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Thematische Bibliographien / Computationnal geometry / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Computationnal geometry“

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Autor: Grafiati

Veröffentlicht am 8. Juni 2024

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1

Toma, Milan, Satvinder K. Guru, Wayne Wu, May Ali und Chi Wei Ong. „Addressing Discrepancies between Experimental and Computational Procedures“. Biology 10, Nr. 6 (15.06.2021): 536. http://dx.doi.org/10.3390/biology10060536.

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Imaging subject-specific heart valve, a crucial step to its design, has experimental variables that if unaccounted for, may lead to erroneous computational analysis and geometric errors of the resulting model. Preparation methods are developed to mitigate some sources of the geometric error. However, the resulting 3D geometry often does not retain the original dimensions before excision. Inverse fluid–structure interaction analysis is used to analyze the resulting geometry and to assess the valve’s closure. Based on the resulting closure, it is determined if the geometry used can yield realistic results. If full closure is not reached, the geometry is adjusted adequately until closure is observed.

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2

Bayer, Tomáš. „The importance of computational geometry for digital cartography“. Geoinformatics FCE CTU 3 (12.04.2008): 15–24. http://dx.doi.org/10.14311/gi.3.2.

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This paper describes the use of computational geometry concepts in the digital cartography. It presents an importance of 2D geometric structures, geometric operations and procedures for automated or semi automated simplification process. This article is focused on automated building simplification procedures, some techniques are illustrated and discussed. Concrete examples with the requirements to the lowest time complexity, emphasis on the smallest area enclosing rectangle, convex hull or self intersection procedures, are given. Presented results illustrate the relationship of digital cartography and computational geometry.

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3

Cafaro, Carlo. „Geometric algebra and information geometry for quantum computational software“. Physica A: Statistical Mechanics and its Applications 470 (März 2017): 154–96. http://dx.doi.org/10.1016/j.physa.2016.11.117.

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4

Moussiaux, A., und Ph Tombal. „Geometric calculus: A new computational tool for Riemannian geometry“. International Journal of Theoretical Physics 27, Nr. 5 (Mai 1988): 613–21. http://dx.doi.org/10.1007/bf00668842.

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5

Veltkamp, Remco C. „Generic Geometric Programming in the Computational Geometry Algorithms Library“. Computer Graphics Forum 18, Nr. 2 (Juni 1999): 131–37. http://dx.doi.org/10.1111/1467-8659.00363.

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6

ASANO, Tetsuo. „Computational Geometry“. Journal of Japan Society for Fuzzy Theory and Systems 13, Nr. 2 (2001): 130–38. http://dx.doi.org/10.3156/jfuzzy.13.2_2.

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7

O'Rourke, Joseph. „Computational geometry“. ACM SIGACT News 23, Nr. 2 (Mai 1992): 26–28. http://dx.doi.org/10.1145/130956.130957.

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8

O'Rourke, J. „Computational Geometry“. Annual Review of Computer Science 3, Nr. 1 (Juni 1988): 389–411. http://dx.doi.org/10.1146/annurev.cs.03.060188.002133.

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9

Agarwal, Pankaj K., und Joseph O'Rourke. „Computational geometry“. ACM SIGACT News 29, Nr. 3 (September 1998): 27–32. http://dx.doi.org/10.1145/300307.300310.

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10

Lee, D. T. „Computational geometry“. ACM Computing Surveys 28, Nr. 1 (März 1996): 27–31. http://dx.doi.org/10.1145/234313.234325.

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11

Mitchell, Joseph S. B., und Joseph O'Rourke. „Computational geometry“. ACM SIGACT News 32, Nr. 3 (September 2001): 63–72. http://dx.doi.org/10.1145/500559.500562.

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12

O'Rourke, Joseph. „Computational geometry“. ACM SIGACT News 26, Nr. 1 (März 1995): 14–16. http://dx.doi.org/10.1145/203610.203613.

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13

Toussaint, Godfried T. „Computational geometry“. Visual Computer 3, Nr. 6 (November 1988): 321–22. http://dx.doi.org/10.1007/bf01901189.

