USING A LEAST SQUARES SUPPORT VECTOR MACHINE TO ESTIMATE A LOCAL GEOMETRIC GEOID MODEL

In this study, test-region global positioning system (GPS) control points exhibitingknown first-order orthometric heights were employed to obtain the points of planecoordinates and ellipsoidal heights by using the real-time GPS kinematicmeasurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on dataintegrity and localization, changing reference-point quantities and distributions toobtain an optimal solution. Furthermore, the LS-SVM was combined with localgeoidal-undulation models that were established by researching and analyzing3kernel functions. The results indicated that the overall precision of the localgeometric geoidal-undulation values calculated using the radial basis function(RBF) and third-order polynomial kernel function was optimal and the root meansquare error (RMSE) was approximately ± 1.5 cm. These findings demonstrated thatthe LS-SVM provides a rapid and practical method for determining orthometricheights and should serve as a valuable academic reference regarding local geoidmodels

USING PARTICLE SWARM OPTIMIZATION TO ESTABLISH A LOCAL GEOMETRIC GEOID MODEL

There exist a number of methods for approximating the local geoid surface and studies carried out to determine a local geoid. In this study, performance of geoid by PSO method in modeling local geoid was presented and analyzed. The ellipsoidal heights (h), derived from GPS observations, and known orthometric heights from first-order bench marks were first used to create local geometric geoid model, then the PSO method was used to convert ellipsoidal heights into orthometric heights (H). The resulting values were used to compare between the spirit leveling and GPS methods. The adopted PSO method can improve the fitting of local geometric geoid by quadratic surface fitting method, which agrees with the known orthometric heights within ±1.02cmthe Cartography produced: General Map, Partial Maps, Profile, Cross Sections and others

A study of the systematic errors inherent in a high precision geodetic network incorporating GPS, gravimetric, and astronomic observations

The reduction of terrestrial observations to a chosen reference ellipsoid, a good distribution of astro-geodetic (deflection and Laplace) stations, a good calibration of distance measuring instruments, an adequate modeling of atmospheric refraction, an insignificant personal equation in astronomical observations, and an accurate geoidal undulation map are all needed to insure a high quality geodetic control network. These conditions are not always fulfilled in some of the countries of the world. In these countries, the established local geodetic datum and primary geodetic networks may lack the detailed information of geoid undulations and deflections of the vertical due to the low density and often inferior accuracy of astro-geodetic stations. As a result of these deficiencies, of occasional instrument calibration problems, and of other error sources, many high precision geodetic networks suffer from shape and scale distortions which may accumulate into significant systematic errors. Failure to account for these will lead not only to incorrectly adjusted geodetic coordinates, but also to over-optimistic estimates of a posteriori accuracies of the local high precision geodetic networks. The influence of the systematic errors considered in this study are on the scale and on the orientation of the network. The resulting network deformations will cause inconsistencies with newer high precision GPS observations. These inconsistencies can also appear to be network systematic errors and will be referred to as such hereafter. A few examples of the analysis of a high precision geodetic network are used as illustration. The first order triangulation network of Taiwan Geodetic Datum of 1980 (TGD80) is the source of the data used to investigate these systematic errors. For addressing these problems, a comprehensive description of procedures is given to determine and partially correct the systematic errors. By combining satellite (TRANSIT and GPS), gravimetric and astro-geodetic observations, errors of scale and orientation can be detected, and to some extent, corrected within the local high precision geodetic network. The proposed approach seeks to detect and, where possible, reduce the significant systematic errors before dealing with the network least squares adjustment. Network distortions remaining after adjustment are also investigated using independently derived GPS station positions as a reference standard. Errors can be detected and controlled more efficiently and better with GPS in the near future. This could enhance the utility of primary high precision geodetic networks which may otherwise be degraded by the presence of significant systematic errors

METHOD OF VIRTUAL REALITY DATA GUIDING SYSTEM

本發明係一種虛擬實境資料指示方法，使用者於輸入所欲查詢之資料的關鍵字並進行查詢後，可獲得一搜尋結果列表，該列表的每一筆結果對應一個虛擬實境碼，該虛擬實境碼紀錄所查詢之物品、文件、資料或書籍的實際擺放位置，使用者可以選擇顯示一虛擬實際場景，將查詢之物品、文件、資料或書籍標示於虛擬實境場景中

Using a least squares support vector machine to estimate a local geometric geoid model

In this study, test-region global positioning system (GPS) control points exhibiting known first-order orthometric heights were employed to obtain the points of plane coordinates and ellipsoidal heights by using the real-time GPS kinematic measurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on data integrity and localization, changing reference-point quantities and distributions to obtain an optimal solution. Furthermore, the LS-SVM was combined with local geoidal-undulation models that were established by researching and analyzing3 kernel functions. The results indicated that the overall precision of the local geometric geoidal-undulation values calculated using the radial basis function (RBF) and third-order polynomial kernel function was optimal and the root mean square error (RMSE) was approximately ± 1.5 cm. These findings demonstrated that the LS-SVM provides a rapid and practical method for determining orthometric heights and should serve as a valuable academic reference regarding local geoid models

USING PARTICLE SWARM OPTIMIZATION TO ESTABLISH A LOCAL GEOMETRIC GEOID MODEL

There exist a number of methods for approximating the local geoid surface and studies carried out to determine a local geoid. In this study, performance of geoid by PSO method in modeling local geoid was presented and analyzed. The ellipsoidal heights (h), derived from GPS observations, and known orthometric heights from first-order benchmarks were first used to create local geometric geoid model, then the PSO method was used to convert ellipsoidal heights into orthometric heights (H). The resulting values were used to compare between the spirit leveling and GPS methods. The adopted PSO method can improve the fitting of local geometric geoid by quadratic surface fitting method, which agrees with the known orthometric heights within ±1.02c

Using a least squares support vector machine to estimate a local geometric geoid model

In this study, test-region global positioning system (GPS) control points exhibiting known first-order orthometric heights were employed to obtain the points of plane coordinates and ellipsoidal heights by using the real-time GPS kinematic measurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on data integrity and localization, changing reference-point quantities and distributions to obtain an optimal solution. Furthermore, the LS-SVM was combined with local geoidal-undulation models that were established by researching and analyzing3 kernel functions. The results indicated that the overall precision of the local geometric geoidal-undulation values calculated using the radial basis function (RBF) and third-order polynomial kernel function was optimal and the root mean square error (RMSE) was approximately ± 1.5 cm. These findings demonstrated that the LS-SVM provides a rapid and practical method for determining orthometric heights and should serve as a valuable academic reference regarding local geoid models