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Journal articles on the topic 'Similarity transformations'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

Zhu, N. G., L. Xu, and M. Z. Chen. "Similarity Transformations for Compressor Blading." Journal of Turbomachinery 114, no. 3 (1992): 561–68. http://dx.doi.org/10.1115/1.2929180.

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Improving the performance of high-speed axial compressors through low-speed model compressor testing has proved to be economical and effective (Wisler, 1985). The key to this technique is to design low-speed blade profiles that are aerodynamically similar to their high-speed counterparts. The conventional aerodynamic similarity transformation involves the small disturbance potential flow assumption; therefore, its application is severely limited and generally not used in practical design. In this paper, a set of higher order transformation rules are presented that can accommodate large disturb

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2

Katriel, Jacob, and A. I. Georgieva. "q-similarity transformations." Physics Letters A 280, no. 5-6 (2001): 246–48. http://dx.doi.org/10.1016/s0375-9601(01)00075-5.

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3

Mullineux, G. "Matrices inducing similarity transformations." International Journal of Mathematical Education in Science and Technology 16, no. 1 (1985): 33–37. http://dx.doi.org/10.1080/0020739850160106.

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4

Andersen, Allan T., V. A. Barker, and Bo Friis Nielsen. "Similarity transformations of MAPs." Mathematical Problems in Engineering 5, no. 4 (1999): 349–59. http://dx.doi.org/10.1155/s1024123x99001131.

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5

Bharti, Puja, and Jagmohan Tanti. "Similarity Classes of Linear Transformations." Journal of the Indian Mathematical Society 87, no. 3-4 (2020): 148. http://dx.doi.org/10.18311/jims/2020/25448.

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In this paper, we investigate the similarity classes of linear transformations on a vector space using structure theorem for finitely generated modules over a principal ideal domain. We also establish formulae to count similarity classes with a given polynomial as a characteristic polynomial and to count total number of classes when the scalar field is finite.

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6

Larson, David R. "Nest Algebras and Similarity Transformations." Annals of Mathematics 121, no. 2 (1985): 409. http://dx.doi.org/10.2307/1971180.

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7

Adhikari, S. "On Symmetrizable Systems of Second Kind." Journal of Applied Mechanics 67, no. 4 (2000): 797–802. http://dx.doi.org/10.1115/1.1322038.

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We discuss under what conditions multiple-parameter asymmetric linear dynamical systems can be transformed into equivalent symmetric systems by nonsingular linear transformations. So far, in structural dynamics literature this problem has been addressed in the context of the original work by Taussky. Taussky’s approach of symmetrization was based on similarity transformation. In this paper an approach is proposed to transform asymmetric systems into symmetric systems by equivalence transformation. We call Taussky’s approach of symmetrization by similarity transformation “first kind” and propos

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8

Pakdemirli, Mehmet, and Muhammet Yurusoy. "Similarity Transformations for Partial Differential Equations." SIAM Review 40, no. 1 (1998): 96–101. http://dx.doi.org/10.1137/s003614459631001x.

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9

Zhong, Qing-Chang. "J-SPECTRAL FACTORIZATION VIA SIMILARITY TRANSFORMATIONS." IFAC Proceedings Volumes 38, no. 1 (2005): 339–44. http://dx.doi.org/10.3182/20050703-6-cz-1902.01001.

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10

Bhakta, Mousomi, and Moshe Marcus. "Semilinear elliptic equations admitting similarity transformations." Journal of Functional Analysis 267, no. 10 (2014): 3894–930. http://dx.doi.org/10.1016/j.jfa.2014.08.008.

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11

Lee, Myoungji, Marc G. Genton, and Mikyoung Jun. "Testing Self-Similarity Through Lamperti Transformations." Journal of Agricultural, Biological, and Environmental Statistics 21, no. 3 (2016): 426–47. http://dx.doi.org/10.1007/s13253-016-0258-1.

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12

Rigas, George, Christophoros Nikou, Yorgos Goletsis, and Dimitrios I. Fotiadis. "Hierarchical Similarity Transformations Between Gaussian Mixtures." IEEE Transactions on Neural Networks and Learning Systems 24, no. 11 (2013): 1824–35. http://dx.doi.org/10.1109/tnnls.2013.2267803.

