Search results

For the measurement of large-scale components in aircraft assembly, the evaluation of coordinate transformation parameters between the coordinate frames of individual measurement systems to the assembly frame is an essential task, which is usually completed by registration of the enhanced reference system (ERS) points. This paper aims to propose an analytical method to evaluate the uncertainties of transformation parameters considering both the measurement error and the deployment error of ERS points.

For each measuring station, the measured coordinates of ERS points are first roughly registered to the assembly coordinate system using the singular value decomposition method. Then, a linear transformation model considering the measurement error and deployment error of ERS points is developed, and the analytical solution of transformation parameters’ uncertainties is derived. Moreover, the covariance matrix of each ERS points in the transformation evaluation is calculated based on a new uncertainty ellipsoid model and variance-covariance propagation law.

For the transformation of both single and multiple measuring stations, the derived uncertainties of transformation parameters by the proposed analytical method are identical to that obtained by the state-of-the-art iterative method, but the solution process is simpler, and the computation expenses are much less.

The proposed uncertainty evaluation method would be useful for in-site measurement and optimization of the configuration of ERS points in the design of fixture and large assembly field. It could also be applied to other registration applications with errors on both sides of registration points.

The purpose of this paper is to study the parameters' properties of GM(n, h) model on the basis of multiple transformation and the relationship of GM(n, h) model and other grey models.

Multiple transformation property of parameters is important to construct a grey model. However, there is no research on the property of GM(n, h) model, therefore it is meaningful to study the relationship between GM(n, h) model and other grey models.

The multiple transformation property of parameters of GM(n, h) model is recognized. The parameters' value is dependent on multiple transformation value. The values of simulative and predicative are only dependent to the multiple transformation of the main variable and independent to other variables.

The properties of other grey models could be obtained by analyzing the property of GM(n, h) model.

It is a very useful result for constructing a grey model.

This paper discusses multiple transformation property of GM(n, h) model and the relationship between the GM(n, h) model and other grey models. These grey models are put into a common model and the affections that parameters' multiple transformation caused to the model are studied.

The purpose of this paper is to describe the procedure for near-automation of the most commonly used manual georeferencing technique in a desktop GIS environment for historic aerial photographs strip in library archives.

Most of the archived historic aerial photography consists of series of aerial photographs that overlap to some extent, as the optimal overlap ratio is known as 60 percent by photogrammetric standard. Therefore, conjugate points can be detected for the overlapping area. The first image was georeferenced manually by six-parameter affine transformation using 2013 National Agriculture Imagery Program images as ground truths. Then, conjugate points were detected in the first and second images using Speeded Up Robust Features and Random Sample Consensus. The ground space coordinates of conjugate points were estimated using the first image’s six parameters. Then the second image’s six parameters were calculated using conjugate points’ ground space coordinates and pixel coordinates in the second image. This procedure was repeated until the last image was georeferenced. However, error accumulated as the number of photographs increased. Therefore, another six-parameter affine transformation was implemented using control points in the first, middle, and last images. Finally, the images were warped using open source GIS tools.

The result shows that historic aerial strip collections can be georeferenced with far less time and labor using the technique proposed compared with the traditional manual georeferencing technique in a desktop GIS environment.

The suggested approach will promote the usage of historic aerial photographs for various scientific purposes including land use and land cover change detection, soil erosion pattern recognition, agricultural practices change analysis, environmental improvement assessment, and natural habitat change detection.

Most commonly used georeferencing procedures for historic aerial photographs in academic libraries require significant time and effort for manual measurement of conjugate points in the object images and the ground truth images. By maximizing the automation of georeferencing procedures, the suggested approach will save significant time and effort of library workforce.

With the suggested approach, large numbers of historic aerial photographs can be rapidly georeferenced. This will allow libraries to provide more geospatial data to scientific communities.

This is a unique approach to rapid georeferencing of historic aerial photograph strips.

In aircraft assembly, standard reference points with nominal coordinates are commonly applied for coordinate transformation between multiple measurement stations and the assembly coordinate system. For several reasons in practical application, these points often fail to envelop the key assembly space, which leads to large transformation uncertainty. This paper aims to analyze and further reduce the coordinate transformation uncertainty by introducing a new hybrid reference system (HRS).

