| contributor author | Md Rashedul Islam | |
| contributor author | David Z. Zhu | |
| date accessioned | 2017-05-08T21:51:45Z | |
| date available | 2017-05-08T21:51:45Z | |
| date copyright | July 2013 | |
| date issued | 2013 | |
| identifier other | %28asce%29hy%2E1943-7900%2E0000763.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/64600 | |
| description abstract | Acoustic doppler velocimeter (ADV) data can be contaminated by spikes from various sources. Available despiking methods were found to encounter difficulties in despiking ADV data from a turbulent jet flow. An iteration-free despiking algorithm was developed for highly contaminated ADV data by applying a bivariate kernel density function and its gradient to separate the data cluster from the spike clusters. It is shown that the new method overcomes some of the deficiencies of the existing despiking methods. | |
| publisher | American Society of Civil Engineers | |
| title | Kernel Density–Based Algorithm for Despiking ADV Data | |
| type | Journal Paper | |
| journal volume | 139 | |
| journal issue | 7 | |
| journal title | Journal of Hydraulic Engineering | |
| identifier doi | 10.1061/(ASCE)HY.1943-7900.0000734 | |
| tree | Journal of Hydraulic Engineering:;2013:;Volume ( 139 ):;issue: 007 | |
| contenttype | Fulltext | |