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Journal articles on the topic '3D data analysi'

Author: Grafiati

Published: 10 March 2023

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1

Browning, Paul. "MACSPIN: 3D DATA ANALYSIS SOFTWARE." Terra Nova 4, no. 6 (1992): 701–4. http://dx.doi.org/10.1111/j.1365-3121.1992.tb00620.x.

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Wu, Youping, and Zhihui Zhou. "Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm." Journal of Function Spaces 2022 (July 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/6135829.

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With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquis

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3

Wang, Cuixia. "Optimization of Three-Dimensional Model of Landscape Space Based on Big Data Analysis." Journal of Function Spaces 2022 (August 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/7002983.

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Based on virtual reality technology, landscape 3D modeling provides users with the possibility to construct a simulated garden landscape environment design effect online, so it has high requirements for accuracy. With the continuous improvement of precision requirements, the number of people involved in the construction of 3D models is also increasing, which puts forward higher requirements for modeling. Based on this, this paper studies the optimization strategy of landscape space 3D model based on big data analysis. Based on the analysis of the establishment of the 3D model and the related a

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Deighton, M., and M. Petrou. "Data mining for large scale 3D seismic data analysis." Machine Vision and Applications 20, no. 1 (2007): 11–22. http://dx.doi.org/10.1007/s00138-007-0101-3.

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5

Li, W., S. Zlatanova, and B. Gorte. "VOXEL DATA MANAGEMENT AND ANALYSIS IN POSTGRESQL/POSTGIS UNDER DIFFERENT DATA LAYOUTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences VI-3/W1-2020 (November 17, 2020): 35–42. http://dx.doi.org/10.5194/isprs-annals-vi-3-w1-2020-35-2020.

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Abstract. Three-dimensional (3D) raster data (also named voxel) is important sources for 3D geo-information applications, which have long been used for modelling continuous phenomena such as geological and medical objects. Our world can be represented in voxels by gridding the 3D space and specifying what each grid represents by attaching every voxel to a real-world object. Nature-triggered disasters can also be modelled in volumetric representation. Unlike point cloud, it is still a lack of wide research on how to efficiently store and manage such semantic 3D raster data. In this work, we wou

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Gautier, J., S. Christophe, and M. Brédif. "VISUALIZING 3D CLIMATE DATA IN URBAN 3D MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 781–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-781-2020.

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Abstract. In order to understand and explain urban climate, the visual analysis of urban climate data and their relationships with the urban morphology is at stake. This involves partly to co-visualize 3D field climate data, obtained from simulation, with urban 3D models. We propose two ways to visualize and navigate into simulated climate data in urban 3D models, using series of horizontal 2D planes and 3D point clouds. We then explore different parameters regarding transparency, 3D semiologic rules, filtering and animation functions in order to improve the visual analysis of climate data 3D

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Barbu, Viorel, and Michael Röckner. "Global solutions to random 3D vorticity equations for small initial data." Journal of Differential Equations 263, no. 9 (2017): 5395–411. http://dx.doi.org/10.1016/j.jde.2017.06.020.

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8

Papatheodorou, Theodore, John Giannatsis, and Vassilis Dedoussis. "Evaluating 3D Printers Using Data Envelopment Analysis." Applied Sciences 11, no. 9 (2021): 4209. http://dx.doi.org/10.3390/app11094209.

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Data Envelopment Analysis (DEA) is an established powerful mathematical programming technique, which has been employed quite extensively for assessing the efficiency/performance of various physical or virtual and simple or complex production systems, as well as of consumer and industrial products and technologies. The purpose of the present study is to investigate whether DEA may be employed for evaluating the technical efficiency/performance of 3D printers, an advanced manufacturing technology of increasing importance for the manufacturing sector. For this purpose, a representative sample of

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Mery, Francisco, Carolina Méndez-Orellana, Javier Torres, et al. "3D simulation of aneurysm clipping: Data analysis." Data in Brief 37 (August 2021): 107258. http://dx.doi.org/10.1016/j.dib.2021.107258.

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Plyusnin, Ilya, Alistair R. Evans, Aleksis Karme, Aristides Gionis, and Jukka Jernvall. "Automated 3D Phenotype Analysis Using Data Mining." PLoS ONE 3, no. 3 (2008): e1742. http://dx.doi.org/10.1371/journal.pone.0001742.

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Simon, József, and Attila Felinger. "Correlation analysis on 3D data – Introducing the alteration analysis." Chemometrics and Intelligent Laboratory Systems 158 (November 2016): 54–60. http://dx.doi.org/10.1016/j.chemolab.2016.08.016.

