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Journal articles on the topic 'Estimation de normales'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Bolduc, Denis, and Mustapha Kaci. "Estimation des modèles probit polytomiques : un survol des techniques." Articles 69, no. 3 (2009): 161–91. http://dx.doi.org/10.7202/602113ar.

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RÉSUMÉ Parce qu’il admet des structures très générales d’interdépendance entre les modalités, le probit polytomique (MNP) fournit une des formes les plus intéressantes pour modéliser les choix discrets qui découlent d’une maximisation d’utilité aléatoire. L’obstacle majeur et bien connu dans l’estimation de ce type de modèle tient à la complexité que prennent les calculs lorsque le nombre de modalités considérées est élevé. Cette situation est due essentiellement à la présence d’intégrales normales multidimensionnelles qui définissent les probabilités de sélection. Au cours des deux dernières

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Camero Jiménez, Carlos W., Erick A. Chacón Montalvan, Vilma S. Romero Romero, and Luisa E. Quispe Ortiz. "METODOLOGÍA PARA LA ESTIMACIÓN DE ÍNDICES DE CAPACIDAD EN PROCESOS PARA DATOS NO NORMALES." Revista Cientifica TECNIA 24, no. 1 (2017): 43. http://dx.doi.org/10.21754/tecnia.v24i1.32.

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La globalización ha ido intensificando la competencia en muchos mercados. Con el fin de mantener su competitividad, las empresas buscan satisfacer las necesidades de los clientes mediante el cumplimiento de los requerimientos del mercado. En este contexto, los Índices de Capacidad de Proceso (ICP) juegan un rol trascendental en el análisis de capacidad de los procesos. Para el caso de datos no normales existen dos enfoques generales basados en transformaciones (Transformación de Box –Cox y de Johnson) y percentiles (Sistemas de distribuciones de Pearson y de Burr). Sin embargo, estudios anteri

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Notton, Gilles, Ionut Caluianu, Iolanda Colda, and Sorin Caluianu. "Influence d’un ombrage partiel sur la production électrique d’un module photovoltaïque en silicium monocristallin." Journal of Renewable Energies 13, no. 1 (2023): 49–62. http://dx.doi.org/10.54966/jreen.v13i1.177.

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Le développement du marché photovoltaïque nécessite de connaître parfaitement la production électrique de ces systèmes sur différents sites en particulier pour estimer sa rentabilité économique. Cette estimation précise ne peut se faire qu’en prenant en compte les effets d’ombrage qui ont des conséquences dramatiques sur la puissance électrique délivrée. Dans cet article, nous avons testé un modèle double-diode de courbes I-V sur un module photovoltaïque au silicium monocristallin (BP585F) sous des conditions normales d’éclairement; puis après une brève explication du comportement électrique d

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García, Jhon Jario. "Los estudios de acontecimiento y la importancia de la metodología de estimación." Lecturas de Economía, no. 70 (September 11, 2009): 223–35. http://dx.doi.org/10.17533/udea.le.n70a2262.

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Esta nota realiza una revisión bibliográfica a partir de la literatura existente sobre los estudios de acontecimiento (event study). Su foco principal es la importancia de la metodología utilizada para la estimación de los retornos anormales en el largo plazo, ya que en este periodo los estudios de acontecimientos son sensibles al proceso de generación de los retornos Savickas, 2003 y Aktas et al., 2007). Se encuentra que, en el largo plazo se debe controlar el impacto de acontecimientos sin relación, con la estimación de la ventana para los retornos normales, donde la mejor alternativa para l

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Wu, Zhaohao, Deyun Zhong, Zhaopeng Li, Liguan Wang, and Lin Bi. "Orebody Modeling Method Based on the Normal Estimation of Cross-Contour Polylines." Mathematics 10, no. 3 (2022): 473. http://dx.doi.org/10.3390/math10030473.

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The normal estimation of cross-contour polylines largely determines the implicit orebody modeling result. However, traditional methods cannot estimate normals effectively due to the complex topological adjacency relationship of the cross-contour polylines manually interpreted in the process of exploration and production. In this work, we present an orebody implicit modeling method based on the normal estimation of cross-contour polylines. The improved method consists of three stages: (1) estimating the normals of cross-contour polylines by using the least square plane fitting method based on p

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SAUVANT, D., P. CHAPOUTOT, and H. ARCHIMEDE. "La digestion des amidons par les ruminants et ses conséquences." INRAE Productions Animales 7, no. 2 (1994): 115–24. http://dx.doi.org/10.20870/productions-animales.1994.7.2.4161.

