Littérature scientifique sur le sujet « Geometric statistics »
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Articles de revues sur le sujet "Geometric statistics"
Berry, M. V., et Pragya Shukla. « Geometric Phase Curvature Statistics ». Journal of Statistical Physics 180, no 1-6 (9 octobre 2019) : 297–303. http://dx.doi.org/10.1007/s10955-019-02400-6.
Texte intégralConstantin, Peter. « Geometric Statistics in Turbulence ». SIAM Review 36, no 1 (mars 1994) : 73–98. http://dx.doi.org/10.1137/1036004.
Texte intégralDrew, Donald A. « Evolution of Geometric Statistics ». SIAM Journal on Applied Mathematics 50, no 3 (juin 1990) : 649–66. http://dx.doi.org/10.1137/0150038.
Texte intégralGrady, D. E., et M. E. Kipp. « Geometric statistics and dynamic fragmentation ». Journal of Applied Physics 58, no 3 (août 1985) : 1210–22. http://dx.doi.org/10.1063/1.336139.
Texte intégralTimonin, P. N. « Statistics of geometric clusters in Potts model : statistical mechanics approach ». Proceedings of the Royal Society A : Mathematical, Physical and Engineering Sciences 476, no 2240 (août 2020) : 20200215. http://dx.doi.org/10.1098/rspa.2020.0215.
Texte intégralTrofimov, V. K. « Encoding geometric sources with unknown statistics ». Herald of the Siberian State University of Telecommunications and Informatics, no 2 (18 juin 2021) : 79–87. http://dx.doi.org/10.55648/1998-6920-2021-15-2-79-87.
Texte intégralAnevski, Dragi, Christopher Genovese, Geurt Jongbloed et Wolfgang Polonik. « Statistics for Shape and Geometric Features ». Oberwolfach Reports 13, no 3 (2016) : 1821–74. http://dx.doi.org/10.4171/owr/2016/32.
Texte intégralFeragen, Aasa, Thomas Hotz, Stephan Huckemann et Ezra Miller. « Statistics for Data with Geometric Structure ». Oberwolfach Reports 15, no 1 (4 janvier 2019) : 125–86. http://dx.doi.org/10.4171/owr/2018/3.
Texte intégralANASTOPOULOS, CHARIS. « SPIN-STATISTICS THEOREM AND GEOMETRIC QUANTIZATION ». International Journal of Modern Physics A 19, no 05 (20 février 2004) : 655–76. http://dx.doi.org/10.1142/s0217751x04017860.
Texte intégralDettmann, C. P., O. Georgiou et G. Knight. « Spectral statistics of random geometric graphs ». EPL (Europhysics Letters) 118, no 1 (1 avril 2017) : 18003. http://dx.doi.org/10.1209/0295-5075/118/18003.
Texte intégralThèses sur le sujet "Geometric statistics"
Saive, Yannick. « DirCNN : Rotation Invariant Geometric Deep Learning ». Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252573.
Texte intégralNyligen har ämnet geometrisk deep learning presenterat ett nytt sätt för maskininlärningsalgoritmer att arbeta med punktmolnsdata i dess råa form.Banbrytande arkitekturer som PointNet och många andra som byggt på dennes framgång framhåller vikten av invarians under inledande datatransformationer. Sådana transformationer inkluderar skiftning, skalning och rotation av punktmoln i ett tredimensionellt rum. Precis som vi önskar att klassifierande maskininlärningsalgoritmer lyckas identifiera en uppochnedvänd hund som en hund vill vi att våra geometriska deep learning-modeller framgångsrikt ska kunna hantera transformerade punktmoln. Därför använder många modeller en inledande datatransformation som tränas som en del av ett neuralt nätverk för att transformera punktmoln till ett globalt kanoniskt rum. Jag ser tillkortakommanden i detta tillgångavägssätt eftersom invariansen är inte fullständigt garanterad, den är snarare approximativ. För att motverka detta föreslår jag en lokal deterministisk transformation som inte måste läras från datan. Det nya lagret i det här projektet bygger på Edge Convolutions och döps därför till DirEdgeConv, namnet tar den riktningsmässiga invariansen i åtanke. Lagret ändras en aning för att introducera ett nytt lager vid namn DirSplineConv. Dessa lager sätts ihop i olika modeller som sedan jämförs med sina efterföljare på samma uppgifter för att ge en rättvis grund för att jämföra dem. Resultaten är inte lika bra som toppmoderna resultat men de är ändå tillfredsställande. Jag tror även resultaten kan förbättas genom att förbättra inlärningshastigheten och dess schemaläggning. I ett experiment där ablation genomförs på de nya lagren ser vi att lagrens huvudkoncept förbättrar resultaten överlag.
