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Journal articles on the topic 'Inverse distance weighted'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Tugrul, Bulent, and Huseyin Polat. "Privacy-Preserving Inverse Distance Weighted Interpolation." Arabian Journal for Science and Engineering 39, no. 4 (November 10, 2013): 2773–81. http://dx.doi.org/10.1007/s13369-013-0887-4.

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2

Mueller, T. G., S. R. K. Dhanikonda, N. B. Pusuluri, A. D. Karathanasis, K. K. Mathias, B. Mijatovic, and B. G. Sears. "OPTIMIZING INVERSE DISTANCE WEIGHTED INTERPOLATION WITH CROSS-VALIDATION." Soil Science 170, no. 7 (July 2005): 504–15. http://dx.doi.org/10.1097/01.ss.0000175342.30164.89.

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3

Dong Wang, Huchuan Lu, Ziyang Xiao, and Ming-Hsuan Yang. "Inverse Sparse Tracker With a Locally Weighted Distance Metric." IEEE Transactions on Image Processing 24, no. 9 (September 2015): 2646–57. http://dx.doi.org/10.1109/tip.2015.2427518.

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4

Achilleos, Georgios. "Errors within the Inverse Distance Weighted (IDW) interpolation procedure." Geocarto International 23, no. 6 (October 13, 2008): 429–49. http://dx.doi.org/10.1080/10106040801966704.

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5

He, Yong, Binwu Zhang, and Enyu Yao. "Weighted Inverse Minimum Spanning Tree Problems Under Hamming Distance." Journal of Combinatorial Optimization 9, no. 1 (February 2005): 91–100. http://dx.doi.org/10.1007/s10878-005-5486-1.

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6

Liu, Longcheng, and Jianzhong Zhang. "Inverse maximum flow problems under the weighted Hamming distance." Journal of Combinatorial Optimization 12, no. 4 (September 20, 2006): 395–408. http://dx.doi.org/10.1007/s10878-006-9006-8.

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7

Taskinen, Antti, Hannu Sirviö, and Bertel Vehviläinen. "Interpolation of Daily Temperature in Finland." Hydrology Research 34, no. 5 (October 1, 2003): 413–26. http://dx.doi.org/10.2166/nh.2003.0015.

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The present approach for daily temperature interpolation of the Watershed Simulation and Forecasting System of the Finnish Environment Institute is based on the inverse distance weighted interpolation. In order to improve the calculation, three alternative methods were tested: 1) modified inverse distance weighted model, 2) regression with dummy variables for taking into account time and 3) regression equation calibrated for each day. The regression model calibrated for each day proved to be the most promising model. By average, the difference between the accuracy of it and the inverse distance weighted methods wasn't big but some indication was found that in single cases it can make a difference. The estimated parameters were found to have realistic physical meanings.

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8

Bapat, R. B., and S. Sivasubramanian. "Product distance matrix of a graph and squared distance matrix of a tree." Applicable Analysis and Discrete Mathematics 7, no. 2 (2013): 285–301. http://dx.doi.org/10.2298/aadm130415006b.

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Let G be a strongly connected, weighted directed graph. We define a product distance ?(i,j) for pairs i,j of vertices and form the corresponding product distance matrix. We obtain a formula for the determinant and the inverse of the product distance matrix. The edge orientation matrix of a directed tree is defined and a formula for its determinant and its inverse, when it exists, is obtained. A formula for the determinant of the (entry-wise) squared distance matrix of a tree is proved.

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9

Mueller, T. G., N. B. Pusuluri, K. K. Mathias, P. L. Cornelius, R. I. Barnhisel, and S. A. Shearer. "Map Quality for Ordinary Kriging and Inverse Distance Weighted Interpolation." Soil Science Society of America Journal 68, no. 6 (November 2004): 2042–47. http://dx.doi.org/10.2136/sssaj2004.2042.

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10

Guan, Xiucui, and Binwu Zhang. "Inverse 1-median problem on trees under weighted Hamming distance." Journal of Global Optimization 54, no. 1 (June 23, 2011): 75–82. http://dx.doi.org/10.1007/s10898-011-9742-x.

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11

Liu, Longcheng, and Enyu Yao. "Weighted inverse maximum perfect matching problems under the Hamming distance." Journal of Global Optimization 55, no. 3 (April 4, 2012): 549–57. http://dx.doi.org/10.1007/s10898-012-9901-8.