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12

Ramm, A. G. "Stability of the inversion of 3D fixed-frequency scattering data." Journal of Mathematical Analysis and Applications 169, no. 2 (1992): 329–49. http://dx.doi.org/10.1016/0022-247x(92)90082-o.

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13

Shang, Jian, and Ru-liang Yang. "Data Processing and Typhoon 3D Data Analysis of TRMM Precipitation Radar." Journal of Electronics & Information Technology 30, no. 11 (2011): 2724–27. http://dx.doi.org/10.3724/sp.j.1146.2007.00670.

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14

Li, Ou. "TDA 3D Data Model and Feature Simplification Analysis." Journal of Physics: Conference Series 1883, no. 1 (2021): 012023. http://dx.doi.org/10.1088/1742-6596/1883/1/012023.

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Kovesi, Peter, Ben Richardson, Eun-Jung Holden, and Jeffrey Shragge. "Phase-Based Image Analysis of 3D Seismic Data." ASEG Extended Abstracts 2012, no. 1 (2012): 1–4. http://dx.doi.org/10.1071/aseg2012ab183.

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16

Fujishiro, I., Y. Takeshima, T. Azuma, and S. Takahashi. "Volume data mining using 3D field topology analysis." IEEE Computer Graphics and Applications 20, no. 5 (2000): 46–51. http://dx.doi.org/10.1109/38.865879.

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17

Salleh, S., U. Ujang, S. Azri, and T. L. Choon. "SPATIAL ADJACENCY ANALYSIS OF CITYGML BUILDINGS VIA 3D TOPOLOGICAL DATA STRUCTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 573–79. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-573-2019.

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Abstract. Adjacencies between objects provides the most basic connectivity information of objects. This connectivity information provides support for more complex 3D spatial analysis such as 3D navigation, nearest neighbour and others. In 3D models, the connectivity information is maintained by building a comprehensive 3D topology. As the international standard for 3D city models, CityGML employs a simple XML links mechanism that references related entities to each other as a means of maintaining topological information. This method fulfils the purpose of relating connected entities but, it do

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Wang, Jiangning, Jing Ren, Tianyu Xi, Siqin Ge, and Liqiang Ji. "Specifications and Standards for Insect 3D Data." Biodiversity Information Science and Standards 2 (May 21, 2018): e26561. http://dx.doi.org/10.3897/biss.2.26561.

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With the continuous development of imaging technology, the amount of insect 3D data is increasing, but research on data management is still virtually non-existent. This paper will discuss the specifications and standards relevant to the process of insect 3D data acquisition, processing and analysis. The collection of 3D data of insects includes specimen collection, sample preparation, image scanning specifications and 3D model specification. The specimen collection information uses existing biodiversity information standards such as Darwin Core. However, the 3D scanning process contains unique

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19

Bussetta, Philippe, Romain Boman, and Jean-Philippe Ponthot. "Efficient 3D data transfer operators based on numerical integration." International Journal for Numerical Methods in Engineering 102, no. 3-4 (2014): 892–929. http://dx.doi.org/10.1002/nme.4821.

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20

Bornik, Alexander, and Wolfgang Neubauer. "3D Visualization Techniques for Analysis and Archaeological Interpretation of GPR Data." Remote Sensing 14, no. 7 (2022): 1709. http://dx.doi.org/10.3390/rs14071709.

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The non-invasive detection and digital documentation of buried archaeological heritage by means of geophysical prospection is increasingly gaining importance in modern field archaeology and archaeological heritage management. It frequently provides the detailed information required for heritage protection or targeted further archaeological research. High-resolution magnetometry and ground-penetrating radar (GPR) became invaluable tools for the efficient and comprehensive non-invasive exploration of complete archaeological sites and archaeological landscapes. The analysis and detailed archaeolo

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21

Darrah, Marjorie, Matthew Richardson, Bradley DeRoos, and Mitchell Wathen. "Optimal LiDAR Data Resolution Analysis for Object Classification." Sensors 22, no. 14 (2022): 5152. http://dx.doi.org/10.3390/s22145152.