Ho, Pak-kei. « Parametric and non-parametric inference for Geometric Process ». Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31483859.
Texte intégralHo, Pak-kei, et 何柏基. « Parametric and non-parametric inference for Geometric Process ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31483859.
Texte intégralKeil, Mitchel J. « Automatic generation of interference-free geometric models of spatial mechanisms ». Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-08252008-162631/.
Texte intégralSuttmiller, Alexander Gage. « Streamline Feature Detection : Geometric and Statistical Evaluation of Streamline Properties ». The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315967677.
Texte intégralSaha, Abhijoy. « A Geometric Framework for Modeling and Inference using the Nonparametric Fisher–Rao metric ». The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1562679374833421.
Texte intégralChu, Chi-Yang. « Applied Nonparametric Density and Regression Estimation with Discrete Data| Plug-In Bandwidth Selection and Non-Geometric Kernel Functions ». Thesis, The University of Alabama, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10262364.
Texte intégralBandwidth selection plays an important role in kernel density estimation. Least-squares cross-validation and plug-in methods are commonly used as bandwidth selectors for the continuous data setting. The former is a data-driven approach and the latter requires a priori assumptions about the unknown distribution of the data. A benefit from the plug-in method is its relatively quick computation and hence it is often used for preliminary analysis. However, we find that much less is known about the plug-in method in the discrete data setting and this motivates us to propose a plug-in bandwidth selector. A related issue is undersmoothing in kernel density estimation. Least-squares cross-validation is a popular bandwidth selector, but in many applied situations, it tends to select a relatively small bandwidth, or undersmooths. The literature suggests several methods to solve this problem, but most of them are the modifications of extant error criterions for continuous variables. Here we discuss this problem in the discrete data setting and propose non-geometric discrete kernel functions as a possible solution. This issue also occurs in kernel regression estimation. Our proposed bandwidth selector and kernel functions perform well in simulated and real data.
Carriere, Mathieu. « On Metric and Statistical Properties of Topological Descriptors for geometric Data ». Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS433/document.
Texte intégralIn the context of supervised Machine Learning, finding alternate representations, or descriptors, for data is of primary interest since it can greatly enhance the performance of algorithms. Among them, topological descriptors focus on and encode the topological information contained in geometric data. One advantage of using these descriptors is that they enjoy many good and desireable properties, due to their topological nature. For instance, they are invariant to continuous deformations of data. However, the main drawback of these descriptors is that they often lack the structure and operations required by most Machine Learning algorithms, such as a means or scalar products. In this thesis, we study the metric and statistical properties of the most common topological descriptors, the persistence diagrams and the Mappers. In particular, we show that the Mapper, which is empirically instable, can be stabilized with an appropriate metric, that we use later on to conpute confidence regions and automatic tuning of its parameters. Concerning persistence diagrams, we show that scalar products can be defined with kernel methods by defining two kernels, or embeddings, into finite and infinite dimensional Hilbert spaces
Pedersen, Morten Akhøj. « Méthodes riemanniennes et sous-riemanniennes pour la réduction de dimension ». Electronic Thesis or Diss., Université Côte d'Azur, 2023. http://www.theses.fr/2023COAZ4087.
