In the non-contact measurement using the linear structured light(LSL),the extraction precision of the light stripe center directly affects the measurement accuracy of the whole detection system.To solve the problem th...In the non-contact measurement using the linear structured light(LSL),the extraction precision of the light stripe center directly affects the measurement accuracy of the whole detection system.To solve the problem that general algorithms cannot accurately extract the center of the light stripe with the uneven width and unstable greyvalue distribution,an adaptive optimization method is proposed.In this method,the stripe region is firstly segmented,and the widths of the laser stripe are calculated by boundary detection.The initial stripe center points are computed by the quadratic weighted grayscale centroid method based on the self-adaptive stripe width.After that,these center points are optimized according to the determined slope threshold.The sub-pixel coordinates of these center points are recalculated.Detailed analysis is also performed in line with the proposed evaluation index of the extraction algorithm.The experimental results show that the mean square error of extracted center points is only 0.1 pixel,meaning that the accuracy of laser stripe center extraction is improved significantly by the method.Furthermore,the method can run effectively at a relatively low computational time cost,and can demonstrate great robustness as well.展开更多
The aim of this article is to present author＇s application of wavelets to predict short-term macroeconomic indicators Proposed to predict short-term time series （in particular for predicting macroeconomic indicators...The aim of this article is to present author＇s application of wavelets to predict short-term macroeconomic indicators Proposed to predict short-term time series （in particular for predicting macroeconomic indicators）, proprietary model is based on wavelet analysis with Haar wavelets, Daubechies wavelets, and adaptive models; they are the trend crawling model and alignment exponential model. Adaptive models have been modified through the introduction of wavelet function and combined into a single forecast model. Obtained from conducted research results, it shows the model an effective instrument to predict the short-term.展开更多
To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating...To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating indicator, which was derived from the convergence condition of EASI, and can be used to evaluate the separation degree of separated signals. Furthermore, a nonlinear monotone increasing function between suitable step sizes and separating indicator is constructed to adaptively adjust step sizes, and forgetting factor is employed to weaken effects of data at the initial stage. Numerical case studies and experimental studies on a test bed with shell structures are provided to validate the efficiency improvement of the proposed method. This study can benefit for vibration & acoustic monitoring and control, and machinery condition monitoring and fault diagnosis.展开更多
基金the National Natural Science Foundation of China(No.51975293)the Aeronautical Science Foundation of China(No.2019ZD052010)。
文摘In the non-contact measurement using the linear structured light(LSL),the extraction precision of the light stripe center directly affects the measurement accuracy of the whole detection system.To solve the problem that general algorithms cannot accurately extract the center of the light stripe with the uneven width and unstable greyvalue distribution,an adaptive optimization method is proposed.In this method,the stripe region is firstly segmented,and the widths of the laser stripe are calculated by boundary detection.The initial stripe center points are computed by the quadratic weighted grayscale centroid method based on the self-adaptive stripe width.After that,these center points are optimized according to the determined slope threshold.The sub-pixel coordinates of these center points are recalculated.Detailed analysis is also performed in line with the proposed evaluation index of the extraction algorithm.The experimental results show that the mean square error of extracted center points is only 0.1 pixel,meaning that the accuracy of laser stripe center extraction is improved significantly by the method.Furthermore,the method can run effectively at a relatively low computational time cost,and can demonstrate great robustness as well.
文摘The aim of this article is to present author＇s application of wavelets to predict short-term macroeconomic indicators Proposed to predict short-term time series （in particular for predicting macroeconomic indicators）, proprietary model is based on wavelet analysis with Haar wavelets, Daubechies wavelets, and adaptive models; they are the trend crawling model and alignment exponential model. Adaptive models have been modified through the introduction of wavelet function and combined into a single forecast model. Obtained from conducted research results, it shows the model an effective instrument to predict the short-term.
基金supported by the National Natural Science Foundation of China(Grant No.51305329)the China Postdoctoral Science Foundation(Grant No.2014T70911)+1 种基金the Doctoral Foundation of Education Ministry of China(Grant No.20130201120040)Basic Research Project of Natural Science in Shaanxi Province(Grant No.2015JQ5183)
文摘To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating indicator, which was derived from the convergence condition of EASI, and can be used to evaluate the separation degree of separated signals. Furthermore, a nonlinear monotone increasing function between suitable step sizes and separating indicator is constructed to adaptively adjust step sizes, and forgetting factor is employed to weaken effects of data at the initial stage. Numerical case studies and experimental studies on a test bed with shell structures are provided to validate the efficiency improvement of the proposed method. This study can benefit for vibration & acoustic monitoring and control, and machinery condition monitoring and fault diagnosis.
