Combined Estimation Model Research Articles

Researches on Software Cost Combined Estimation Based on RBF Neural Network and RVM

Under the precondition of relatively adequate historical sample data of obtainable software cost, the thesis makes comprehensive analysis of the advantages and disadvantages of complementary neural network and vector machines, and attempts to study the software cost combined estimation based on RBF neural network and RVM and to build combined estimation model, then applies the entropy evaluation method to identify the weight coefficient of this combined estimation model, and finally it adopts the data from COCOMO database to verify this combined estimation as well as the rationality and scientificity of this model.

Vehicle Re-Identification With Dynamic Time Windows for Vehicle Passage Time Estimation

A simple method for vehicle re-identification to generate vehicle passage times with loop data is developed. The method departs from other existing methods for vehicle passage time estimation: 1) It handles vehicle signatures one at a time and evaluates each vehicle observed only once. 2) The commonly used prespecified time window is replaced by a dynamic list of vehicles to be matched. 3) Vehicle matching is based on a combined estimation model that integrates spot traffic data with spatial vehicle data. The performance of the algorithm was tested with field data. Furthermore, to examine the effect of some of the assumptions on the performance of the algorithm, we compared the result with that obtained from an offline optimization model based on a spatial constraint that considers as many vehicles as possible for matching. The proposed method is particularly suitable for real-time applications since it can be easily implemented with little calibration effort and is computationally efficient.

Combined Estimation of Low-Altitude Radio Refractivity Based on Sea Clutters from Multiple Shipboard Radars

A combined low-altitude radio refractivity estimation model based on sea clutters from multiple shipboard radar systems is introduced. In this estimation model, a new adaptive objective function used to calculate the match between the modeled clutter and observed clutter is employed to utilize the clutter characteristics more appropriately. Taking the evaporation duct height estimation for an example, statistics of the inversion results of the combined estimation model are shown and compared with that of a single radar retrieved results. The validation and discussion of the advantages of this model are presented.

Matching of 3D surfaces and their intensities

3D surface matching would be an ill conditioned problem when the curvature of the object surface is either homogenous or isotropic, e.g. for plane or spherical types of objects. A reliable solution can only be achieved if supplementary information or functional constraints are introduced. In a previous paper, an algorithm for the least squares matching of overlapping 3D surfaces, which were digitized/sampled point by point using a laser scanner device, by the photogrammetric method or other techniques, was proposed [Gruen, A., and Akca, D., 2005. Least squares 3D surface and curve matching. ISPRS Journal of Photogrammetry and Remote Sensing 59 (3), 151–174.]. That method estimates the transformation parameters between two or more fully 3D surfaces, minimizing the Euclidean distances instead of z-differences between the surfaces by least squares. In this paper, an extension to the basic algorithm is given, which can simultaneously match surface geometry and its attribute information, e.g. intensity, colour, temperature, etc. under a combined estimation model. Three experimental results based on terrestrial laser scanner point clouds are presented. The experiments show that the basic algorithm can solve the surface matching problem provided that the object surface has at least the minimal information. If not, the laser scanner derived intensities are used as supplementary information to find a reliable solution. The method derives its mathematical strength from the least squares image matching concept and offers a high level of flexibility for many kinds of 3D surface correspondence problem.