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13

Fernández, Francisco M. "Non-Hermitian Hamiltonians and Similarity Transformations." International Journal of Theoretical Physics 55, no. 2 (2015): 843–50. http://dx.doi.org/10.1007/s10773-015-2724-x.

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14

San Miguel, A. "Deformable Asymmetric Tops under Similarity Transformations." Journal of Nonlinear Science 13, no. 5 (2003): 471–85. http://dx.doi.org/10.1007/s00332-003-0539-z.

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15

Yokote, Ken-ichi, Danushka Bollegala, and Mitsuru Ishizuka. "Similarity Is Not Entailment — Jointly Learning Similarity Transformation for Textual Entailment." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1720–26. http://dx.doi.org/10.1609/aaai.v26i1.8348.

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Predicting entailment between two given texts is an important task upon which the performance of numerous NLP tasks depend on such as question answering, text summarization, and information extraction. The degree to which two texts are similar has been used extensively as a key feature in much previous work in predicting entailment. However, using similarity scores directly, without proper transformations, results in suboptimal performance. Given a set of lexical similarity measures, we propose a method that jointly learns both (a) a set of non-linear transformation functions for those similar

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16

Shavarovskii, B. Z. "Reduction of matrices by means of equivalent transformations and similarity transformations." Mathematical Notes 64, no. 5 (1998): 663–73. http://dx.doi.org/10.1007/bf02316292.

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17

Osikawa, Motosige, and Haruo Totoki. "Self-similarity of ergodic measure preserving transformations." Hiroshima Mathematical Journal 22, no. 2 (1992): 219–24. http://dx.doi.org/10.32917/hmj/1206392899.

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18

Gal-Or, Benjamin. "Fundamentals and similarity transformations of vectored aircraft." Journal of Aircraft 31, no. 1 (1994): 181–87. http://dx.doi.org/10.2514/3.46472.

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19

Yokote, Ken-ichi, Danushka Bollegala, and Mitsuru Ishizuka. "Jointly Learning Similarity Transformations for Textual Entailment." Transactions of the Japanese Society for Artificial Intelligence 28, no. 2 (2013): 220–29. http://dx.doi.org/10.1527/tjsai.28.220.

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20

Morgenstern, Yaniv, Filipp Schmidt, and Roland Fleming. "Effects of shape transformations on perceived similarity." Journal of Vision 17, no. 10 (2017): 1383. http://dx.doi.org/10.1167/17.10.1383.

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21

Li, Shousong, J. Craig Hamilton, and Paul J. Gemperline. "Generalized rank annihilation method using similarity transformations." Analytical Chemistry 64, no. 6 (1992): 599–607. http://dx.doi.org/10.1021/ac00030a007.

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22

Bevilacqua, Roberto, Enrico Bozzo, and Gianna M. Del Corso. "Transformations to rank structures by unitary similarity." Linear Algebra and its Applications 402 (June 2005): 126–34. http://dx.doi.org/10.1016/j.laa.2004.12.029.

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23

Draper, Bruce A., and Albert Lionelle. "Evaluation of selective attention under similarity transformations." Computer Vision and Image Understanding 100, no. 1-2 (2005): 152–71. http://dx.doi.org/10.1016/j.cviu.2004.08.006.

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24

ÖZGÜR, Nihal Yılmaz. "Ellipses and similarity transformations with norm functions." TURKISH JOURNAL OF MATHEMATICS 42, no. 6 (2018): 3204–10. http://dx.doi.org/10.3906/mat-1806-22.

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25

Nishitani, Toshiyuki, and Kazushi Ishii. "Similarity Transformations of the Navier-Stokes Equation." Journal of the Physical Society of Japan 54, no. 12 (1985): 4561–69. http://dx.doi.org/10.1143/jpsj.54.4561.

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26

Zuber, Irina E. "Stabilization of nonlinear systems by similarity transformations." Journal of Applied Mathematics and Stochastic Analysis 11, no. 4 (1998): 519–26. http://dx.doi.org/10.1155/s1048953398000422.