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12

Zhou, Hui, Qi Ding, and Ruiling Jia. "Inverse of the distance matrix of a weighted cactoid digraph." Applied Mathematics and Computation 362 (December 2019): 124552. http://dx.doi.org/10.1016/j.amc.2019.06.066.

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13

Jiang, Yiwei, Longcheng Liu, Biao Wu, and Enyu Yao. "Inverse minimum cost flow problems under the weighted Hamming distance." European Journal of Operational Research 207, no. 1 (November 2010): 50–54. http://dx.doi.org/10.1016/j.ejor.2010.03.029.

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14

Jiang, Yong, Weifeng Lin, Longcheng Liu, and Anzhen Peng. "Constrained inverse minimum flow problems under the weighted Hamming distance." Theoretical Computer Science 883 (September 2021): 59–68. http://dx.doi.org/10.1016/j.tcs.2021.06.006.

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15

Yang, Fan, and Wei Zhu Yang. "Data Interpolation Methods by Inverse Distance Weighted Average Method between Multidisciplines." Advanced Materials Research 1044-1045 (October 2014): 620–23. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.620.

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In this article, to handle data exchange between different grid systems efficiently and accuracy, the accuracy of inverse distance weighted average method is researched by different searching radius and exponent parameter. The result is compared with other two interpolation methods, radial basis function interpolation and local triangular projection method. The result shows that the search radius and exponential parameter of inverse distance weighted average interpolation method have not significant influence on interpolation result when radius is large.

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16

Guan, Xiucui, and Yangbo Cao. "Constrained and bicriteria inverse bottleneck optimization problems under weighted Hamming distance." Optimization 61, no. 2 (February 2012): 129–42. http://dx.doi.org/10.1080/02331934.2011.651470.

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17

Liu, Longcheng, Wenhao Zheng, and Chao Li. "Inverse minimum flow problem under the weighted sum-type Hamming distance." Discrete Applied Mathematics 229 (October 2017): 101–12. http://dx.doi.org/10.1016/j.dam.2017.06.002.

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18

Li, Xianyue, Xichao Shu, Huijing Huang, and Jingjing Bai. "Capacitated partial inverse maximum spanning tree under the weighted Hamming distance." Journal of Combinatorial Optimization 38, no. 4 (July 5, 2019): 1005–18. http://dx.doi.org/10.1007/s10878-019-00433-x.

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19

Liu, Longcheng, and Enyu Yao. "Capacity inverse minimum cost flow problems under the weighted Hamming distance." Optimization Letters 10, no. 6 (July 7, 2015): 1257–68. http://dx.doi.org/10.1007/s11590-015-0919-y.

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20

Zhang, Zongliang, Jonathan Li, Xin Li, Yangbin Lin, Shanxin Zhang, and Cheng Wang. "A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 725–28. http://dx.doi.org/10.5194/isprsarchives-xli-b1-725-2016.

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This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.

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21

Zhang, Zongliang, Jonathan Li, Xin Li, Yangbin Lin, Shanxin Zhang, and Cheng Wang. "A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 725–28. http://dx.doi.org/10.5194/isprs-archives-xli-b1-725-2016.

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This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.

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22

LIU, LONGCHENG, and ENYU YAO. "A WEIGHTED INVERSE MINIMUM CUT PROBLEM UNDER THE BOTTLENECK TYPE HAMMING DISTANCE." Asia-Pacific Journal of Operational Research 24, no. 05 (October 2007): 725–36. http://dx.doi.org/10.1142/s0217595907001474.

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An inverse optimization problem is defined as follows. Let S denote the set of feasible solutions of an optimization problem P, let c be a specified cost (capacity) vector, and x0 ∈ S. We want to perturb the cost (capacity) vector c to d so that x0 is an optimal solution of P with respect to the cost (capacity) vector d, and to minimize some objective function. In this paper, we consider the weighted inverse minimum cut problem under the bottleneck type Hamming distance. For the general case, we present a combinatorial algorithm that runs in strongly polynomial time.

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23

Jin, Pei Fen, Pei Xue Liu, and Yu Jie Chen. "An Improved Algorithm of Weighted Centroid Algorithm Based on RSSI." Advanced Materials Research 926-930 (May 2014): 2955–58. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2955.

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This paper presents an improved weighted centroid algorithm. The inverse of n multiply cubic distance algorithm was used as the weight, thus avoiding data swamping in the weighted centroid algorithm and preventing information loss. Under the same conditions, the improved weighted centroid algorithm is better than the traditional weighted centroid algorithm. The accuracy is improved by 7.14%.