Several temporary extension points without known coordinates are added to enhance the tightness between different stations, especially at the weakness area in the network, thus constituting an HRS together with the existing standard reference points. The coordinate transformation model of the HRS-based measurement network is established based on an extend Gauss–Markov model. By using the geometrical differential property and variance-covariance propagation law, the covariance matrixes in the transformation model are calculated, and the analytical solution of the uncertainties of transformation parameters are ultimately derived. The transformation uncertainty of each check points is presented by Helmert error expression.

The proposed analytical solution of transformation uncertainty is verified using the state-of-the-art Monte Carlo simulation method, but the solution process is simpler and the computation expenses are much less.

The HRS with three temporary extension points is practically applied to a tail boom in-site measurement for assembly. The average transformation uncertainty has been reduced by 26 per cent to less than 0.05 mm.

The hybrid coordinate transformation model is proposed for the first time. The HRS method for transformation uncertainty reduction is more economical and practical than increasing the number of standard reference points.

An extension of the Schwarz‐Christoffel transformation is described to formally map polygons which contain curved boundaries. The curved boundaries are divided into small ‘curved elements’ and each element is approximated by a second degree polynomial (higher degree polynomials can also be used). The iterative algorithm of evaluating the unknown constants of the basic S‐C transformation described in a companion paper is applied to the extended S‐C transformation to compute its unknown constants, including the coefficients of the polynomials. Excellent results are achieved as far as accuracy and convergence are concerned. Examples including a practical application, are provided. The mapping of curved polygons is important because they provide a better model of a physical device.

The purpose of this paper is to study chemical reaction and heat and mass transfer effects on steady free convection flow in an inclined porous plate in the presence of MHD and viscous dissipation through the application of scaling group of transformation and numerical method.

The fourth-order Runge-Kutta along with the shooting method is employed in the numerical solution of the governing equations.

The magnetic field parameter, the permeability of porous medium and the viscous dissipation are demonstrated to exert a more significant effect on the flow field and, thus, on the heat transfer from the plate to the fluid.

The problem is relatively original.

The work is aimed at studying the electromagnetic interaction between a homogeneous, isotropic single-negative (SNG) slab and an inhomogeneous, anisotropic double-positive (DPS) slab.

The approach is based on the transformation optics framework, which allows systematic design and modelling of anisotropic, inhomogeneous metamaterials with inherent field-manipulation capabilities.

The paper finds that a transformation-optics-based DPS slab can compensate the inherent opaqueness to the electromagnetic radiation of a SNG slab. Here, “compensation” means that the resulting bi-layer may give rise to zero-reflection for a normally-incident plane wave at a given frequency. Such phenomenon is inherently accompanied by (de)funneling effects for collimated-beam illumination, and it turns out to be quite robust to material losses as well as geometrical and constitutive-parameter truncation.

The results provide further evidence and insight in how SNG-like responses may be emulated (within narrow parametric ranges) by suitably-engineered spatial inhomogeneity and anisotropy in DPS media. Moreover, they also show that resonant transmission phenomena through SNG materials may be engineered via the powerful framework of transformation optics.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

The purpose of this paper is to investigate the achievable improvement in reconstruction accuracy in electrical tomography through the incorporation of physical bound constraints as prior knowledge in the inverse problem solution.

The structure of the nonlinear least squares inverse problem formulation and the importance of prior knowledge are addressed. Several different methods for the incorporation of bound constraints are discussed. The methods are compared by means of reconstructions from simulated and measured data and the computational demands.

The inclusion of bound constraints on the material values in the inverse problem solution results in a considerable improvement of the reconstructions. The occurrence of artefacts and blurring can be reduced. Among the investigated constraint handling methods, the logarithmic parameter reconstruction approach can be implemented with minimal additional computational effort.

The study is performed with discrete two‐phase material distributions as occurring in industrial problems. A further step would be the extension to multiple phases.

The logarithmic transform method is a novel approach for the incorporation of bound constraints in tomography. It outperforms other constraint handling approaches and may be of interest for electrical tomography systems in various applications.

Aims to report a novel method of estimating the geometric manipulation to which present day data hiding is subjected.

Computes the feature points matching measure for the geometric manipulation using shape‐specific points. The feature points matching is realized by simulated annealing algorithm.

Finds that the purposed scheme does not require the original image because reference point information on the original image has been contained in the secret key.

Experiments herein have proved the method's robustness in face of geometric attacks and a new way of designing a better blind secret message decodes has been revealed.