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14

O'Rourke, Joseph. „Computational geometry“. ACM SIGACT News 25, Nr. 1 (März 1994): 31–33. http://dx.doi.org/10.1145/181773.181777.

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15

Woo, Tony C. „Computational geometry“. Visual Computer 1, Nr. 2 (August 1985): 67. http://dx.doi.org/10.1007/bf01898348.

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16

Meng, Qingen, John Fisher und Ruth Wilcox. „The effects of geometric uncertainties on computational modelling of knee biomechanics“. Royal Society Open Science 4, Nr. 8 (August 2017): 170670. http://dx.doi.org/10.1098/rsos.170670.

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The geometry of the articular components of the knee is an important factor in predicting joint mechanics in computational models. There are a number of uncertainties in the definition of the geometry of cartilage and meniscus, and evaluating the effects of these uncertainties is fundamental to understanding the level of reliability of the models. In this study, the sensitivity of knee mechanics to geometric uncertainties was investigated by comparing polynomial-based and image-based knee models and varying the size of meniscus. The results suggested that the geometric uncertainties in cartilage and meniscus resulting from the resolution of MRI and the accuracy of segmentation caused considerable effects on the predicted knee mechanics. Moreover, even if the mathematical geometric descriptors can be very close to the imaged-based articular surfaces, the detailed contact pressure distribution produced by the mathematical geometric descriptors was not the same as that of the image-based model. However, the trends predicted by the models based on mathematical geometric descriptors were similar to those of the imaged-based models.

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17

Dhande, S. G., K. P. Karunakaran und B. K. Misra. „Geometric Modeling of Manufacturing Processes Using Symbolic and Computational Conjugate Geometry“. Journal of Engineering for Industry 117, Nr. 3 (01.08.1995): 288–96. http://dx.doi.org/10.1115/1.2804333.

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The present paper describes a unified symbolic model of conjugate geometry. This model can be used to study the geometry of a cutting tool and the surface generated by it on a blank along with the kinematic relationships between the tool and the blank. A symbolic algorithm for modeling a variety of shape generating processes has been developed. It has been shown that using this algorithm one can develop geometric models for conventional machining processes such as milling, turning, etc. as well as unconventional or advanced machining techniques such as Electric Discharge Machining (EDM), Laser Beam Machining (LBM) etc. The proposed symbolic algorithm has been implemented using the symbolic manipulation software, MACSYMA. The algorithm is based on the concepts of envelope theory and conjugate geometry of a pair of mutually enveloping surfaces. A case study on the manufacture of a helicoidal surface and an illustrative example are given at the end of the paper.

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18

Abbott, Steve, Helmut Pottman und Johannes Wallner. „Computational Line Geometry“. Mathematical Gazette 86, Nr. 507 (November 2002): 571. http://dx.doi.org/10.2307/3621207.

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19

Overmars, Mark H. „Teaching computational geometry“. ACM SIGGRAPH Computer Graphics 29, Nr. 1 (Februar 1995): 18–22. http://dx.doi.org/10.1145/216218.216224.

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20

Shaska, T. „Computational algebraic geometry“. Journal of Symbolic Computation 57 (Oktober 2013): 1–2. http://dx.doi.org/10.1016/j.jsc.2013.05.001.

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21

Aggarwal, A., B. Chazelle, L. Guibas, C. Ó'Dúnlaing und C. Yap. „Parallel computational geometry“. Algorithmica 3, Nr. 1-4 (November 1988): 293–327. http://dx.doi.org/10.1007/bf01762120.

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22

O'Rourke, Joseph. „Computational geometry column“. ACM SIGACT News 19, Nr. 3-4 (November 1988): 21–26. http://dx.doi.org/10.1145/58395.58397.

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23

O'Rourke, Joseph. „Computational geometry column“. ACM SIGACT News 20, Nr. 2 (März 1989): 10–11. http://dx.doi.org/10.1145/70640.70641.