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24

Liu, Longcheng, and Qin Wang. "Constrained inverse min–max spanning tree problems under the weighted Hamming distance." Journal of Global Optimization 43, no. 1 (March 21, 2008): 83–95. http://dx.doi.org/10.1007/s10898-008-9294-x.

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25

Tayyebi, Javad, and Massoud Aman. "On inverse linear programming problems under the bottleneck-type weighted Hamming distance." Discrete Applied Mathematics 240 (May 2018): 92–101. http://dx.doi.org/10.1016/j.dam.2015.12.017.

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26

Tayyebi, Javad, and Massoud Aman. "Note on “Inverse minimum cost flow problems under the weighted Hamming distance”." European Journal of Operational Research 234, no. 3 (May 2014): 916–20. http://dx.doi.org/10.1016/j.ejor.2013.11.014.

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27

Gossel, Wolfgang, Thomas Chudy, and Michael Falkenhagen. "Interpolation based on isolines: line-geometry-based inverse distance weighted interpolation (L-IDW) with sample applications from the geosciences." Zeitschrift der Deutschen Gesellschaft für Geowissenschaften 163, no. 4 (December 1, 2012): 493–505. http://dx.doi.org/10.1127/1860-1804/2012/0163-0493.

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28

Ravetto Enri, Simone, Alessandra Gorlier, Ginevra Nota, Marco Pittarello, Giampiero Lombardi, and Michele Lonati. "Distance from Night Penning Areas as an Effective Proxy to Estimate Site Use Intensity by Grazing Sheep in the Alps." Agronomy 9, no. 6 (June 21, 2019): 333. http://dx.doi.org/10.3390/agronomy9060333.

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Livestock site use intensity can vary widely across a grazing area due to several factors such as topography and distance from sheds and water sources. However, an accurate approximation of animal site use should be assessed for each part of the grazing area to apply effective management strategies. In the Alps, shepherds manage sheep through lenient supervision during the day and confining the animals in temporary night penning areas (TNPA) at night. In our case study, we assessed sheep site use over the grazing area with global positioning system (GPS) collars and calculated the sums of inverse distances from all TNPA (unweighted and weighted on the number of penning nights) and from all water sources, as well as the slope, on 118 sample points. We assessed the relative importance of these variables in affecting site use intensity by animals using different sets of models. Both the unweighted and weighted distances from TNPA were found to be the most important factors. The best fitting model accounted for the weighted distance from TNPA and the distance from water, but the latter showed a lower relative importance. Our study suggests that using the distance from TNPA, preferably weighted on the number of penning nights, is an effective proxy to estimate the spatial variability of sheep stocking rate during grazing in the Alps.

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29

Zhang, Fu Cai, and Xiao Gang Sun. "Application of Inverse Distance Weighted Interpolation Method in Finite Temperature Point Temperature Field." Applied Mechanics and Materials 599-601 (August 2014): 1268–71. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1268.

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The paper introduces a kind of inverse distance weighted interpolation theoretical derivation and its solving way. The basic principle of this method is that to use known information limited data were interpolated to the unknown information for point, through the obtained interpolation point value to calculate the new interpolation point value. Thus, it is solved that the problem that limited number of temperature points can not be comprehensive description of temperature field, and the method is applied in the process of solving the unknown temperature points in the two dimensional temperature field. The characteristics of this method are simple, practical, less amount of calculation.

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30

Liu, Longcheng, and Enyu Yao. "Inverse min–max spanning tree problem under the Weighted sum-type Hamming distance." Theoretical Computer Science 396, no. 1-3 (May 2008): 28–34. http://dx.doi.org/10.1016/j.tcs.2007.12.006.

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31

Mohaghegh, M., and F. Baroughi Bonab. "Inverse min-max spanning r-arborescence problem under the weighted sum-type Hamming distance." Asian-European Journal of Mathematics 09, no. 03 (August 2, 2016): 1650068. http://dx.doi.org/10.1142/s1793557116500686.

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The inverse min-max spanning [Formula: see text]-arborescence problem under the weighted sum-type Hamming distance on graphs is to modify the edge cost vector with respect to given modification bounds such that a given spanning [Formula: see text]-arborescence becomes a min-max spanning [Formula: see text]-arborescence and the total modification cost under the sum-type Hamming distance for all edges is minimized. It is shown that the problem is solvable in strongly polynomial time.