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When classifying objects in 3D LiDAR data, it is important to use efficient collection methods and processing algorithms. This paper considers the resolution needed to classify 3D objects accurately and discusses how this resolution is accomplished for the RedTail RTL-450 LiDAR System. We employ VoxNet, a convolutional neural network, to classify the 3D data and test the accuracy using different data resolution levels. The results show that for our data set, if the neural network is trained using higher resolution data, then the accuracy of the classification is above 97%, even for the very sp

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22

Wang, Qing Guo. "A 3D Surface Data Model for Fast Visualization of 3DCM." Advanced Materials Research 594-597 (November 2012): 2351–55. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.2351.

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3D data model is an indispensable component to any 3D GIS, and forms the basis of 3D spatial analysis and representation. At present, plenty of representative 3D data models are proposed. However, existing models neglect the display result and the consumption of storage space. Based on the analysis of existing 3D GIS data model, a 3D surface model is proposed for fast visualization in this paper, which is composed of node, segment and triangle. The data structure and formal representation of the proposed 3D surface model is developed to organize and store data of 3D model. Finally, an experime

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23

Andronov, Leonid, Jonathan Michalon, Khalid Ouararhni, et al. "3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations." Bioinformatics 34, no. 17 (2018): 3004–12. http://dx.doi.org/10.1093/bioinformatics/bty200.

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24

Ridzuan, N., U. Ujang, S. Azri, I. Mohamad Yusoff, and T. L. Choon. "VOXELIZATION TECHNIQUES: DATA SEGMENTATION AND DATA MODELLING FOR 3D BUILDING MODELS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 149–55. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-149-2022.

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Abstract. Voxelization of data is discretizing the 3D space, in which the simplest form is a single voxel. There is a large number of publications that are related to voxelization. However, this paper focuses on the voxelization technique implemented in 3D building modelling. This paper aims to get the development idea of the voxelization technique throughout these past years to determine the suitable technique and method for including a 3D voxelized building model in Computational Fluid Dynamics (CFD). From the search and analysis, it is found that this technique is not only related to data m

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25

Holland, Stephen, Chevonne McInnis, Rafael Radkowski, and Adarsh Krishnamurthy. "NDE Data Analysis and Modeling in 3D CAD Context." Materials Evaluation 78, no. 1 (2020): 95–103. http://dx.doi.org/10.32548/2020.me-04095.

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26

Knyaz, V., and A. Gaboutchian. "Automated Morphometric Analysis of 3D Data in Paleoanthropological Research." Nanobiotechnology Reports 16, no. 5 (2021): 668–75. http://dx.doi.org/10.1134/s2635167621050098.

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27

Klemm, Paul, Kai Lawonn, Sylvia Glaber, et al. "3D Regression Heat Map Analysis of Population Study Data." IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016): 81–90. http://dx.doi.org/10.1109/tvcg.2015.2468291.

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28

INOUE, Ryo, Naotaka SAKAI, and Satoshi SHIMAWAKI. "3D body image generation using ASCII motion analysis data." Proceedings of the JSME Conference on Frontiers in Bioengineering 2003.14 (2003): 143–44. http://dx.doi.org/10.1299/jsmebiofro.2003.14.143.

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29

Chopra, Satinder, and Vladimir Alexeev. "Applications of texture attribute analysis to 3D seismic data." Leading Edge 25, no. 8 (2006): 934–40. http://dx.doi.org/10.1190/1.2335155.

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30

Jung, Jaehoon, Michael J. Olsen, David S. Hurwitz, Alireza G. Kashani, and Kamilah Buker. "3D virtual intersection sight distance analysis using lidar data." Transportation Research Part C: Emerging Technologies 86 (January 2018): 563–79. http://dx.doi.org/10.1016/j.trc.2017.12.004.

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31

Sheng, Ya-Nan, Zhi-Chuan Guan, and Kai Wei. "Analysis of 3D formation pressure based on logging data." Journal of Petroleum Exploration and Production Technology 7, no. 2 (2016): 471–77. http://dx.doi.org/10.1007/s13202-016-0266-2.

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32

Fang, Daoyuan, and Bin Han. "GLOBAL SOLUTION FOR THE GENERALIZED ANISOTROPIC NAVIER–STOKES EQUATIONS WITH LARGE DATA." Mathematical Modelling and Analysis 20, no. 2 (2015): 205–31. http://dx.doi.org/10.3846/13926292.2015.1020894.

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We are concerned with 3D incompressible generalized anisotropic Navier– Stokes equations with hyperdissipative term in horizontal variables. We prove that there exists a unique global solution for it with large initial data in anisotropic Besov space.