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24

O'Rourke, Joseph. „Computational geometry column“. ACM SIGACT News 20, Nr. 3 (Juli 1989): 25–26. http://dx.doi.org/10.1145/70642.70644.

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25

Kanatani, Kenichi. „Computational projective geometry“. CVGIP: Image Understanding 54, Nr. 3 (November 1991): 333–48. http://dx.doi.org/10.1016/1049-9660(91)90034-m.

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26

Rojas, J. Maurice. „Computational Arithmetic Geometry“. Journal of Computer and System Sciences 62, Nr. 2 (März 2001): 216–35. http://dx.doi.org/10.1006/jcss.2000.1728.

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27

Fiorini, Rodolfo A. „The Emerging Computational Biolinguistic Framework“. International Journal of Cognitive Informatics and Natural Intelligence 12, Nr. 4 (Oktober 2018): 1–19. http://dx.doi.org/10.4018/ijcini.2018100101.

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The convergence of software and intelligent sciences forms the transdisciplinary field of computational intelligence. Abstract intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom-up (BU). The human brain is at least a factor of 1 billion more efficient than our present digital technology, and a factor of 10 million more efficient than the best digital technology that we can imagine today. The unavoidable conclusion is that current neuromorphic engineering has something fundamental to learn from the human brain and cells about a new and much more effective form of computation, with a convenient, effective, efficient, and reliable BU approach. The author presents a brain-inspired geometric-logical scheme defining fundamental human linguistic and predicative competence. According to CICT, complete duality of opposition and implication geometry in logical geometry and language can model n-dimensional predicative competence and beyond, according to available computational resources.

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28

Fachrudin, Achmad Dhany, und Dwi Juniati. „Kinds of Mathematical Thinking Addressed in Geometry Research in Schools: A Systematic Review“. Jurnal Riset Pendidikan dan Inovasi Pembelajaran Matematika (JRPIPM) 6, Nr. 2 (17.07.2023): 154–65. http://dx.doi.org/10.26740/jrpipm.v6n2.p154-165.

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Geometry is one of the content of mathematics which in many studies is associated with students' thinking abilities, such as critical thinking and reasoning abilities or others..This study aims to conduct a systematic review of the geometry research in school for identifying the types of mathematical thinking and their interconnections. We searched the Scopus database for articles published from 2003 to 2023 using relevant keywords. We applied the PRISMA method to select and evaluate the studies or articles based on the empirical data. We retrieved and evaluated data from the studies on the various styles of mathematical thinking evolved. Out of 166 titles that were initially obtained, only 10 titles passed the five stages of the systematic review protocol process. We identified 10 types of mathematical thinking that were discussed in the context of learning geometry at school: Creative Mathematical Reasoning (CMR), Computational thinking, Geometric reasoning, Geometric thinking van hiele theory, Geometric thinking (3D geometric thinking with representations), 3D geometry thinking, Visuo spatial reasoning, Geometry Spatial Reasoning, mathematical creative reasoning (MCR), and Inductive reasoning. We also found some connections of literature between these types of mathematical thinking, such as CMR and MCR, Geometric reasoning and Geometric thinking, and Visuo spatial reasoning and Geometry Spatial Reasoning. This systematic review provides an overview of the current state of research on geometry and reasoning in school mathematics and reveals some gaps and directions for future study. It also has implications for teachers who want to enhance their students’ mathematical thinking skills in geometry by exposing them to different types of mathematical thinking and their connections.

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29

Datta, Sambit, und David Beynon. „A Computational Approach to the Reconstruction of Surface Geometry from Early Temple Superstructures“. International Journal of Architectural Computing 3, Nr. 4 (Dezember 2005): 471–86. http://dx.doi.org/10.1260/147807705777781068.

