Relevant bibliographies by topics / Interpolation transition

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Academic literature on the topic 'Interpolation transition'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Interpolation transition"

1

Liu, Chao, and Hui Wang. "A real-time predictor-modification-evaluation–corrector-modification-evaluation parametric interpolator for numerical control transition curves." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 234, no. 1-2 (2019): 95–107. http://dx.doi.org/10.1177/0954405419856951.

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A real-time parametric interpolator based on a predictor-modification-evaluation–corrector-modification-evaluation algorithm is proposed in this article, which is utilized to efficiently calculate the reference points of transition curves. Meanwhile, the stable calculation is guaranteed by analyzing the convergence condition of the predictor-modification-evaluation–corrector-modification-evaluation algorithm. Under the convergence condition, the proposed parametric interpolator and traditional line interpolators are simultaneously implemented to interpolate a two-dimensional butterfly path, which consists of quintic Bézier transition curves and line segments. Simulation and experiments are carried out, and the results demonstrate that the proposed real-time predictor-modification-evaluation–corrector-modification-evaluation parametric interpolator achieves the highest accuracy and the light stripe on the tool path is further reduced and hardly observed. Compared with other parametric interpolators, the proposed real-time predictor-modification-evaluation–corrector-modification-evaluation parametric interpolator is capable of achieving a good balance between interpolation accuracy and interpolation efficiency.
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2

Lee, Byung Ho, Soon Woo Park, Soojin Jo, and Moon Ki Kim. "Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates." PLOS ONE 16, no. 11 (2021): e0258818. http://dx.doi.org/10.1371/journal.pone.0258818.

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Large-scale conformational changes are essential for proteins to function properly. Given that these transition events rarely occur, however, it is challenging to comprehend their underlying mechanisms through experimental and theoretical approaches. In this study, we propose a new computational methodology called internal coordinate normal mode-guided elastic network interpolation (ICONGENI) to predict conformational transition pathways in proteins. Its basic approach is to sample intermediate conformations by interpolating the interatomic distance between two end-point conformations with the degrees of freedom constrained by the low-frequency dynamics afforded by normal mode analysis in internal coordinates. For validation of ICONGENI, it is applied to proteins that undergo open-closed transitions, and the simulation results (i.e., simulated transition pathways) are compared with those of another technique, to demonstrate that ICONGENI can explore highly reliable pathways in terms of thermal and chemical stability. Furthermore, we generate an ensemble of transition pathways through ICONGENI and investigate the possibility of using this method to reveal the transition mechanisms even when there are unknown metastable states on rough energy landscapes.
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3

Luo, Jun, and Ying Chen. "Colour Restoration of Image Obtained from CCD Sensor Directly." Cybernetics and Information Technologies 14, no. 2 (2014): 81–91. http://dx.doi.org/10.2478/cait-2014-0021.

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Abstract The original image data obtained from Charge-coupled Device (CCD) can be called original data, which is lack of color information. In order to restore the color of original image, firstly, we design a Bayer color filter array, and then we use bilinear interpolation algorithm and smooth hue transition interpolation algorithm to restore the color of original image. However, the hues of adjacent pixels change abruptly by the bilinear interpolation, therefore, we use smooth hue transition interpolation to enhance the edge of original image, and finally we identify the ultimate performance of these interpolation algorithms.
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4

Zemlyakova, Irina A. "Interpolation of an Incomplete Market by a Complete One for a Trinomial Model in the Quantile Hedging Problem." UNIVERSITY NEWS. NORTH-CAUCASIAN REGION. NATURAL SCIENCES SERIES, no. 4-1 (216-1) (December 28, 2022): 21–28. http://dx.doi.org/10.18522/1026-2237-2022-4-1-21-28.