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33

Mezghani, Neila, Rayan Soltana, Youssef Ouakrim, et al. "Healthy Knee Kinematic Phenotypes Identification Based on a Clustering Data Analysis." Applied Sciences 11, no. 24 (2021): 12054. http://dx.doi.org/10.3390/app112412054.

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The purpose of this study is to identify healthy phenotypes in knee kinematics based on clustering data analysis. Our analysis uses the 3D knee kinematics curves, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation, measured via a KneeKG™ system during a gait task. We investigated two data representation approaches that are based on the joint analysis of the three dimensions. The first is a global approach that is considered a concatenation of the kinematic data without any dimensionality reduction. The second is a local approach that is considered a set of 69

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34

Khayyal, Heba K., Zaki M. Zeidan, and Ashraf A. A. Beshr. "Creation and Spatial Analysis of 3D City Modeling based on GIS Data." Civil Engineering Journal 8, no. 1 (2022): 105–23. http://dx.doi.org/10.28991/cej-2022-08-01-08.

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The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data

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Rubel, O., G. H. Weber, Min-Yu Huang, et al. "Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 7, no. 1 (2010): 64–79. http://dx.doi.org/10.1109/tcbb.2008.49.

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36

Hou, Thomas Y., Zhen Lei, and Congming Li. "Global Regularity of the 3D Axi-Symmetric Navier–Stokes Equations with Anisotropic Data." Communications in Partial Differential Equations 33, no. 9 (2008): 1622–37. http://dx.doi.org/10.1080/03605300802108057.

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37

Brabant, Loes, Jelle Vlassenbroeck, Yoni De Witte, et al. "Three-Dimensional Analysis of High-Resolution X-Ray Computed Tomography Data with Morpho+." Microscopy and Microanalysis 17, no. 2 (2011): 252–63. http://dx.doi.org/10.1017/s1431927610094389.

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AbstractThree-dimensional (3D) analysis is an essential tool to obtain quantitative results from 3D datasets. Considerable progress has been made in 3D imaging techniques, resulting in a growing need for more flexible, complete analysis packages containing advanced algorithms. At the Centre for X-ray Tomography of the Ghent University (UGCT), research is being done on the improvement of both hardware and software for high-resolution X-ray computed tomography (CT). UGCT collaborates with research groups from different disciplines, each having specific needs. To meet these requirements the analy

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38

Wang, Yongxin, Fan Geng, and Shu Wang. "On the 3D Incompressible Boussinesq Equations in a Class of Variant Spherical Coordinates." Journal of Function Spaces 2022 (May 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/9121813.

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This paper investigates the global stabilizing effects of the geometry of the domain at which the flow locates and of the geometric structure of the solution to the incompressible flows by studying the three-dimensional (3D) incompressible, viscosity, and diffusivity Boussinesq system in spherical coordinates. We establish the global existence and uniqueness of the smooth solution to the Cauchy problem for a full 3D incompressible Boussinesq system in a class of variant spherical coordinates for a class of smooth large initial data. We also construct one class of nonempty bounded domains in th

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Prieto, Iñaki, Jose Luis Izkara, and Elena Usobiaga. "The Application of LiDAR Data for the Solar Potential Analysis Based on Urban 3D Model." Remote Sensing 11, no. 20 (2019): 2348. http://dx.doi.org/10.3390/rs11202348.

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Solar maps are becoming a popular resource and are available via the web to help plan investments for the benefits of renewable energy. These maps are especially useful when the results have high accuracy. LiDAR technology currently offers high-resolution data sources that are very suitable for obtaining an urban 3D geometry with high precision. Three-dimensional visualization also offers a more accurate and intuitive perspective of reality than 2D maps. This paper presents a new method for the calculation and visualization of the solar potential of building roofs on an urban 3D model, based o

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40

Cheng, Xiao Min, Zi Qing Ye, and Jian Jian Zhang. "Analysis and Reconstruct of Complex Data Based on 3D Laser Scanner." Advanced Materials Research 479-481 (February 2012): 2231–34. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.2231.

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Solve the problem of simplifying, aligning, and restoring while rebuilding a large number of point group data. First, scanning art work by using 3DFAMILY laser scanner and obtain the 3D data of its surface, then removing the desultory points on the boundary, using curvature simplification and point position alignment to revise data. Finally, fixing the loophole and modify the convex block partially and finishing up 3D reconstruction. We transform the reconstruction result into STL format file, and build the prototype of art work in Rapid Prototyping System.

