Littérature scientifique sur le sujet « Data projection »
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Articles de revues sur le sujet "Data projection"
Tejada, Eduardo, Rosane Minghim et Luis Gustavo Nonato. « On Improved Projection Techniques to Support Visual Exploration of Multi-Dimensional Data Sets ». Information Visualization 2, no 4 (décembre 2003) : 218–31. http://dx.doi.org/10.1057/palgrave.ivs.9500054.
Texte intégralRaymer, James, Nicholas Biddle et Qing Guan. « A multiregional sources of growth model for school enrolment projections ». Australian Population Studies 1, no 1 (19 novembre 2017) : 26–40. http://dx.doi.org/10.37970/aps.v1i1.10.
Texte intégralLehmann, Dirk J., et Holger Theisel. « General Projective Maps for Multidimensional Data Projection ». Computer Graphics Forum 35, no 2 (mai 2016) : 443–53. http://dx.doi.org/10.1111/cgf.12845.
Texte intégralVlassis, Nikos, Yoichi Motomura et Ben Kröse. « Supervised Dimension Reduction of Intrinsically Low-Dimensional Data ». Neural Computation 14, no 1 (1 janvier 2002) : 191–215. http://dx.doi.org/10.1162/089976602753284491.
Texte intégralKessler, Fritz. « Map Projection Education in General Cartography Textbooks : A Content Analysis ». Cartographic Perspectives, no 90 (16 août 2018) : 6–30. http://dx.doi.org/10.14714/cp90.1449.
Texte intégralSchreck, Tobias, Tatiana von Landesberger et Sebastian Bremm. « Techniques for Precision-Based Visual Analysis of Projected Data ». Information Visualization 9, no 3 (septembre 2010) : 181–93. http://dx.doi.org/10.1057/ivs.2010.2.
Texte intégralKhaIiI Ibrahim Kadhim. « Principal Components Analysis as enhancement Operator and Compression factor ». journal of the college of basic education 17, no 72 (17 juin 2019) : 25–33. http://dx.doi.org/10.35950/cbej.v17i72.4495.
Texte intégralSpur, M., V. Tourre, G. Moreau et P. Le Callet. « VIRTUAL DATA SPHERE : INVERSE STEREOGRAPHIC PROJECTION FOR IMMERSIVE MULTI-PERSPECTIVE GEOVISUALIZATION ». ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (18 mai 2022) : 235–42. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-235-2022.
Texte intégralChen, Shukun, Winfred Wenhui Xuan et Wei Yu. « Beyond Reporting Verbs : Exploring Chinese EFL Learners’ Deployment of Projection in Summary Writing ». SAGE Open 12, no 2 (avril 2022) : 215824402210933. http://dx.doi.org/10.1177/21582440221093356.
Texte intégralChen, Shukun, Winfred Wenhui Xuan et Wei Yu. « Beyond Reporting Verbs : Exploring Chinese EFL Learners’ Deployment of Projection in Summary Writing ». SAGE Open 12, no 2 (avril 2022) : 215824402210933. http://dx.doi.org/10.1177/21582440221093356.
Texte intégralThèses sur le sujet "Data projection"
McWilliams, Brian Victor Parulian. « Projection based models for high dimensional data ». Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/9577.
Texte intégralSibley, Christy N. « Analyzing Navy Officer Inventory Projection Using Data Farming ». Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/6868.
Texte intégralThe Navys Strategic Planning and Analysis Directorate (OPNAV N14) uses a complex model to project officer status in the coming years. The Officer Strategic Analysis Model (OSAM) projects officer status using an initial inventory, historical loss rates, and dependent functions for accessions, losses, lateral transfers, and promotions that reflect Navy policy and U.S. law. OSAM is a tool for informing decision makers as they consider potential policy changes, or analyze the impact of policy changes already in place, by generating Navy Officer inventory projections for a specified time horizon. This research explores applications of data farming for potential improvement of OSAM. An analysis of OSAM inventory forecast variations over a large number of scenarios while changing multiple input parameters enables assessment of key inputs. This research explores OSAM through applying the principles of design of experiments, regression modeling, and nonlinear programming. The objectives of this portion of the work include identifying critical parameters, determining a suitable measure of effectiveness, assessing model sensitivities, evaluating performance across a spectrum of loss adjustment factors, and determining appropriate values of key model inputs for future use in forecasting Navy officer inventory.
Eslava-Gomez, Guillermina. « Projection pursuit and other graphical methods for multivariate data ». Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.236118.