Texte intégralIn this thesis, we propose new methods for dimension reduction based on differential geometry, that is, finding a representation of a set of observations in a space of lower dimension than the original data space. Methods for dimension reduction form a cornerstone of statistics, and thus have a very wide range of applications. For instance, a lower dimensional representation of a data set allows visualization and is often necessary for subsequent statistical analyses. In ordinary Euclidean statistics, the data belong to a vector space and the lower dimensional space might be a linear subspace or a non-linear submanifold approximating the observations. The study of such smooth manifolds, differential geometry, naturally plays an important role in this last case, or when the data space is itself a known manifold. Methods for analysing this type of data form the field of geometric statistics. In this setting, the approximating space found by dimension reduction is naturally a submanifold of the given manifold. The starting point of this thesis is geometric statistics for observations belonging to a known Riemannian manifold, but parts of our work form a contribution even in the case of data belonging to Euclidean space, mathbb{R}^d.An important example of manifold valued data is shapes, in our case discrete or continuous curves or surfaces. In evolutionary biology, researchers are interested in studying reasons for and implications of morphological differences between species. Shape is one way to formalize morphology. This application motivates the first main contribution of the thesis. We generalize a dimension reduction method used in evolutionary biology, phylogenetic principal component analysis (P-PCA), to work for data on a Riemannian manifold - so that it can be applied to shape data. P-PCA is a version of PCA for observations that are assumed to be leaf nodes of a phylogenetic tree. From a statistical point of view, the important property of such data is that the observations (leaf node values) are not necessarily independent. We define and estimate intrinsic weighted means and covariances on a manifold which takes the dependency of the observations into account. We then define phylogenetic PCA on a manifold to be the eigendecomposition of the weighted covariance in the tangent space of the weighted mean. We show that the mean estimator that is currently used in evolutionary biology for studying morphology corresponds to taking only a single step of our Riemannian gradient descent algorithm for the intrinsic mean, when the observations are represented in Kendall's shape space. Our second main contribution is a non-parametric method for dimension reduction that can be used for approximating a set of observations based on a very flexible class of submanifolds. This method is novel even in the case of Euclidean data. The method works by constructing a subbundle of the tangent bundle on the data manifold via local PCA. We call this subbundle the principal subbundle. We then observe that this subbundle induces a sub-Riemannian structure and we show that the resulting sub-Riemannian geodesics with respect to this structure stay close to the set of observations. Moreover, we show that sub-Riemannian geodesics starting from a given point locally generate a submanifold which is radially aligned with the estimated subbundle, even for non-integrable subbundles. Non-integrability is likely to occur when the subbundle is estimated from noisy data, and our method demonstrates that sub-Riemannian geometry is a natural framework for dealing which such problems. Numerical experiments illustrate the power of our framework by showing that we can achieve impressively large range reconstructions even in the presence of quite high levels of noise