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For a system x˙=A(x)+b(x)u, u(x)=s∗(x)x, x∈ℝn, where the pair (A(x),b(x)) is given, we obtain the feedback vector s(x) to stabilize the corresponding closed loop system. For an arbitrarily chosen constant vector g, a sufficient condition of the existence and an explicit form of a similarity transformation T(A(x),b(x),g) is established. The latter transforms matrix A(x) into the Frobenius matrix, vector b(x) into g, and an unknown feedback vector s(x) into the first unit vector. The boundaries of A˜(y,g) are determined by the boundaries of {∂kA(x)∂xk,∂kb(x)∂xk}, k=0,n−1¯. The stabilization of t

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27

Chowdhury, K. L. "Similarity transformations for the axisymmetric Boussinesque's problem." Acta Mechanica 68, no. 3-4 (1987): 171–83. http://dx.doi.org/10.1007/bf01190881.

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28

Bannon, Thomas J. "Fractals and Transformations." Mathematics Teacher 84, no. 3 (1991): 178–85. http://dx.doi.org/10.5951/mt.84.3.0178.

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In traditional geometry we model nature using the points, lines, and planes of Euclid. Very recently Mandelbrot (1983) gave us fractals to use in building new models. One of the reasons these new fractal models are successful is that a great deal of self similarity is found in nature. Weather patterns over local areas resemble large-scale weather patterns. Branches resemble trees and rocks resemble mountains. The regularities as well as the irregularities of nature repeat at different scales. Geographical features are so self-similar that cartographers must place scales on maps to keep islands

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29

Rangelov, Dragan. "Types of geometrical transformations and perceptual similarity of figures." Psihologija 37, no. 4 (2004): 483–93. http://dx.doi.org/10.2298/psi0404483r.

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In this study it was investigated which of several types of geometrical transformations emerge as dominant principle in grouping forms. It was assumed that forms which have more similar features would be grouped together more often. The stimuli consisted of basic form (O) and it?s transformation: (T1) rotation; (T2) projection onto horizontal plane; (T3) projection onto vertical plane; (T4) stretching and (T5) twisting. There were two groups of stimuli: (G1) asymmetrical planes and (G2) linear patterns. Stimuli varied in complexity (i.e. number of lines they contain): (S1) four; (S2) five and

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30

AGHILI, S. ALIREZA, DIVYAKANT AGRAWAL, and AMR EL ABBADI. "SEQUENCE SIMILARITY SEARCH USING DISCRETE FOURIER AND WAVELET TRANSFORMATION TECHNIQUES." International Journal on Artificial Intelligence Tools 14, no. 05 (2005): 733–54. http://dx.doi.org/10.1142/s0218213005002363.

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In this paper, we study the problem of sequence similarity search. We incorporate vector transformations and apply DFT (Discrete Fourier Transformation) and DWT (Discrete Wavelet Transformation, Haar) dimensionality reduction techniques to reduce the search space/time of sequence similarity range queries. Our empirical results on a number of Prokaryote and Eukaryote DNA contig databases demonstrate up to 50-fold filtration ratio reduction of the search space and up to 13 times faster filtration. The proposed transformation techniques may easily be integrated as a pre-processing phase on top of

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31

Seago, Nanette, Jennifer Jacobs, Mark Driscoll, Michael Matassa, and Matassa Callahan. "Developing Teachers' Knowledge of a Transformations-Based Approach to Geometric Similarity." Mathematics Teacher Educator 2, no. 1 (2013): 74–85. http://dx.doi.org/10.5951/mathteaceduc.2.1.0074.

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U.S. students' poor performance in the domain of geometric transformations is well documented, as are their diffi culties applying transformations to similarity tasks. At the same time, a transformations-based approach to similarity underlies the Common Core State Standards for middle and high school geometry. We argue that engaging teachers in this topic represents an urgent but largely unmet need. The article considers what a transformations-based approach to similarity looks like by contrasting it with a traditional, static approach and by providing classroom examples of students using thes

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32

Halder, Amlan K., A. Paliathanasis, S. Rangasamy, and P. G. L. Leach. "Similarity Solutions for the Complex Burgers’ Hierarchy." Zeitschrift für Naturforschung A 74, no. 7 (2019): 597–604. http://dx.doi.org/10.1515/zna-2018-0541.