Texte intégralEbert, Matthias. « Non-ideal projection data in X-ray computed tomography ». [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10605022.
Texte intégralCropanese, Frank C. « Synthesis of low k1 projection lithography utilizing interferometry / ». Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1235.
Texte intégralFolgieri, R. « Ensembles based on Random Projection for gene expression data analysis ». Doctoral thesis, Università degli Studi di Milano, 2008. http://hdl.handle.net/2434/45878.
Texte intégralBolton, Richard John. « Multivariate analysis of multiproduct market research data ». Thesis, University of Exeter, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302542.
Texte intégralKishimoto, Paul Natsuo. « Transport demand in China : estimation, projection, and policy assessment ». Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120664.
Texte intégralCataloged from PDF version of thesis. "Some pages in the original document contain text that runs off the edge of the page"--Disclaimer Notice page.
Includes bibliographical references.
China's rapid economic growth in the twenty-first century has driven, and been driven by, concomitant motorization and growth of passenger and freight mobility, leading to greater energy demand and environmental impacts. In this dissertation I develop methods to characterize the evolution of passenger transport demand in a rapidly-developing country, in order to support projection and policy assessment. In Essay #1, I study the role that vehicle tailpipe and fuel quality standards ("emissions standards") can play vis-à-vis economy-wide carbon pricing in reducing emissions of pollutants that lead to poor air quality. I extend a global, computable general equilibrium (CGE) model resolving 30 Chinese provinces by separating freight and passenger transport subsectors, road and non-road modes, and household-owned vehicles; and then linking energy demand in these subsectors to a province-level inventory of primary pollutant emissions and future policy targets. While climate policy yields an air quality co-benefit by inducing shifts away from dirtier fuels, this effect is weak within the transport sector. Current emissions standards can drastically reduce transportation emissions, but their overall impact is limited by transport's share in total emissions, which varies across provinces. I conclude that the two categories of measures examined are complementary, and the effectiveness of emissions standards relies on enforcement in removing older, higher-polluting vehicles from the roads. In Essay #2, I characterize Chinese households' demand for transport by estimating the recently-developed, Exact affine Stone index (EASI) demand system on publicly-available data from non-governmental, social surveys. Flexible, EASI demands are particularly useful in China's rapidly-changing economy and transport system, because they capture ways that income elasticities of demand, and household transport budgets, vary with incomes; with population and road network densities; and with the supply of alternative transport modes. I find transport demand to be highly elastic ([epsilon][subscript x] = 1.46) at low incomes, and that income-elasticity of demand declines but remains greater than unity as incomes rise, so that the share of transport in households' spending rises monotonically from 1.6 % to 7.5 %; a wider, yet lower range than in some previous estimates. While no strong effects of city-level factors are identified, these and other non-income effects account for a larger portion of budget share changes than rising incomes. Finally, in Essay #3, I evaluate the predictive performance of the EASI demand system, by testing the sensitivity of model fit to the data available for estimation, in comparison with the less flexible, but widely used, Almost Ideal demand system (AIDS). In rapidly-evolving countries such as China, survey data without nationwide coverage can be used to characterize transport systems, but the omission of cities and provinces could bias results. To examine this possibility, I estimate demand systems on data subsets and test their predictions against observations for the withheld fraction. I find that simple EASI specifications slightly outperform AIDS under cross-validation; these offer a ready replacement in standalone and CGE applications. However, a trade-off exists between accuracy and the inclusion of policy-relevant covariates when data omit areas with high values of these variables. Also, while province-level fixed-effects control for unobserved heterogeneity across units that may bias parameter estimates, they increase prediction error in out-of-sample applications-revealing that the influence of local conditions on household transport expenditure varies significantly across China's provinces. The results motivate targeted transport data collection that better spans variation on city types and attributes; and the validation technique aids transport modelers in designing and validating demand specifications for projection and assessment.
by Paul Natsuo Kishimoto.
Ph. D. in Engineering Systems
Divak, Martin. « Simulated SAR with GIS data and pose estimation using affine projection ». Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-66303.
Texte intégralGentle, David John. « Tomographic image reconstruction from incomplete projection data with application to industry ». Thesis, University of Surrey, 1990. http://epubs.surrey.ac.uk/842931/.
Texte intégralLivres sur le sujet "Data projection"
Snyder, John Parr. Computer-assisted map projection research. Alexandria, VA : Dept. of the Interior, U.S. Geological Survey, 1985.