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32

Siswanto, Eko, Hasbi Yasin, and Sudarno Sudarno. "PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH." Jurnal Gaussian 8, no. 4 (November 29, 2019): 418–27. http://dx.doi.org/10.14710/j.gauss.v8i4.26722.

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In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

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33

Dar, Imran Ahmad, K. Sankar, Mithas Ahmad Dar, and Mrinmoy Majumder. "Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach." Revue des sciences de l’eau 25, no. 2 (August 7, 2012): 165–82. http://dx.doi.org/10.7202/1011606ar.

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The underground waters in the Mamundiyar basin, India, present real chemical quality problems. Their fluoride content always exceeds the recommended levels. The Inverse Distance Weighted (IDW) method has been used for spatial interpolation of various key chemical parameters. Artificial Neural Network (ANN) modeling was applied to understand the correlation and sensitivity of all chemical parameters with respect to fluorides. The correlation of all the considered parameters is found to be poor where the highest correlation observed was only 0.37. This result showed that four of the parameters, namely pH, chlorides, sulphates and calcium, were found to have greater capacity of influencing fluorides than the other eight parameters. Chlorides were found to be the parameter that was the most sensitive and most correlated to fluorides.

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34

De Keijzer, B., T. B. Klos, and Y. Zhang. "Finding Optimal Solutions for Voting Game Design Problems." Journal of Artificial Intelligence Research 50 (May 22, 2014): 105–40. http://dx.doi.org/10.1613/jair.4109.

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In many circumstances where multiple agents need to make a joint decision, voting is used to aggregate the agents' preferences. Each agent's vote carries a weight, and if the sum of the weights of the agents in favor of some outcome is larger than or equal to a given quota, then this outcome is decided upon. The distribution of weights leads to a certain distribution of power. Several `power indices' have been proposed to measure such power. In the so-called inverse problem, we are given a target distribution of power, and are asked to come up with a game in the form of a quota, plus an assignment of weights to the players whose power distribution is as close as possible to the target distribution (according to some specied distance measure). Here we study solution approaches for the larger class of voting game design (VGD) problems, one of which is the inverse problem. In the general VGD problem, the goal is to find a voting game (with a given number of players) that optimizes some function over these games. In the inverse problem, for example, we look for a weighted voting game that minimizes the distance between the distribution of power among the players and a given target distribution of power (according to a given distance measure). Our goal is to find algorithms that solve voting game design problems exactly, and we approach this goal by enumerating all games in the class of games of interest. We first present a doubly exponential algorithm for enumerating the set of simple games. We then improve on this algorithm for the class of weighted voting games and obtain a quadratic exponential (i.e., 2^O(n^2)) algorithm for enumerating them. We show that this improved algorithm runs in output-polynomial time, making it the fastest possible enumeration algorithm up to a polynomial factor. Finally, we propose an exact anytime-algorithm that runs in exponential time for the power index weighted voting game design problem (the `inverse problem'). We implement this algorithm to find a weighted voting game with a normalized Banzhaf power distribution closest to a target power index, and perform experiments to obtain some insights about the set of weighted voting games. We remark that our algorithm is applicable to optimizing any exponential-time computable function, the distance of the normalized Banzhaf index to a target power index is merely taken as an example.

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35

Allasia, G. "Some physical and mathematical properties of inverse distance weighted methods for scattered data interpolation." Calcolo 29, no. 1-2 (March 1992): 97–109. http://dx.doi.org/10.1007/bf02576764.

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36

Souza, Thiago Juncal, José A. C. Canedo Medeiros, Alessandro Cruz Gonçalves, and Aquilino Senra Martinez. "A method to identify an accidental control rod drop with inverse distance weighted interpolation." Annals of Nuclear Energy 145 (September 2020): 107462. http://dx.doi.org/10.1016/j.anucene.2020.107462.

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37

Bechmann, Andreas, Juan Pablo M. Leon, Bjarke T. Olsen, and Yavor V. Hristov. "The most similar predictor – on selecting measurement locations for wind resource assessment." Wind Energy Science 5, no. 4 (December 2, 2020): 1679–88. http://dx.doi.org/10.5194/wes-5-1679-2020.

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Abstract. We present the “most similar” method for selecting optimal measurement positions for wind resource assessment. Wind resource assessment is generally done by extrapolating a measured and long-term corrected wind climate at one location to a prediction location using a micro-scale flow model. If several measurement locations are available, standard industry practice is to make a weighted average of all the possible predictions using inverse-distance weighting. The most similar method challenges this practice. Instead of weighting several predictions, the method only selects the single measurement location evaluated to be most similar. We validate the new approach by comparing against measurements from 185 met masts from 40 wind farm sites and show improvements compared to inverse-distance weighting. Compared to using the closest measurement location, the error of power density predictions is reduced by 13 % using inverse-distance weighting and 34 % using the most similar method.