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41

Madan, Christopher R. "Creating 3D visualizations of MRI data: A brief guide." F1000Research 4 (August 4, 2015): 466. http://dx.doi.org/10.12688/f1000research.6838.1.

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While magnetic resonance imaging (MRI) data is itself 3D, it is often difficult to adequately present the results papers and slides in 3D. As a result, findings of MRI studies are often presented in 2D instead. A solution is to create figures that include perspective and can convey 3D information; such figures can sometimes be produced by standard functional magnetic resonance imaging (fMRI) analysis packages and related specialty programs. However, many options cannot provide functionality such as visualizing activation clusters that are both cortical and subcortical (i.e., a 3D glass brain),

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42

Abdulkadhim Hameedi, Balsam, Muna Majeed Laftah, and Anwar Abbas Hattab. "Data Hiding in 3D-Medical Image." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 03 (2022): 72–88. http://dx.doi.org/10.3991/ijoe.v18i03.28007.

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Information hiding strategies have recently gained popularity in a variety of fields. Digital audio, video, and images are increasingly being labelled with distinct but undetectable marks that may contain a hidden copyright notice or serial number, or even directly help to prevent unauthorized duplication. This approach is extended to medical images by hiding secret information in them using the structure of a different file format. The hidden information may be related to the patient. In this paper, a method for hiding secret information in DICOM images is proposed based on Discrete Wavelet T

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Prasertsuk, Santirak, and Chawee Busayarat. "3D Data Visualization and Analysis Tools for AI Ready City: Space Syntax and Social Media Data." Nakhara : Journal of Environmental Design and Planning 21, no. 2 (2022): 214. http://dx.doi.org/10.54028/nj202221214.

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Space syntax is now widely accepted as a set of techniques that can be used to efficiently analyze spatial morphological structure at the city or community level. Segment analysis, a type of space syntax that is typically rendered through two-dimensional vector lines, can show the effectiveness of pedestrian and vehicular accesses to parts of a city. However, analysis of a city’s condition is far too diverse and complex for the use of space syntax alone. Other types of information, such as data from social media, can be integrated to determine and locate problems in the city, or to search for

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44

Abellera, Lourdes V. "K. Heather Kennedy: Introduction to 3D Data: Modeling with ArcGIS 3D Analyst and Google Earth." Mathematical Geosciences 44, no. 3 (2012): 375–77. http://dx.doi.org/10.1007/s11004-011-9381-2.

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Almac, Umut, Isıl Polat Pekmezci, and Metin Ahunbay. "Numerical Analysis of Historic Structural Elements Using 3D Point Cloud Data." Open Construction and Building Technology Journal 10, no. 1 (2016): 233–45. http://dx.doi.org/10.2174/1874836801610010233.

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The 3D laser scanner has become a common instrument in numerous field applications such as structural health monitoring, assessment and documentation of structural damages, volume and dimension control of excavations, geometrical recording of built environment, and construction progress monitoring in different fields. It enables capture of millions of points from the surface of objects with high accuracy and in a very short time. These points can be employed to extrapolate the shape of the elements. In this way, the collected data can be developed to construct three-dimensional digital models

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46

Gamey, T. J. "Collection and Analysis of 3D Magnetic Data for UXO Discrimination." Journal of Environmental & Engineering Geophysics 11, no. 3 (2006): 185–96. http://dx.doi.org/10.2113/jeeg11.3.185.

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47

Egurnov, D. A., and D. I. Ignatov. "Triclusters of Close Values for the Analysis of 3D Data." Automation and Remote Control 83, no. 6 (2022): 894–902. http://dx.doi.org/10.1134/s0005117922060078.

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48

Ferrando, L., I. Epifanio, and N. Ventura-Campos. "Ordinal classification of 3D brain structures by functional data analysis." Statistics & Probability Letters 179 (December 2021): 109227. http://dx.doi.org/10.1016/j.spl.2021.109227.

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Bouksim, Mohcine, Fatima Zakani, Khadija Arhid, Mohamed Aboulfatah, and Taoufiq Gadi. "New Approach for 3D Mesh Retrieval Using Data Envelopment Analysis." International Journal of Intelligent Engineering and Systems 11, no. 1 (2018): 1–10. http://dx.doi.org/10.22266/ijies2018.0228.01.

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Henderson, J., G. Paton, B. Froner, J. Lowell, and M. Ackers. "Integrating interpretation expertise and objective data analysis in 3D interpretation." Leading Edge 31, no. 11 (2012): 1374–81. http://dx.doi.org/10.1190/tle31111374.1.

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