Trouver le texte intégralSnyder, John Parr. Computer-assisted map projection research. Alexandria, VA : Dept. of the Interior, U.S. Geological Survey, 1985.
Trouver le texte intégralMap projections : Georeferencing spatial data. Redlands, CA : Environmental Systems Research Institute, 1994.
Trouver le texte intégralGeological Survey (U.S.), dir. Plotting azimuthal stress data on standard map projections using Geoplot. [Reston, Va.?] : U.S. Dept. of the Interior, Geological Survey, 1986.
Trouver le texte intégralPadó, Sebastian. Cross-lingual annotation projection models for role-semantic information. Saarbrücken : Saarland University, 2007.
Trouver le texte intégralKnapp, David. BOREAS soils data over the SSA in raster format and AEAC projection. Greenbelt, Md : National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.
Trouver le texte intégralGolubyatnikov, V. P. Uniqueness questions in reconstruction of multidimensional objects from tomography-type projection data. Utrecht : VSP, 2000.
Trouver le texte intégralSteve, Kopp, et Environmental Systems Research Institute (Redlands, Calif.), dir. Understanding map projections : GIS by ESRI. Redlands, CA : ESRI, 2000.
Trouver le texte intégralW, Crockett Thomas, et Langley Research Center, dir. A scalable parallel cell-projection volume rendering algorithm for three-dimensional unstructured data. Hampton, Va : National Aeronautics and Space Administration, Langley Research Center, 1997.
Trouver le texte intégralW, Crockett Thomas, et Langley Research Center, dir. A scalable parallel cell-projection volume rendering algorithm for three-dimensional unstructured data. Hampton, Va : National Aeronautics and Space Administration, Langley Research Center, 1997.
Trouver le texte intégralChapitres de livres sur le sujet "Data projection"
Anderson, Alan J. B. « Population projection ». Dans Interpreting Data, 139–43. Boston, MA : Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-3192-4_11.
Texte intégralWang, Jianzhong. « Random Projection ». Dans Geometric Structure of High-Dimensional Data and Dimensionality Reduction, 131–48. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27497-8_7.
Texte intégralSánchez Gassen, Nora E. « Base-year data projection ». Dans Germany’s future electors, 169–80. Wiesbaden : Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06942-1_5.
Texte intégralLin, Binbin, Chiyuan Zhang et Xiaofei He. « Orthogonal Projection Analysis ». Dans Intelligent Science and Intelligent Data Engineering, 1–8. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31919-8_1.
Texte intégralGinsburg, Seymour, et Chang-jie Tang. « Projection of Object Histories ». Dans Foundations of Data Organization, 345–58. Boston, MA : Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1881-1_28.
Texte intégralLê Cao, Kim-Anh, et Zoe Marie Welham. « Projection to latent structures ». Dans Multivariate Data Integration Using R, 47–58. Boca Raton : Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003026860-7.
Texte intégralDiaconis, Persi, et Julia Salzman. « Projection pursuit for discrete data ». Dans Institute of Mathematical Statistics Collections, 265–88. Beachwood, Ohio, USA : Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/193940307000000482.
Texte intégralKhare, Kedar. « Image Reconstruction from Projection Data ». Dans Fourier Optics and Computational Imaging, 285–92. Chichester, UK : John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118900352.ch21.
Texte intégralWilson, Tom, Jeromey Temple, Peter McDonald, Ariane Utomo et Bianca Brijnath. « Projection Methods, Data and Assumptions ». Dans The Changing Migrant Composition of Australia’s Population, 11–22. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88939-5_3.
Texte intégralLê Cao, Kim-Anh, et Zoe Marie Welham. « Projection to Latent Structure (PLS) ». Dans Multivariate Data Integration Using R, 137–76. Boca Raton : Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003026860-13.
Texte intégralActes de conférences sur le sujet "Data projection"
Xiang-yang, Yang, Wu Min-shian et Chin Kuo-fan. « Measuring Two Dimensional OTF Applying CT Principle ». Dans Optical Data Storage. Washington, D.C. : Optica Publishing Group, 1985. http://dx.doi.org/10.1364/ods.1985.thdd3.
Texte intégralPerez-Gonzalez, F., et F. Balado. « Quantized projection data hiding ». Dans Proceedings of ICIP 2002 International Conference on Image Processing. IEEE, 2002. http://dx.doi.org/10.1109/icip.2002.1040094.