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38

Ahmed Al_awad, Nasir, and Ghusoon Idan Arb. "ANALYSIS OF SOLAR RADIATION Data IN CIRCULAR MODEL BASED ON NATURAL NEIGHBOUR INVERSE DISTANCE WEIGHTED." Journal of Engineering and Sustainable Development 22, no. 02 (March 1, 2018): 86–93. http://dx.doi.org/10.31272/jeasd.2018.2.70.

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39

Chen, Chen, Fangyu Peng, Rong Yan, Zheng Fan, Yuting Li, and Dequan Wei. "Posture-dependent stability prediction of a milling industrial robot based on inverse distance weighted method." Procedia Manufacturing 17 (2018): 993–1000. http://dx.doi.org/10.1016/j.promfg.2018.10.104.

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40

Wang, Jian, Xiaohong Meng, and Wanqiu Zheng. "Elastic-wave-mode separation in TTI media with inverse-distance weighted interpolation involving position shading." Journal of Geophysics and Engineering 14, no. 5 (September 12, 2017): 1260–67. http://dx.doi.org/10.1088/1742-2140/aa7bd1.

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41

Qu, Rui, Keke Xiao, Jingping Hu, Sha Liang, Huijie Hou, Bingchuan Liu, Fu Chen, Qi Xu, Xiang Wu, and Jiakuan Yang. "Predicting the hormesis and toxicological interaction of mixtures by an improved inverse distance weighted interpolation." Environment International 130 (September 2019): 104892. http://dx.doi.org/10.1016/j.envint.2019.06.002.

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42

Tarmizi, S. N. M., A. Asmat, and S. M. Sumari. "Temporal and spatial PM10 concentration distribution using an inverse distance weighted method in Klang Valley, Malaysia." IOP Conference Series: Earth and Environmental Science 18 (February 25, 2014): 012048. http://dx.doi.org/10.1088/1755-1315/18/1/012048.

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43

Ogbozige, F. J., D. B. Adie, and U. A. Abubakar. "Water quality assessment and mapping using inverse distance weighted interpolation: a case of River Kaduna, Nigeria." Nigerian Journal of Technology 37, no. 1 (January 17, 2018): 249. http://dx.doi.org/10.4314/njt.v37i1.33.

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44

Handayani, A. P., A. Deliar, I. Sumarto, and I. Syabri. "Inverse Distance Weighting interpolation on the optimum distribution of kernel - Geographically Weighted Regression for land price." IOP Conference Series: Earth and Environmental Science 389 (December 13, 2019): 012031. http://dx.doi.org/10.1088/1755-1315/389/1/012031.

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45

Zhu, Weidong, Biao Mei, and Yinglin Ke. "Inverse kinematics solution of a new circumferential drilling machine for aircraft assembly." Robotica 34, no. 1 (May 22, 2014): 98–117. http://dx.doi.org/10.1017/s0263574714001350.

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SUMMARYInverse kinematics solutions are the basis for position and orientation control of automated machines in their Cartesian workspace. This paper presents an efficient and robust inverse kinematics algorithm for a new circumferential drilling machine for aircraft fuselage assembly. After a brief introduction to the circumferential drilling machine and its forward kinematics, the paper discusses the nonlinear optimization method for solving inverse kinematics problems. The objective function is defined as a weighted combination of a position error function and an orientation error function. By representing orientation error as the geodesic distance between two points on a unit sphere, the paper proposes to define the orientation error function by using faithful geodesic distance functions, which are accurate approximations to the geodesic distance when it is small. For increased efficiency, robustness, and easy setting of initial values, the inverse kinematics problem is decomposed into two subproblems. The revolute joint coordinates are obtained by nonlinear optimization, and the prismatic joint coordinates are calculated with closed-form formulas. Numerical experiments show that the objective function defined with faithful geodesic distance functions is effective, and the proposed algorithm is efficient, robust, and accurate. The algorithm has been successfully integrated into the control system of the circumferential drilling machine. Preliminary drilling experiments show that the position accuracy of drilled holes is within ±0.5 mm, which is acceptable for the assembly of large aircrafts.