I denne afhandling præsenteres nye metoder til dimensionsreduktion, baseret p˚adifferential geometri. Det vil sige metoder til at finde en repræsentation af et datasæti et rum af lavere dimension end det opringelige rum. S˚adanne metoder spiller enhelt central rolle i statistik, og har et meget bredt anvendelsesomr˚ade. En laveredimensionalrepræsentation af et datasæt tillader visualisering og er ofte nødvendigtfor efterfølgende statistisk analyse. I traditionel, Euklidisk statistik ligger observationernei et vektor rum, og det lavere-dimensionale rum kan være et lineært underrumeller en ikke-lineær undermangfoldighed som approksimerer observationerne.Studiet af s˚adanne glatte mangfoldigheder, differential geometri, spiller en vigtig rollei sidstnævnte tilfælde, eller hvis rummet hvori observationerne ligger i sig selv er enmangfoldighed. Metoder til at analysere observationer p˚a en mangfoldighed udgørfeltet geometrisk statistik. I denne kontekst er det approksimerende rum, fundetvia dimensionsreduktion, naturligt en submangfoldighed af den givne mangfoldighed.Udgangspunktet for denne afhandling er geometrisk statistik for observationer p˚a ena priori kendt Riemannsk mangfoldighed, men dele af vores arbejde udgør et bidragselv i tilfældet med observationer i Euklidisk rum, Rd.Et vigtigt eksempel p˚a data p˚a en mangfoldighed er former, i vores tilfældediskrete kurver eller overflader. I evolutionsbiologi er forskere interesseret i at studeregrunde til og implikationer af morfologiske forskelle mellem arter. Former er ´en m˚adeat formalisere morfologi p˚a. Denne anvendelse motiverer det første hovedbidrag idenne afhandling. We generaliserer en metode til dimensionsreduktion brugt i evolutionsbiologi,phylogenetisk principal component analysis (P-PCA), til at virke for datap˚a en Riemannsk mangfoldighed - s˚a den kan anvendes til observationer af former. PPCAer en version af PCA for observationer som antages at være de yderste knuder iet phylogenetisk træ. Fra et statistisk synspunkt er den vigtige egenskab ved s˚adanneobservationer at de ikke nødvendigvis er uafhængige. We definerer og estimerer intrinsiskevægtede middelværdier og kovarianser p˚a en mangfoldighed, som tager højde fors˚adanne observationers afhængighed. Vi definerer derefter phylogenetisk PCA p˚a enmangfoldighed som egendekomposition af den vægtede kovarians i tanget-rummet tilden vægtede middelværdi. Vi viser at estimatoren af middelværdien som pt. bruges ievolutionsbiologi til at studere morfologi svarer til at tage kun et enkelt skridt af voresRiemannske gradient descent algoritme for den intrinsiske middelværdi, n˚ar formernerepræsenteres i Kendall´s form-mangfoldighed.Vores andet hovedbidrag er en ikke-parametrisk metode til dimensionsreduktionsom kan bruges til at approksimere et data sæt baseret p˚a en meget flexibel klasse afsubmangfoldigheder. Denne metode er ny ogs˚a i tilfældet med Euklidisk data. Metodenvirker ved at konstruere et under-bundt af tangentbundet p˚a datamangfoldighedenM via lokale PCA´er. Vi kalder dette underbundt principal underbundtet. Viobserverer at dette underbundt inducerer en sub-Riemannsk struktur p˚a M og vi viserat sub-Riemannske geodæter fra et givent punkt lokalt genererer en submangfoldighedsom radialt flugter med det estimerede subbundt, selv for ikke-integrable subbundter.Ved støjfyldt data forekommer ikke-integrabilitet med stor sandsynlighed, og voresmetode demonstrerer at sub-Riemannsk geometri er en naturlig tilgang til at h˚andteredette. Numeriske eksperimenter illustrerer styrkerne ved metoden ved at vise at denopn˚ar rekonstruktioner over store afstande, selv under høje niveauer af støj
Neeser, Rudolph. « A Comparison of Statistical and Geometric Reconstruction Techniques : Guidelines for Correcting Fossil Hominin Crania ». Thesis, University of Cape Town, 2007. http://pubs.cs.uct.ac.za/archive/00000413/.
Texte intégralLivres sur le sujet "Geometric statistics"
Gibilisco, Paolo, Eva Riccomagno, Maria Piera Rogantin et Henry P. Wynn, dir. Algebraic and Geometric Methods in Statistics. Cambridge : Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511642401.
Texte intégralCalin, Ovidiu, et Constantin Udrişte. Geometric Modeling in Probability and Statistics. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07779-6.
Texte intégralGibilisco, Paolo. Algebraic and geometric methods in statistics. Cambridge : Cambridge University Press, 2010.
Trouver le texte intégralRoux, Brigitte Le. Combinatorial Inference in Geometric Data Analysis. Boca Raton, Florida, USA : Chapman and Hall/CRC, Taylor & Francis Group, 2019.
Trouver le texte intégralKiêu, Kiên. Three lectures on systematic geometric sampling. Aarhus [Denmark] : Dept. of Theoretical Statistics, 1997.
Trouver le texte intégralV, Buldygin V., et Kharazishvili A. B, dir. Geometric aspects of probability theory and mathematical statistics. Dordrecht : Kluwer Academic, 2000.
Trouver le texte intégralBuldygin, V. V., et A. B. Kharazishvili. Geometric Aspects of Probability Theory and Mathematical Statistics. Dordrecht : Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-017-1687-1.