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The transition from an incomplete market to a complete market is considered using Haar interpolating filtering. With Haar interpolation, in the case of a transition from the current level of the tree to the next, only one atom is split into two parts, while the remaining atoms do not change. In the course of this interpolation, a complete market arises, for which the martingale measure is already unique. The question arises related to the choice of a single martingale measure for the resulting complete market. As this measure, one of the extreme martingale measures is chosen, the distance from which to the market measure will be the smallest. The Kullback-Leibler distance is chosen as a tool for determining the measure of distance of the extreme martingale measure from the market one. A solution to the quantile hedging problem is constructed for the full market obtained in the course of interpolation. This solution is based on the duality theory of linear programming. The study of the problem of quantile hedging for the trinomial model continues. A computational experiment is proposed, during which there is a transition from an incomplete market to a complete one and the problem of quantile hedging is solved for the resulting complete market. The conclusion about the possibility of reducing the trinomial model to the binomial one is obtained.
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5

Earshia V., Diana, and Sumathi M. "Interpolation of Low-Resolution Images for Improved Accuracy Using an ANN Quadratic Interpolator." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (2023): 135–40. http://dx.doi.org/10.17762/ijritcc.v11i4s.6319.

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The era of digital imaging has transitioned into a new one. Conversion to real-time, high-resolution images is considered vital. Interpolation is employed in order to increase the number of pixels per image, thereby enhancing spatial resolution. Interpolation's real advantage is that it can be deployed on user end devices. Despite raising the number of pixels per inch to enhances the spatial resolution, it may not improve the image's clarity, hence diminishing its quality. This strategy is designed to increase image quality by enhancing image sharpness and spatial resolution simultaneously. Proposed is an Artificial Neural Network (ANN) Quadratic Interpolator for interpolating 3-D images. This method applies Lagrange interpolating polynomial and Lagrange interpolating basis function to the parameter space using a deep neural network. The degree of the polynomial is determined by the frequency of gradient orientation events within the region of interest. By manipulating interpolation coefficients, images can be upscaled and enhanced. By mapping between low- and high-resolution images, the ANN quadratic interpolator optimizes the loss function. ANN Quadratic interpolator does a good work of reducing the amount of image artefacts that occur during the process of interpolation. The weights of the proposed ANN Quadratic interpolator are seeded by transfer learning, and the layers are trained, validated, and evaluated using a standard dataset. The proposed method outperforms a variety of cutting-edge picture interpolation algorithms..
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6

Imanian, Hanifeh, Hamidreza Shirkhani, Abdolmajid Mohammadian, Juan Hiedra Hiedra Cobo, and Pierre Payeur. "Spatial Interpolation of Soil Temperature and Water Content in the Land-Water Interface Using Artificial Intelligence." Water 15, no. 3 (2023): 473. http://dx.doi.org/10.3390/w15030473.

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The distributed measured data in large regions and remote locations, along with a need to estimate climatic data for point sites where no data have been recorded, has encouraged the implementation of spatial interpolation techniques. Recently, the increasing use of artificial intelligence has become a promising alternative to conventional deterministic algorithms for spatial interpolation. The present study aims to evaluate some machine learning-based algorithms against conventional strategies for interpolating soil temperature data from a region in southeast Canada with an area of 1000 km by 550 km. The radial basis function neural networks (RBFN) and the deep learning approach were used to estimate soil temperature along a railroad after the spline deterministic spatial interpolation method failed to interpolate gridded soil temperature data on the desired locations. The spline method showed weaknesses in interpolating soil temperature data in areas with sudden changes. This limitation did not improve even by increasing the spline nonlinearity. Although both radial basis function neural networks and the deep learning approach had successful performances in interpolating soil temperature data even in sharp transition areas, deep learning outperformed the former method with a normalized RMSE of 9.0% against 16.2% and an R-squared of 89.2% against 53.8%. This finding was confirmed in the same investigation on soil water content.
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7

Liu, Xiao-Ming, Jun Jiang, Ling Hong, Zigang Li, and Dafeng Tang. "Fuzzy Noise-Induced Codimension-Two Bifurcations Captured by Fuzzy Generalized Cell Mapping with Adaptive Interpolation." International Journal of Bifurcation and Chaos 29, no. 11 (2019): 1950151. http://dx.doi.org/10.1142/s0218127419501517.

