This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast de...This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast decoupled solution is adopted.The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements.The robustness and the computational efficiency of the R-Capped-Ll model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.展开更多
A fast algorithm for DOA estimation without eigendecomposition is proposed. Unlike the available propagation method (PM), the proposed method need only use partial cross-correlation of array output data, and hence the...A fast algorithm for DOA estimation without eigendecomposition is proposed. Unlike the available propagation method (PM), the proposed method need only use partial cross-correlation of array output data, and hence the computational complexity is further reduced. Moreover, the proposed method is suitable for the case of spatially nonuniform colored noise. Simulation results show the performance of the proposed method is comparable to those of the existing PM method and the standard MUSIC method.展开更多
In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filt...In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filter is proposed.However,ISO16610-31:Gaussian regression filters(GRF)only provide a single method and a few examples,and does not specify the conditions under which the primary profile can be covered.Moreover,the data presented in the example on robustness in ISO16610-31 do not contain roughness components.In actual roughness measurements,no primary profile exists that does not include a roughness component.Because the characteristics of GRFs are unknown,it is not yet clear which filter should be used for which primary profile,and this is an issue that has been raised at ISO and JIS conferences.In addition,the establishment of filter selection guidelines is necessary at measurement sites.Therefore,this paper clarifies the characteristics of GF-series filters,summarizes the points to be considered when using them,and identifies the filter that should be selected according to different situations.Based on the results,a figure that visualizes the characteristics of filters and a flowchart regarding which filter should be used are created;these tools,to the best of the authors’knowledge,did not exist prior to this study.It is believed that these results will help fulfil the needs of measuring job sites and also aid in filter selection.展开更多
The vanishing point detection technology helps automatic driving. In this paper, the straight lines on the road associated with the vanishing point are extracted efficiently by using the regional division and angle li...The vanishing point detection technology helps automatic driving. In this paper, the straight lines on the road associated with the vanishing point are extracted efficiently by using the regional division and angle limitation. And, the vanishing point is detected robustly by using the fast M-estimation method. Proposed method could detect straight-line features associated with vanishing point detection efficient on the road. And the vanishing point was detected exactly by the effect of the fast M-estimation method when the straight-line features not associated with vanishing point detection were detected. The processing time of the proposed method was faster than the camera flame rate （30 fps）. Thus, the proposed method is capable of real-time processing.展开更多
基金supported in part by the National Key Research and Development Plan of China(No.2018YFB0904200)in part by the National Natural Science Foundation of China(No.51725703).
文摘This paper introduces a robust sparse recovery model for compressing bad data and state estimation(SE),based on a revised multi-stage convex relaxation(R-Capped-L1)model.To improve the calculation efficiency,a fast decoupled solution is adopted.The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements.The robustness and the computational efficiency of the R-Capped-Ll model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.
文摘A fast algorithm for DOA estimation without eigendecomposition is proposed. Unlike the available propagation method (PM), the proposed method need only use partial cross-correlation of array output data, and hence the computational complexity is further reduced. Moreover, the proposed method is suitable for the case of spatially nonuniform colored noise. Simulation results show the performance of the proposed method is comparable to those of the existing PM method and the standard MUSIC method.
基金We would like to thank Editage(www.editage.com)for English language editing.
文摘In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filter is proposed.However,ISO16610-31:Gaussian regression filters(GRF)only provide a single method and a few examples,and does not specify the conditions under which the primary profile can be covered.Moreover,the data presented in the example on robustness in ISO16610-31 do not contain roughness components.In actual roughness measurements,no primary profile exists that does not include a roughness component.Because the characteristics of GRFs are unknown,it is not yet clear which filter should be used for which primary profile,and this is an issue that has been raised at ISO and JIS conferences.In addition,the establishment of filter selection guidelines is necessary at measurement sites.Therefore,this paper clarifies the characteristics of GF-series filters,summarizes the points to be considered when using them,and identifies the filter that should be selected according to different situations.Based on the results,a figure that visualizes the characteristics of filters and a flowchart regarding which filter should be used are created;these tools,to the best of the authors’knowledge,did not exist prior to this study.It is believed that these results will help fulfil the needs of measuring job sites and also aid in filter selection.
文摘The vanishing point detection technology helps automatic driving. In this paper, the straight lines on the road associated with the vanishing point are extracted efficiently by using the regional division and angle limitation. And, the vanishing point is detected robustly by using the fast M-estimation method. Proposed method could detect straight-line features associated with vanishing point detection efficient on the road. And the vanishing point was detected exactly by the effect of the fast M-estimation method when the straight-line features not associated with vanishing point detection were detected. The processing time of the proposed method was faster than the camera flame rate （30 fps）. Thus, the proposed method is capable of real-time processing.