Texte intégralTasoulis, Sotiris, Lu Cheng, Niko Valimaki, Nicholas J. Croucher, Simon R. Harris, William P. Hanage, Teemu Roos et Jukka Corander. « Random projection based clustering for population genomics ». Dans 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004291.
Texte intégralKapp, Oscar H., et Chin-Tu Chen. « Reconstruction from limited projection data ». Dans SPIE/IS&T 1992 Symposium on Electronic Imaging : Science and Technology, sous la direction de Raj S. Acharya, Carol J. Cogswell et Dmitry B. Goldgof. SPIE, 1992. http://dx.doi.org/10.1117/12.59533.
Texte intégralYan, Donghui, Yingjie Wang, Jin Wang, Honggang Wang et Zhenpeng Li. « K-nearest Neighbor Search by Random Projection Forests ». Dans 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622307.
Texte intégralStrauch, George E., Jiajian Jax Lin et Jelena Tesic. « Overhead Projection Approach For Multi-Camera Vessel Activity Recognition ». Dans 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671274.
Texte intégralCardoso Braga, Daniel, Mohammadreza Kamyab, Brian Harclerode et Deep Joshi. « Combining Live Drilling Data Stream with a Cloud Data Analytics Pipeline to Perform Real-Time Automated Projections to the Bit ». Dans SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204065-ms.
Texte intégralSegoufin, Luc, et Victor Vianu. « Projection Views of Register Automata ». Dans SIGMOD/PODS '20 : International Conference on Management of Data. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3375395.3387651.
Texte intégralBajcsy, Peter, Antoine Vandecreme et Mary Brady. « Re-projection of terabyte-sized images ». Dans 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691786.
Texte intégralCarraher, Lee A., Philip A. Wilsey, Anindya Moitra et Sayantan Dey. « Random Projection Clustering on Streaming Data ». Dans 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0105.
Texte intégralRapports d'organisations sur le sujet "Data projection"
Dahlke, Garland R. Revalidation of a REA, IMF and BF Projection Model Using Real-time Ultrasound Imaging and Feeding Data in Cattle. Ames (Iowa) : Iowa State University, janvier 2012. http://dx.doi.org/10.31274/ans_air-180814-111.
Texte intégralVerbrugge, Randal J., et Saeed Zaman. Post-COVID Inflation Dynamics : Higher for Longer. Federal Reserve Bank of Cleveland, janvier 2023. http://dx.doi.org/10.26509/frbc-wp-202306.
Texte intégralRofman, Rafael, Joaquín Baliña et Emanuel López. Evaluating the Impact of COVID-19 on Pension Systems in Latin America and the Caribbean. The Case of Argentina. Inter-American Development Bank, octobre 2022. http://dx.doi.org/10.18235/0004508.
Texte intégralHirst, E. Data and projections on US electric-utility DSM programs : 1989--1997. Office of Scientific and Technical Information (OSTI), décembre 1994. http://dx.doi.org/10.2172/10180552.
Texte intégralHoa T. Nguyen, Daithi Stone et E. Wes Bethel. Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis. Office of Scientific and Technical Information (OSTI), janvier 2016. http://dx.doi.org/10.2172/1235087.
Texte intégralGarner, James M., Michael Maher et Michael A. Minnicino. Free Fall Experimental Data for Non-Lethal Artillery Projectile Parts. Fort Belvoir, VA : Defense Technical Information Center, septembre 2004. http://dx.doi.org/10.21236/ada426567.
Texte intégralCooper, Gene R., et Kevin S. Fansler. Comparison of Meteorological Data With Fitted Values Extracted from Projectile Trajectory. Fort Belvoir, VA : Defense Technical Information Center, octobre 1994. http://dx.doi.org/10.21236/ada285921.
Texte intégralRoberts, Neal P. Ballistic Analysis of Firing Table Data for 155MM, M825 Smoke Projectile. Fort Belvoir, VA : Defense Technical Information Center, septembre 1990. http://dx.doi.org/10.21236/ada228776.
Texte intégralCowell, Chandler, Michael P. Gallaher, Justin Larson et Aaron Schwartz. The Potential for SolarPowered Groundwater Irrigation in Sub-Saharan Africa : An Exploratory Analysis. RTI Press, novembre 2022. http://dx.doi.org/10.3768/rtipress.2022.op.0079.2211.
Texte intégralNaguib, Costanza, Martino Pelli, David Poirier et Jeanne Tschopp. The Impact of Cyclones on Local Economic Growth : Evidence from Local Projections. CIRANO, août 2022. http://dx.doi.org/10.54932/xvof3031.
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