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46

Rhee, Jinyoung, Gregory J. Carbone, and James Hussey. "Drought Index Mapping at Different Spatial Units." Journal of Hydrometeorology 9, no. 6 (December 1, 2008): 1523–34. http://dx.doi.org/10.1175/2008jhm983.1.

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Abstract This paper investigates the influence of spatial interpolation and aggregation of data to depict drought at different spatial units relevant to and often required for drought management. Four different methods for drought index mapping were explored, and comparisons were made between two spatial operation methods (simple unweighted average versus spatial interpolation plus aggregation) and two calculation procedures (whether spatial operations are performed before or after the calculations of drought index values). Deterministic interpolation methods including Thiessen polygons, inverse distance weighted, and thin-plate splines as well as a stochastic and geostatistical interpolation method of ordinary kriging were compared for the two methods that use interpolation. The inverse distance weighted method was chosen based on the cross-validation error. After obtaining drought index values for different spatial units using each method in turn, differences in the empirical binned frequency distributions were tested between the methods and spatial units. The two methods using interpolation and aggregation introduced fewer errors in cross validation than the two simple unweighted average methods. Whereas the method performing spatial interpolation and aggregation before calculating drought index values generally provided consistent drought information between various spatial units, the method performing spatial interpolation and aggregation after calculating drought index values reduced errors related to the calculations of precipitation data.

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Waseem, Muhammad, Muhammad Ajmal, Ungtae Kim, and Tae-Woong Kim. "Development and evaluation of an extended inverse distance weighting method for streamflow estimation at an ungauged site." Hydrology Research 47, no. 2 (December 8, 2015): 333–43. http://dx.doi.org/10.2166/nh.2015.117.

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In spatial interpolation, one of the most widely used deterministic methods is the inverse distance weighting (IDW) technique. The general idea of IDW is primarily based on the hypothesis that the attribute value of an ungauged site is the weighted average of the known attribute values within the neighborhood, and the ‘weights’ are merely associated with the horizontal distances between the gauged and ungauged sites. However, here we propose an extended version of IDW (hereafter, called the EIDW method) to provide ‘alternative weights’ based on the blended geographical and physiographical spaces for estimation of streamflow percentiles at ungauged sites. Based on the leave-one-out cross-validation procedure, the coefficient of determination had a value of 0.77 and 0.82 for the proposed EIDW models, M1 and M2, respectively, with low root mean square errors. Moreover, after investigating the relationship between the prediction efficiency and the distance decay parameter (C), the better performance of the M1 and M2 resulted at C = 2. Furthermore, the results of this study show that the EIDW could be considered as a constructive way forward to provide more accurate and consistent results in comparison to the traditional IDW or the dimension reduction technique-based IDW.

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48

Han, Kuk-Il, Do-Hwi Kim, Jun-Hyuk Choi, and Tae-Kuk Kim. "Development of an inverse distance weighted active infrared stealth scheme using the repulsive particle swarm optimization algorithm." Applied Optics 57, no. 12 (April 16, 2018): 3072. http://dx.doi.org/10.1364/ao.57.003072.

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Jiao Songming, 焦嵩鸣, 武晓凯 Wu Xiaokai, 郑晓坤 Zheng Xiaokun, and 阮天宇 Ruan Tianyu. "Construction and Application of Greedy Triangulation for Lidar Point-Cloud Data Based on Inverse-Distance-Weighted Interpolation." Laser & Optoelectronics Progress 56, no. 21 (2019): 212801. http://dx.doi.org/10.3788/lop56.212801.

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Musashi, Jaka Pratama, Henny Pramoedyo, and Rahma Fitriani. "Comparison of Inverse Distance Weighted and Natural Neighbor Interpolation Method at Air Temperature Data in Malang Region." CAUCHY 5, no. 2 (May 21, 2018): 48. http://dx.doi.org/10.18860/ca.v5i2.4722.

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Abstract:

The purpose of this study was to compare the results of Inverse Distance Weighted (IDW) and Natural Neighbor interpolation methods for spatial data of air temperature in the Malang Region. Interpolation is one way to determine a point of events from several points around the known value. Spatial interpolation can be used to estimate an area that does not have a data record using the value of its known surroundings. 38 points observation air temperature of Malang Region in 2016 is used as a sample point to interpolate the surrounding air temperature. Obtained optimum parameter power value is 2 for IDW interpolation method. The RMSE comparison results show that IDW method is better to be used than the Natural Neighbor Interpolation method with the RMSE values of 1,2292 for the IDW method and 1,6173 for the NN method.

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