Texte intégralNATO Advanced Study on Propagation of Correlations in Constrained Systems (1990 Cargèse, France). Correlations and connectivity : Geometric aspects of physics, chemistry, and biology. Dordrecht : Kluwer Academic Publishers, 1990.
Trouver le texte intégralKanatani, Kenʼichi. Statistical optimization for geometric computation : Theory and practice. Amsterdam : Elsevier, 1996.
Trouver le texte intégralFang, Kʻai-tʻai. Number-theoretic methods in statistics. London : Chapman & Hall, 1994.
Trouver le texte intégralChapitres de livres sur le sujet "Geometric statistics"
Marshall, Albert W., Ingram Olkin et Barry C. Arnold. « Geometric Inequalities ». Dans Springer Series in Statistics, 269–96. New York, NY : Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-68276-1_8.
Texte intégralKühnel, Line, Tom Fletcher, Sarang Joshi et Stefan Sommer. « Latent Space Geometric Statistics ». Dans Pattern Recognition. ICPR International Workshops and Challenges, 163–78. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68780-9_16.
Texte intégralSaville, David J., et Graham R. Wood. « The Geometric Tool Kit ». Dans Springer Texts in Statistics, 10–38. New York, NY : Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-0971-3_2.
Texte intégralDorst, Leo, et Steven De Keninck. « Physical Geometry by Plane-Based Geometric Algebra ». Dans Springer Proceedings in Mathematics & ; Statistics, 43–76. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55985-3_2.
Texte intégralLeung, Kit-Nam. « Arithmetic and Geometric Processes ». Dans Springer Handbook of Engineering Statistics, 931–55. London : Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-288-1_49.
Texte intégralHitzer, Eckhard, et Dietmar Hildenbrand. « Introduction to Geometric Algebra ». Dans Springer Proceedings in Mathematics & ; Statistics, 1–41. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55985-3_1.
Texte intégralScheaffer, Richard L., Ann Watkins, Mrudulla Gnanadesikan et Jeffrey A. Witmer. « Waiting for Reggie Jackson : The Geometric Distribution ». Dans Activity-Based Statistics, 79–81. New York, NY : Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-3843-8_17.
Texte intégralChang, Ted. « Tangent Space Approximation in Geometric Statistics ». Dans Springer Handbook of Engineering Statistics, 1059–73. London : Springer London, 2023. http://dx.doi.org/10.1007/978-1-4471-7503-2_53.
Texte intégralKosambi, D. D. « The Geometric Method in Mathematical Statistics ». Dans D.D. Kosambi, 131–39. New Delhi : Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3676-4_17.
Texte intégralAbramov, Viktor, et Jaan Vajakas. « Geometric Approach to Ghost Fields ». Dans Springer Proceedings in Mathematics & ; Statistics, 475–88. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55361-5_27.
Texte intégralActes de conférences sur le sujet "Geometric statistics"
CAI, JUN, et JOSÉ GARRIDO. « ASYMPTOTIC FORMS AND BOUNDS FOR TAILS OF CONVOLUTIONS OF COMPOUND GEOMETRIC DISTRIBUTIONS, WITH APPLICATIONS ». Dans Proceedings of Statistics 2001 Canada : The 4th Conference in Applied Statistics. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2002. http://dx.doi.org/10.1142/9781860949531_0010.
Texte intégralSudsuk, Areeya, et Winai Bodhisuwan. « The Topp-Leone geometric distribution ». Dans 2016 12th International Conference on Mathematics, Statistics, and Their Application (ICMSA). IEEE, 2016. http://dx.doi.org/10.1109/icmsa.2016.7954319.
Texte intégralLi, Lee Siaw, et Maman A. Djauhari. « Monitoring autocorrelated process : A geometric Brownian motion process approach ». Dans INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND STATISTICS 2013 (ICMSS2013) : Proceedings of the International Conference on Mathematical Sciences and Statistics 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4823976.
Texte intégralSagadavan, Revathi, et Maman A. Djauhari. « Autocorrelated multivariate process control : A geometric Brownian motion approach ». Dans INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND STATISTICS 2013 (ICMSS2013) : Proceedings of the International Conference on Mathematical Sciences and Statistics 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4823979.
