提出一种改进的插值算法;最后,将“MLTS-LS”算法应用于矿浆细度三维离散数据的拟合。结果显示,“MLTS-LS”算法的和方差值与均方差值明显小于其他算法,且其确定系数值与校正决定系数值均接近于1,表明“MLTS-LS”算法对矿浆细度三维离散数据的拟合效果较好。展开更多
为了提高变压器故障诊断正确率,笔者提出一种基于改进秃鹰(improved bald eagle search,IBES)算法优化最小二乘支持向量机(least squares support vector machine,LSSVM)的变压器故障诊断方法。利用高斯-柯西变异算子对最优秃鹰个体进...为了提高变压器故障诊断正确率,笔者提出一种基于改进秃鹰(improved bald eagle search,IBES)算法优化最小二乘支持向量机(least squares support vector machine,LSSVM)的变压器故障诊断方法。利用高斯-柯西变异算子对最优秃鹰个体进行变异,使IBES算法能够及时局部最优,提高了IBES算法的收敛精度。采用IBES算法对LSSVM的核参数和惩罚参数进行优化,建立基于IBES-LSSVM的变压器故障诊断模型,并与BES-LSSVM、GWO-SVM和GA-BP模型进行仿真实验对比。结果表明,IBES-LSSVM模型的诊断正确率为98.33%,比上述对比模型分别提高了3.50%、7.27%和9.26%,且计算时间最短,验证了该文所提变压器故障诊断方法的正确性和实用性。展开更多
基金Supported by the National Natural Science Foundation of China(61571063)Key Scientific Research Projects of Colleges and Universities in Henan Province(20A510014)Key Scientific and Technological Projects in Henan Province。
文摘The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms(ECGs)are easily contaminated by physiological artifacts and external noises,and these morphological characteristics show significant variations for different patients.A fast patient-specific arrhythmia diagnosis classifier scheme is proposed,in which a wavelet adaptive threshold denoising is combined with quantum genetic algorithm(QAG)based on least squares twin support vector machine(LSTSVM).The wavelet adaptive threshold denoising is employed for noise reduction,and then morphological features combined with the timing interval features are extracted to evaluate the classifier.For each patient,an individual and fast classifier will be trained by common and patient-specific training data.Following the recommendations of the Association for the Advancements of Medical Instrumentation(AAMI),experimental results over the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 98.22%,99.65%and 99.41%for the abnormal,ventricular ectopic beats(VEBs)and supra-VEBs(SVEBs),respectively.Besides the detection accuracy,sensitivity and specificity,our proposed method consumes the less CPU running time compared with the other representative state of the art methods.It can be ported to Android based embedded system,henceforth suitable for a wearable device.
文摘为提高非视距场景下超宽带(ultra‑wideband,UWB)定位精度,本文提出一种基于误差因子的改进加权最小二乘(weighted least square,WLS)算法.该算法利用测距值和实时信道冲激响应特征训练1维卷积神经网络,实现误差因子的准确预测;基于预测得到的误差因子设计改进WLS算法的加权矩阵,赋予不同基站合理的权重,以改善非视距场景下UWB定位性能.通过实测采集静态和动态定位数据对改进WLS算法进行性能验证.实验结果表明:视距场景下,改进WLS算法与最小二乘(least square,LS)算法、WLS算法定位性能相近;非视距场景下,改进WLS算法明显优于LS算法、WLS算法,能够有效抑制非视距误差.
基金Supported by the Ministerial Level Advanced Research Foundation(3031030)the"111"Project(B08043)
文摘A method of multiple outputs least squares support vector regression （LS-SVR） was developed and described in detail, with the radial basis function （RBF） as the kernel function. The method was applied to predict the future state of the power-shift steering transmission （PSST）. A prediction model of PSST was gotten with multiple outputs LS-SVR. The model performance was greatly influenced by the penalty parameter γ and kernel parameter σ2 which were optimized using cross validation method. The training and prediction of the model were done with spectrometric oil analysis data. The predictive and actual values were compared and a fault in the second PSST was found. The research proved that this method had good accuracy in PSST fault prediction, and any possible problem in PSST could be found through a comparative analysis.
基金supported by the 2021 Open Project Fund of Science and Technology on Electromechanical Dynamic Control Laboratory,grant number 212-C-J-F-QT-2022-0020China Postdoctoral Science Foundation,grant number 2021M701713+1 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province,grant number KYCX23_0511the Jiangsu Funding Program for Excellent Postdoctoral Talent,grant number 20220ZB245。
文摘The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.
文摘针对传统矿浆细度检测的离线筛分法效率低且不能及时反馈至上层磨矿系统的问题,为开发出细度自动检测技术,提出一种曲面拟合算法,即:基于最小二乘法改进的移动最小截平方法(MLTS-LS,Moving Least Trimmed Square-Least Square)对矿浆细度数据进行曲面拟合,以达到快速检测矿浆细度的目的。首先,通过细度检测试验采集矿浆细度三维离散数据;其次,计算分析“Nearest”、“Linear”、“Cubic”、“V4”和传统的最小二乘法的曲面拟合评价指标,提出一种改进的插值算法;最后,将“MLTS-LS”算法应用于矿浆细度三维离散数据的拟合。结果显示,“MLTS-LS”算法的和方差值与均方差值明显小于其他算法,且其确定系数值与校正决定系数值均接近于1,表明“MLTS-LS”算法对矿浆细度三维离散数据的拟合效果较好。
文摘为了提高变压器故障诊断正确率,笔者提出一种基于改进秃鹰(improved bald eagle search,IBES)算法优化最小二乘支持向量机(least squares support vector machine,LSSVM)的变压器故障诊断方法。利用高斯-柯西变异算子对最优秃鹰个体进行变异,使IBES算法能够及时局部最优,提高了IBES算法的收敛精度。采用IBES算法对LSSVM的核参数和惩罚参数进行优化,建立基于IBES-LSSVM的变压器故障诊断模型,并与BES-LSSVM、GWO-SVM和GA-BP模型进行仿真实验对比。结果表明,IBES-LSSVM模型的诊断正确率为98.33%,比上述对比模型分别提高了3.50%、7.27%和9.26%,且计算时间最短,验证了该文所提变压器故障诊断方法的正确性和实用性。