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AbstractA detailed analysis of the invariant point transformations for the first four partial differential equations which belong to the complex Burgers’ hierarchy is performed. Moreover, a detailed application of the reduction process through the Lie-point symmetries is presented while we construct the similarity solutions. We conclude that the differential equations of our consideration are reduced to first-order equations such as the Abel, Riccati, and to a linearisable second-order differential equation by using similarity transformations.

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33

Arora, Akansha, and Samrith Ram. "Enumerating partial linear transformations in a similarity class." Linear Algebra and its Applications 625 (September 2021): 196–211. http://dx.doi.org/10.1016/j.laa.2021.05.007.

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34

Abou Halima, Hisham, and Abo El Hassan F. Rahil. "APPLICATION OF SIMILARITY TRANSFORMATIONS IN GEODETIC MONITORING NETWORKS." ERJ. Engineering Research Journal 24, no. 4 (2001): 97–113. http://dx.doi.org/10.21608/erjm.2001.71135.

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35

Andraphanova, Natalia V. "Geometrical similarity transformations in Dynamic Geometry Environment GeoGebra." European Journal of Contemporary Education 12, no. 2 (2015): 116–28. http://dx.doi.org/10.13187/ejced.2015.12.116.

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36

Schwalbe-Koda, Daniel, Zach Jensen, Elsa Olivetti, and Rafael Gómez-Bombarelli. "Graph similarity drives zeolite diffusionless transformations and intergrowth." Nature Materials 18, no. 11 (2019): 1177–81. http://dx.doi.org/10.1038/s41563-019-0486-1.

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37

Brychcín, Tomáš. "Linear transformations for cross-lingual semantic textual similarity." Knowledge-Based Systems 187 (January 2020): 104819. http://dx.doi.org/10.1016/j.knosys.2019.06.027.

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38

Li, Shousong, J. Craig Hamilton, and Paul J. Gemperline. "Correction. Generalized Rank Annihilation Method Using Similarity Transformations." Analytical Chemistry 65, no. 11 (1993): 1647. http://dx.doi.org/10.1021/ac00059a600.

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39

Bribiesca, Ernesto. "Measuring 3-D shape similarity using progressive transformations." Pattern Recognition 29, no. 7 (1996): 1117–29. http://dx.doi.org/10.1016/0031-3203(95)00150-6.

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40

Meissner, Leszek, and Marcel Nooijen. "Effective and intermediate Hamiltonians obtained by similarity transformations." Journal of Chemical Physics 102, no. 24 (1995): 9604–14. http://dx.doi.org/10.1063/1.468777.

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41

Silva, Yasin N., Walid G. Aref, Per-Ake Larson, Spencer S. Pearson, and Mohamed H. Ali. "Similarity queries: their conceptual evaluation, transformations, and processing." VLDB Journal 22, no. 3 (2012): 395–420. http://dx.doi.org/10.1007/s00778-012-0296-4.

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42

Albares, Paz, and Pilar Garcia Estévez. "Miura-Reciprocal Transformation and Symmetries for the Spectral Problems of KdV and mKdV." Mathematics 9, no. 9 (2021): 926. http://dx.doi.org/10.3390/math9090926.

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We present reciprocal transformations for the spectral problems of Korteveg de Vries (KdV) and modified Korteveg de Vries (mKdV) equations. The resulting equations, RKdV (reciprocal KdV) and RmKdV (reciprocal mKdV), are connected through a transformation that combines both Miura and reciprocal transformations. Lax pairs for RKdV and RmKdV are straightforwardly obtained by means of the aforementioned reciprocal transformations. We have also identified the classical Lie symmetries for the Lax pairs of RKdV and RmKdV. Non-trivial similarity reductions are computed and they yield non-autonomous or

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43

Pazzis, Clément de Seguins, and Peter Šemrl. "Continuous space-time transformations." Advances in Geometry 18, no. 3 (2018): 385–93. http://dx.doi.org/10.1515/advgeom-2017-0056.