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Recovering the control or implicit geometry underlying temple architecture requires bringing together fragments of evidence from field measurements, relating these to mathematical and geometric descriptions in canonical texts and proposing “best-fit” constructive models. While scholars in the field have traditionally used manual methods, the innovative application of niche computational techniques can help extend the study of artefact geometry. This paper demonstrates the application of a hybrid computational approach to the problem of recovering the surface geometry of early temple superstructures. The approach combines field measurements of temples, close-range architectural photogrammetry, rule-based generation and parametric modelling. The computing of surface geometry comprises a rule-based global model governing the overall form of the superstructure, several local models for individual motifs using photogrammetry and an intermediate geometry model that combines the two. To explain the technique and the different models, the paper examines an illustrative example of surface geometry reconstruction based on studies undertaken on a tenth century stone superstructure from western India. The example demonstrates that a combination of computational methods yields sophisticated models of the constructive geometry underlying temple form and that these digital artefacts can form the basis for in depth comparative analysis of temples, arising out of similar techniques, spread over geography, culture and time.

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30

JIANG, D., und N. F. STEWART. „FLOATING-POINT ARITHMETIC FOR COMPUTATIONAL GEOMETRY PROBLEMS WITH UNCERTAIN DATA“. International Journal of Computational Geometry & Applications 19, Nr. 04 (August 2009): 371–85. http://dx.doi.org/10.1142/s0218195909003015.

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It has been suggested in the literature that ordinary finite-precision floating-point arithmetic is inadequate for geometric computation, and that researchers in numerical analysis may believe that the difficulties of error in geometric computation can be overcome by simple approaches. It is the purpose of this paper to show that these suggestions, based on an example showing failure of a certain algorithm for computing planar convex hulls, are misleading, and why this is so. It is first shown how the now-classical backward error analysis can be applied in the area of computational geometry. This analysis is relevant in the context of uncertain data, which may well be the practical context for computational-geometry algorithms such as, say, those for computing convex hulls. The exposition will illustrate the fact that the backward error analysis does not pretend to overcome the problem of finite precision: it merely provides a way to distinguish those algorithms that overcome the problem to whatever extent it is possible to do so. It is then shown that often the situation in computational geometry is exactly parallel to other areas, such as the numerical solution of linear equations, or the algebraic eigenvalue problem. Indeed, the example mentioned can be viewed simply as an example of the use of an unstable algorithm, for a problem for which computational geometry has already discovered provably stable algorithms. Finally, the paper discusses the implications of these analyses for applications in three-dimensional solid modeling. This is done by considering a problem defined in terms of a simple extension of the planar convex-hull algorithm, namely, the verification of the well-formedness of extruded objects. A brief discussion concerning more difficult problems in solid modeling is also included.

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31

Mike, Joshua, Colin D. Sumrall, Vasileios Maroulas und Fernando Schwartz. „Nonlandmark classification in paleobiology: computational geometry as a tool for species discrimination“. Paleobiology 42, Nr. 4 (18.05.2016): 696–706. http://dx.doi.org/10.1017/pab.2016.19.

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AbstractOne important and sometimes contentious challenge in paleobiology is discriminating between species, which is increasingly accomplished by comparing specimen shape. While lengths and proportions are needed to achieve this task, finer geometric information, such as concavity, convexity, and curvature, plays a crucial role in the undertaking. Nonetheless, standard morphometric methodologies such as landmark analysis are not able to capture in a quantitative way these features and other important fine-scale geometric notions.Here we develop and implement state-of-the-art techniques from the emerging field of computational geometry to tackle this problem with the Mississippian blastoid Pentremites. We adapt a previously known computational framework to produce a measure of dissimilarity between shapes. More precisely, we compute “distances” between pairs of 3D surface scans of specimens by comparing a mix of global and fine-scale geometric measurements. This process uses the 3D scan of a specimen as a whole piece of data incorporating complete geometric information about the shape; as a result, scans used must accurately reflect the geometry of whole, undamaged, undeformed specimens. Using this information we are able to represent these data in clusters and ultimately reproduce and refine results obtained in previous work on species discrimination. Our methodology is landmark free, and therefore faster and less prone to human error than previous landmark-based methodologies.

