PolyU IR Collection:
http://hdl.handle.net/10397/1844
2015-07-02T20:18:32ZAn improved geometric lunar figure from Chang’E-1 and SELENE laser altimetry
http://hdl.handle.net/10397/7386
Title: An improved geometric lunar figure from Chang’E-1 and SELENE laser altimetry
Authors: Iz, H. Bâki; Shum, C. K.; Chen, Y. Q.; Dai, C. L.
Abstract: This study calibrates the footprint positions of 8.5 million Chang'E-1 and 8.8 million SELENE laser altimetry measurements against accurately known lunar laser reflector locations. The resulting datasets are used to estimate triaxial, biaxial and spherical models of the lunar figure based on the two datasets individually. The equatorial semi-major, minor and polar axes of the Chang'E-1 and SELENE solutions differ by 143 m, 3 m and 49 m respectively. The differences between their geometric centers and the lunar center of mass along the three axes are 186 m, 3 m, and 52 m. The complete laser altimetry datasets from the two missions reveal a lunar figure that is more spherical than previously thought. Furthermore, the Chang'E-1 and SELENE solutions are in better agreement with each other than either is with the ULCN 2005 lunar figure.2011-01-01T00:00:00ZVector regression introduced
http://hdl.handle.net/10397/7363
Title: Vector regression introduced
Authors: Mok, Tik; Iz, H. Bâki
Abstract: This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable) is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables) and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables) also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.2014-01-01T00:00:00ZModeling regional sea level rise using local tide gauge data
http://hdl.handle.net/10397/7362
Title: Modeling regional sea level rise using local tide gauge data
Authors: Iz, H. Bâki; Berry, L.; Koch, M.
Abstract: Currently regional mean sea level trends and variations are inferred from the analysis of several individual local tide gauge data that spanonly a long period of time at a given region. In this study, we propose using a model to merge various tide gauge data, regardless of theirtime span, in a single solution, to estimate parameters representative of regional mean sea level trends. The proposed model can accountfor the geographical correlations among the local tide gauge stations as well as serial correlations, if needed, for individual stations’ data.Such a vigorous regional solution enables statistically optimal uncertainties for estimated and projected trends. The proposed formulationalso unifies all the local reference levels by modeling their offsets from a predefined station’s reference level. To test its effectiveness, theproposed model was used to investigate the regional mean sea level variations for the coastal areas of the Florida Panhandle using 26 localtide gauge stations that span approximately 830 years of monthly averages from the Permanent Service for Mean Sea Level repository. Thenew estimate for the regional trend is 2.14 mm/yr with a ±0.03 mm/yr standard error, which is an order of magnitude improvement overthe most recent mean sea level trend estimates and projections for the Florida region obtained from simple averages of local solutions.2012-01-01T00:00:00ZSteep-slope monitoring : GPS multiple-antenna system at Xiaowan dam
http://hdl.handle.net/10397/7364
Title: Steep-slope monitoring : GPS multiple-antenna system at Xiaowan dam
Authors: He, Xiufeng; Sang, Wengang; Chen, Yong-qi; Ding, Xiaoli
Abstract: Although an effective tool for deformation monitoring ot large structures and high-risk slopes, GPS can also entail a high-cost disadvantage in iarge projects. A remote-controlled monitoring system using an electronic switching device for a multiple antennas watches the steep siopes under construction at the Xiaowan hydropower station in China.2005-01-01T00:00:00Z