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In this paper, the Fuzzy Generalized Cell Mapping (FGCM) method is developed with the help of the Adaptive Interpolation (AI) in the space of fuzzy parameters. The adaptive interpolation on the set-valued fuzzy parameter is introduced in computing the one-step transition membership matrix to enhance the efficiency of the FGCM. For each of initial points in the state space, a coarse database is constructed at first, and then interpolation nodes are inserted into the database iteratively each time errors are examined with the explicit formula of interpolation error until the maximal errors are just under the error bound. With such an adaptively expanded database on hand, interpolating calculations assure the required accuracy with maximum efficiency gains. The new method is termed as Fuzzy Generalized Cell Mapping with Adaptive Interpolation (FGCM with AI), and is used to investigate codimension-two bifurcations in two-dimensional and three-dimensional nonlinear dynamical systems with fuzzy noise. It is found that global changes in fuzzy dynamics are dominated by the underlying deterministic counterparts, and the fuzzy attractor expands along the unstable manifold leading to a collision with a saddle when a bifurcation occurs. The examples show that the FGCM with AI has a thirtyfold to fiftyfold efficiency over the traditional FGCM to achieve the same analyzing accuracy.
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8

Fudge, T. J., E. D. Waddington, H. Conway, J. M. D. Lundin, and K. Taylor. "Interpolation methods for Antarctic ice-core timescales: application to Byrd, Siple Dome and Law Dome ice cores." Climate of the Past 10, no. 3 (2014): 1195–209. http://dx.doi.org/10.5194/cp-10-1195-2014.

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Abstract. Antarctic ice cores have often been dated by matching distinctive features of atmospheric methane to those detected in annually dated ice cores from Greenland. Establishing the timescale between these tie-point ages requires interpolation. While the uncertainty at tie points is relatively well described, uncertainty of the interpolation is not. Here we assess the accuracy of three interpolation schemes using data from the WAIS Divide ice core in West Antarctica; we compare the interpolation methods with the annually resolved timescale for the past 30 kyr. Linear interpolation yields large age errors (up to 380 years) between tie points, abrupt changes in duration of climate events at tie points, and an age bias. Interpolations based on the smoothest accumulation rate (ACCUM) or the smoothest annual-layer thickness (ALT) yield timescales that more closely agree with the annually resolved timescale and do not have abrupt changes in duration at tie points. We use ALT to assess the uncertainty in existing timescales for the past 30 kyr from Byrd, Siple Dome, and Law Dome. These ice-core timescales were developed with methods similar to linear interpolation. Maximum age differences exceed 1000 years for Byrd and Siple Dome, and 500 years for Law Dome. For the glacial–interglacial transition (21 to 12 kyr), the existing timescales are, on average, older than ALT by 40 years for Byrd, 240 years for Siple Dome, and 150 years for Law Dome. Because interpolation uncertainty is often not considered, age uncertainties for ice-core records are often underestimated.
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9

Liu, Xiao-Ming, Jun Jiang, Ling Hong, and Dafeng Tang. "Studying the Global Bifurcation Involving Wada Boundary Metamorphosis by a Method of Generalized Cell Mapping with Sampling-Adaptive Interpolation." International Journal of Bifurcation and Chaos 28, no. 02 (2018): 1830003. http://dx.doi.org/10.1142/s0218127418300033.

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In this paper, a new method of Generalized Cell Mapping with Sampling-Adaptive Interpolation (GCMSAI) is presented in order to enhance the efficiency of the computation of one-step probability transition matrix of the Generalized Cell Mapping method (GCM). Integrations with one mapping step are replaced by sampling-adaptive interpolations of third order. An explicit formula of interpolation error is derived for a sampling-adaptive control to switch on integrations for the accuracy of computations with GCMSAI. By applying the proposed method to a two-dimensional forced damped pendulum system, global bifurcations are investigated with observations of boundary metamorphoses including full to partial and partial to partial as well as the birth of fully Wada boundary. Moreover GCMSAI requires a computational time of one thirtieth up to one fiftieth compared to that of the previous GCM.
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10

Huang, Jian, Ai Ping Song, Jian Ming Tao, and Dan Ping Yi. "Method of Corner Smooth Transition Based on Interpolation Spline." Advanced Materials Research 655-657 (January 2013): 1260–66. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.1260.

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Adjustable form cubic interpolation spline curve, changing its form adjustment coefficient can adjust the curvature radius and shape. Using this spline curve can realize the high-speed smooth connecting between adjacent processing trajectory during NC movement. To internal outline corner transition, use cambered spline transfer curve; To external outline corner transition, can use cambered or toroidal spline transfer curve. During the corner transition process, keep the speed constant, adjust the curvature the curvature radius to control the value of acceleration. Corner smooth transition based on the interpolation spline, can effectively reduce the mutation of acceleration, improve processing efficiency, and weaken the impact to the machine tool.
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