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AbstractWe prove that every continuous map acting on the four-dimensional Minkowski space and preserving light cones in one direction only is either a Poincaré similarity, that is, a product of a Lorentz transformation and a dilation, or it is of a very special degenerate form.

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44

Lasorne, Benjamin. "On the Use of Lie Group Homomorphisms for Treating Similarity Transformations in Nonadiabatic Photochemistry." Advances in Mathematical Physics 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/795730.

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A formulation based on Lie group homomorphisms is presented for simplifying the treatment of unitary similarity transformations of Hamiltonian matrices in nonadiabatic photochemistry. A general derivation is provided whereby it is shown that a similarity transformation acting on a traceless, Hermitian matrix through a unitary matrix ofSU(n)is equivalent to the product of a single matrix ofOn2-1by a real vector. We recall how Pauli matrices are the adequate tool whenn=2and show how the same is achieved forn=3with Gell-Mann matrices.

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45

Fan, Hong-Yi, and Hui Zou. "Similarity transformation operators as the images of classical symplectic transformations in coherent state representation." Physics Letters A 252, no. 6 (1999): 281–87. http://dx.doi.org/10.1016/s0375-9601(98)00900-1.

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46

Sempio, J. N. H., R. K. D. Aranas, B. P. Lim, B. J. Magallon, M. E. A. Tupas, and I. A. Ventura. "ASSESSMENT OF DIFFERENT IMAGE TRANSFORMATION METHODS ON DIWATA-1 SMI IMAGES USING STRUCTURAL SIMILARITY MEASURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 393–400. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-393-2019.

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Abstract. This paper aims to provide a qualitative assessment of different image transformation parameters as applied on images taken by the spaceborne multispectral imager (SMI) sensor installed in Diwata-1, the Philippines’ first Earth observation microsatellite, with the aim of determining the order of transformation that is sufficient for operationalization purposes. Images of the Palawan area were subjected to different image transformations by manual georeferencing using QGIS 3, and cloud masks generated and applied to remove the effects of clouds. The resulting images were then subjecte

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47

Kinelovskii S.A. "Similarity between shock-induced polymorphic transitions in the silica system." Technical Physics 92, no. 6 (2022): 695. http://dx.doi.org/10.21883/tp.2022.06.54415.320-21.

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A set of experimental data on the thermodynamic parameters of polymorphic transitions in the silica system is considered. Analysis of these parameters in dimensionless form is performed. A fundamental result of the analysis is that the thermodynamic parameters of all silica polymorphs after transitions are described by a single universal Hugoniot of polymorphic transition. It is shown that the two-shock model of polymorphic transformations proposed earlier by the author describes all the results obtained in the analysis. A joint consideration of the experimental data and model calculations lea

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48

LANG, CHRISTIAN A., and AMBUJ K. SINGH. "FASTER SIMILARITY SEARCH FOR MULTIMEDIA DATA VIA QUERY TRANSFORMATIONS." International Journal of Image and Graphics 03, no. 01 (2003): 3–29. http://dx.doi.org/10.1142/s0219467803000890.

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The performance of nearest neighbor (NN) queries degrades noticeably with increasing dimensionality of the data due to reduced selectivity of high-dimensional data and an increased number of seek operations during NN-query execution. If the NN-radii would be known in advance, the disk accesses could be reordered such that seek operations are minimized. We therefore propose a new way of estimating the NN-radius based on the fractal dimensionality and sampling. It is applicable to any page-based index structure. We show that the estimation error is considerably lower than for previous approaches

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49

Hong-Yi, Fan, and J. VanderLinde. "Similarity transformations in one- and two-mode Fock space." Journal of Physics A: Mathematical and General 24, no. 11 (1991): 2529–38. http://dx.doi.org/10.1088/0305-4470/24/11/019.

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50

Shavarovskii, B. Z. "Similarity transformations of decomposable matrix polynomials and related questions." Computational Mathematics and Mathematical Physics 49, no. 9 (2009): 1469–82. http://dx.doi.org/10.1134/s0965542509090012.

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