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32

Wyk, Christopher J. Van, und Joseph O'Rourke. „Computational Geometry in C.“ Mathematics of Computation 64, Nr. 210 (April 1995): 894. http://dx.doi.org/10.2307/2153463.

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33

O'Rourke, Joseph. „Computational geometry column 46“. ACM SIGACT News 35, Nr. 3 (September 2004): 42–45. http://dx.doi.org/10.1145/1027914.1027926.

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34

Dumitrescu, Adrian. „Computational Geometry Column 64“. ACM SIGACT News 47, Nr. 4 (08.12.2016): 44–47. http://dx.doi.org/10.1145/3023855.3023868.

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35

O'Rourke, Joseph. „Computational geometry column 49“. ACM SIGACT News 38, Nr. 2 (Juni 2007): 51–55. http://dx.doi.org/10.1145/1272729.1272740.

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36

Dumitrescu, Adrian, und Csaba D. Tóth. „Computational geometry column 54“. ACM SIGACT News 43, Nr. 4 (19.12.2012): 90–97. http://dx.doi.org/10.1145/2421119.2421136.

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37

Demaine, Erik D., und Joseph O'Rourke. „Computational geometry column 37“. ACM SIGACT News 30, Nr. 3 (September 1999): 39–42. http://dx.doi.org/10.1145/333623.333625.

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38

O'Rourke, Joseph. „Computational geometry column 36“. ACM SIGACT News 30, Nr. 3 (September 1999): 35–38. http://dx.doi.org/10.1145/333623.335719.

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39

O'Rourke, Joseph. „Computational geometry column 38“. ACM SIGACT News 31, Nr. 1 (März 2000): 28–30. http://dx.doi.org/10.1145/346048.346050.

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40

O'Rourke, Joseph. „Computational geometry column 31“. ACM SIGACT News 28, Nr. 2 (Juni 1997): 20–23. http://dx.doi.org/10.1145/261342.261348.

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41

O'Rourke, Joseph. „Computational geometry column 32“. ACM SIGACT News 28, Nr. 3 (September 1997): 12–16. http://dx.doi.org/10.1145/262301.262303.

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42

Dumitrescu, Adrian, und Minghui Jiang. „Computational geometry column 56“. ACM SIGACT News 44, Nr. 2 (03.06.2013): 80–87. http://dx.doi.org/10.1145/2491533.2491550.

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43

O'Rourke, Joseph. „Computational geometry column 27“. ACM SIGACT News 26, Nr. 4 (Dezember 1995): 19–21. http://dx.doi.org/10.1145/219817.219826.

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44

Dumitrescu, Adrian, und Minghui Jiang. „Computational geometry column 58“. ACM SIGACT News 44, Nr. 4 (10.12.2013): 73–78. http://dx.doi.org/10.1145/2556663.2556679.

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45

O'Rourke, Joseph, und A. John Mallinckrodt. „Computational Geometry in C“. Computers in Physics 9, Nr. 1 (1995): 55. http://dx.doi.org/10.1063/1.4823371.

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46

Venkatasubramanian, Suresh. „Computational geometry column 55“. ACM SIGACT News 44, Nr. 1 (06.03.2013): 70–78. http://dx.doi.org/10.1145/2447712.2447732.

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47

Elbassioni, Khaled, und Adrian Dumitrescu. „Computational Geometry Column 66“. ACM SIGACT News 48, Nr. 4 (13.12.2017): 57–74. http://dx.doi.org/10.1145/3173127.3173138.

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48

O'Rourke, Joseph. „Computational Geometry Column 24“. ACM SIGACT News 25, Nr. 4 (Dezember 1994): 12–14. http://dx.doi.org/10.1145/190616.993026.

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49

Zhu, Binhai. „Computational Geometry Column 70“. ACM SIGACT News 51, Nr. 1 (12.03.2020): 105–17. http://dx.doi.org/10.1145/3388392.3388404.

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50

O'Rourke, Joseph. „Computational geometry column 23“. ACM SIGACT News 25, Nr. 3 (September 1994): 24–27. http://dx.doi.org/10.1145/193820.193831.

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