surface fitting algorithm
Recently Published Documents

SFA (Surface Fitting Algorithm) for continuous displacement is an important method for digital image correlation with antinoise ability and computational efficiency advantages in practical applications. In order to improve the algorithm accuracy and expand its application range, this paper tries to improve the SFA and studies the modified cubic surface fitting algorithm CTSFA (Corrected Three Surface Fitting Algorithm), which is suitable for solving the initial value of continuous displacement. Bilinear interpolation and adjacent interpolation are used to analyze the gray level at any integer-pixel position in the displacement matrix and the weight coefficient is given. The distance-weighted method is used to approximate the true initial displacement value of the continuum, and the algorithm suitable for digital image processing is extended to the continuum displacement solution. The cubic surface expression of the CTSFA programmatic application is solved by the least squares method, and the correlation coefficient of the power basis function is calculated. In the computer simulation of speckle test, the comparison between CTSFA and SFA on the calculation results of linear and nonlinear displacement fields shows that the calculated amount of CTSFA is basically the same as that of SFA, but the calculation accuracy is doubled. The study of analysing the Brazilian splitting test using CTSFA and SFA reveals that CTSFA is better than SFA in observing the development of cracks.

Abstract The advances in manufacturing methods such as Additive manufacturing provide more flexibility in fabrication of complex geometries. Meanwhile, design tools such as aesthetic design and topology optimization algorithms have been implemented in industrial applications mostly due to the provided flexibility to manufacture freeform surfaces. Computational time and efficiency of the developed algorithms for design, manufacturing and inspection are heavily dependent on the geometric complexity of surfaces. In this paper a measure to estimate the geometric complexity is introduced based on the inherent property of a surface which is curvature. A quantitative value for the geometric complexity is defined through normalization and integration of the mean curvatures. Case studies of the implementation of the proposed measure of complexity verifies the ability of the method to predict the convergence of a surface fitting algorithm based on the geometric complexity of the input model.

Active electronically scanned array antenna (AESA antenna) is capable of controlling the radiation pattern by controlling the feeding phase of the radiating elements. It has good performance and plays an important role in radar systems. With the development of AESA antenna towards high-frequency bands and high-density arrays, the structural-electromagnetic-thermal (SET) coupling becomes increasingly significant. It seriously restricts the realization of high performances of AESA antennas. However, the previously reported theoretical multi-field-coupled model for the coupling problem ignores the effect of the deformations of the feed system and array elements on the electrical performance. It only considers the positional deviations of the array elements in the coupling field. As a result, the accuracy of the numerical solution by the theoretical model is reduced. To overcome the above problems, this paper first establishes the field-circuit coupling model by introducing the deformation errors of the feed system into the existing theoretical model. Secondly, this paper proposes a new numerical solution for the multi-field-coupled problem of AESA antennas based on model reconstruction. And the model reconstruction includes the following: the NURBS (nonuniform rational B-spline) surface fitting algorithm that completes the mapping from finite element models to geometric models by the surface equations established by the node information and the local model reconstruction algorithm that determines the local geometric models by the positions and the directions. The NURBS surface fitting algorithm guarantees the accuracy of both the positions and shapes of array elements. The local model reconstruction algorithm ensures the accuracy of the amplitudes and phases of feed connectors. Finally, the numerical solution was applied to the 32-element AESA antenna and the simulations are close to the measurements.

To characterize reservoirs with complex fault blocks or lithology, geophysicists often need to depict the edge of geologic bodies such as small faults. Edge detection is a powerful tool for structural feature identification; however, conventional edge-detection operators that are widely used in image edge detection are not always adequate for seismic data. In fact, most conventional edge-detection methods are effective along a plane. For seismic data, it is more appropriate to detect edge information along the slope of an event. We evaluated a new method for fault detection based on a surface-fitting algorithm. The surface-fitting algorithm was used to find the local slope of a seismic event, and then edge detection is performed along this local fitted plane. For each point in a seismic volume, we defined a small neighborhood in a plane parallel to the local reflector with the help of dip estimation. The data in the neighborhood were then approximated by a bivariate cubic function, called the facet model. Then, the local gradient of the function is calculated and referred to as the facet model attribute. To enhance the robustness of the output attribute and suppress noise, the gradient values were summed over a vertical window and normalized by the energy. To evaluate the performance of our method, we also calculated the dip-guided Sobel attribute and variance attribute. Compared with these three attributes, the result of our method suggested more accurate edge detection, and it showed more detail in fault detection.

This paper proposed target image in a subpixel level matching algorithm for binocular CCD ranging, which is based on the principle of binocular CCD ranging. In the paper, firstly, we introduced the ranging principle of the binocular ranging system and deduced a binocular parallax formula. Secondly, we deduced the algorithm which was named improved cross-correlation matching algorithm and cubic surface fitting algorithm for target images matched, and it could achieve a subpixel level matching for binocular CCD ranging images. Lastly, through experiment we have analyzed and verified the actual CCD ranging images, then analyzed the errors of the experimental results and corrected the formula of calculating system errors. Experimental results showed that the actual measurement accuracy of a target within 3 km was higher than 0.52%, which meet the accuracy requirements of the high precision binocular ranging.