Texte intégralLei, Li, et LongTing Wang. « Geometric and topological structures of complex numbers ». Dans International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), sous la direction de Ke Chen, Nan Lin, Romeo Meštrović, Teresa A. Oliveira, Fengjie Cen et Hong-Ming Yin. SPIE, 2022. http://dx.doi.org/10.1117/12.2628101.
Texte intégralTing, Dai. « Statistics Properties of Geometric Brown Motion under Haar Wavelet ». Dans 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.1090.
Texte intégralFletcher, P. Thomas, Suresh Venkatasubramanian et Sarang Joshi. « Robust statistics on Riemannian manifolds via the geometric median ». Dans 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587747.
Texte intégralKurfess, Thomas R., et David L. Banks. « Statistical Verification of Conformance to Geometric Tolerance ». Dans ASME 1994 Design Technical Conferences collocated with the ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/detc1994-0065.
Texte intégralKuchkarova, Dilarom, Bafo Khaitov et Bakhtiyor Ismatov. « Geometric modeling of the fore camera’s surface of pumping stations ». Dans 2021 ASIA-PACIFIC CONFERENCE ON APPLIED MATHEMATICS AND STATISTICS. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0090264.
Texte intégralChen, Shuonan, Ziting Huang, Yuchen Lai et Xingyi Lu. « Simulation of geometric Brownian motion in stock price ». Dans International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), sous la direction de Ke Chen, Nan Lin, Romeo Meštrović, Teresa A. Oliveira, Fengjie Cen et Hong-Ming Yin. SPIE, 2022. http://dx.doi.org/10.1117/12.2628052.
Texte intégralRapports d'organisations sur le sujet "Geometric statistics"
Singer, D. A., et R. Kouda. Application of geometric probability and Bayesian statistics to the search for mineral deposits. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128119.
Texte intégralThompson, David C., Joseph Maurice Rojas et Philippe Pierre Pebay. Computational algebraic geometry for statistical modeling FY09Q2 progress. Office of Scientific and Technical Information (OSTI), mars 2009. http://dx.doi.org/10.2172/984161.
Texte intégralWillsky, Alan S. Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion. Fort Belvoir, VA : Defense Technical Information Center, juillet 2004. http://dx.doi.org/10.21236/ada425745.
Texte intégralWilson, D., Matthew Kamrath, Caitlin Haedrich, Daniel Breton et Carl Hart. Urban noise distributions and the influence of geometric spreading on skewness. Engineer Research and Development Center (U.S.), novembre 2021. http://dx.doi.org/10.21079/11681/42483.
Texte intégralRockmore, Daniel. Dynamic Information Networks : Geometry, Topology and Statistical Learning for the Articulation of Structure. Fort Belvoir, VA : Defense Technical Information Center, juin 2015. http://dx.doi.org/10.21236/ada624183.
Texte intégralPerdigão, Rui A. P., et Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, novembre 2020. http://dx.doi.org/10.46337/201111.
Texte intégralThompson, Beavers et Han. L51544 Criteria to Stop Active Pit Growth. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), janvier 1987. http://dx.doi.org/10.55274/r0010282.
Texte intégralZevotek, Robin, et Steve Kerber. Fire Service Summary Report : Study of the Effectiveness of Fire Service Positive Pressure Ventilation During Fire Attack in Single Family Homes Incorporating Modern Construction Practices. UL Firefighter Safety Research Institute, mai 2016. http://dx.doi.org/10.54206/102376/ncck4947.
Texte intégralZevotek, Robin, et Steve Kerber. Study of the Effectiveness of Fire Service Positive Pressure Ventilation During Fire Attack in Single Family Homes Incorporating Modern Construction Practices. UL Firefighter Safety Research Institute, mai 2016. http://dx.doi.org/10.54206/102376/gsph6169.
Texte intégralKerber, Steve. Fire Service Summary : Study of the Effectiveness of Fire Service Vertical Ventilation and Suppression Tactics in Single Family Homes. UL Firefighter Safety Research Institute, juin 2013. http://dx.doi.org/10.54206/102376/roua2913.
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