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Il est maintenant acquis que la digestion ruminale de l’amidon varie largement en fonction de la nature de l’aliment et de son traitement technologique. La mesure in sacco de la dégradation théorique de l’amidon (X,%) permet de prédire la digestibilité préintestinale (Y,%) de ce constituant : Y = 0,483 X + 45,62. Les différences sur les paramètres de la digestion de l’amidon peuvent se répercuter sur la protéosynthèse microbienne, le profil fermentaire et la stabilité de l’écosystème ruminal. La digestibilité de l’amidon entrant dans l’intestin grêle n’est que partielle et semble révéler une l

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MITRA, NILOY J., AN NGUYEN, and LEONIDAS GUIBAS. "ESTIMATING SURFACE NORMALS IN NOISY POINT CLOUD DATA." International Journal of Computational Geometry & Applications 14, no. 04n05 (2004): 261–76. http://dx.doi.org/10.1142/s0218195904001470.

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In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in ℝ2or a smooth surface in ℝ3, and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.

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Shi, Tiandong, Deyun Zhong, and Liguan Wang. "Geological Modeling Method Based on the Normal Dynamic Estimation of Sparse Point Clouds." Mathematics 9, no. 15 (2021): 1819. http://dx.doi.org/10.3390/math9151819.

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The effect of geological modeling largely depends on the normal estimation results of geological sampling points. However, due to the sparse and uneven characteristics of geological sampling points, the results of normal estimation have great uncertainty. This paper proposes a geological modeling method based on the dynamic normal estimation of sparse point clouds. The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clou

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Wu, Xianyu, Penghao Li, Xin Zhang, Jiangtao Chen, and Feng Huang. "Three Dimensional Shape Reconstruction via Polarization Imaging and Deep Learning." Sensors 23, no. 10 (2023): 4592. http://dx.doi.org/10.3390/s23104592.

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Deep-learning-based polarization 3D imaging techniques, which train networks in a data-driven manner, are capable of estimating a target’s surface normal distribution under passive lighting conditions. However, existing methods have limitations in restoring target texture details and accurately estimating surface normals. Information loss can occur in the fine-textured areas of the target during the reconstruction process, which can result in inaccurate normal estimation and reduce the overall reconstruction accuracy. The proposed method enables extraction of more comprehensive information, mi

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Zandy, Moe, Vicky Chang, Deepa P. Rao, and Minh T. Do. "Exposition à la fumée du tabac et sommeil : estimation de l’association entre concentration de cotinine urinaire et qualité du sommeil." Promotion de la santé et prévention des maladies chroniques au Canada 40, no. 3 (2020): 77–89. http://dx.doi.org/10.24095/hpcdp.40.3.02f.

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Introduction La majorité des études sur l’exposition à la fumée du tabac et la qualité du sommeil se fondent sur le tabagisme autodéclaré, ce qui peut entraîner des erreurs de classification de l’exposition et des biais liés à l’autodéclaration. L’objectif de cette étude consistait à étudier les associations entre la concentration de cotinine urinaire – un marqueur biologique de l’exposition à la fumée du tabac – et diverses mesures de la qualité du sommeil, soit le temps de sommeil, la continuité ou l’efficacité du sommeil, la satisfaction à l’égard du sommeil et le degré de vigilance durant

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Zhao, Ruibin, Mingyong Pang, Caixia Liu, and Yanling Zhang. "Robust Normal Estimation for 3D LiDAR Point Clouds in Urban Environments." Sensors 19, no. 5 (2019): 1248. http://dx.doi.org/10.3390/s19051248.

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Normal estimation is a crucial first step for numerous light detection and ranging (LiDAR) data-processing algorithms, from building reconstruction, road extraction, and ground-cover classification to scene rendering. For LiDAR point clouds in urban environments, this paper presents a robust method to estimate normals by constructing an octree-based hierarchical representation for the data and detecting a group of large enough consistent neighborhoods at multiscales. Consistent neighborhoods are mainly determined based on the observation that an urban environment is typically comprised of regu

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Liu, Guangshuai, Xurui Li, Si Sun, and Wenyu Yi. "Robust and Fast Normal Mollification via Consistent Neighborhood Reconstruction for Unorganized Point Clouds." Sensors 23, no. 6 (2023): 3292. http://dx.doi.org/10.3390/s23063292.

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This paper introduces a robust normal estimation method for point cloud data that can handle both smooth and sharp features. Our method is based on the inclusion of neighborhood recognition into the normal mollification process in the neighborhood of the current point: First, the point cloud surfaces are assigned normals via a normal estimator of robust location (NERL), which guarantees the reliability of the smooth region normals, and then a robust feature point recognition method is proposed to identify points around sharp features accurately. Furthermore, Gaussian maps and clustering are ad

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Lin, Siyou, Zuoqiang Shi, and Yebin Liu. "Fast and Globally Consistent Normal Orientation based on the Winding Number Normal Consistency." ACM Transactions on Graphics 43, no. 6 (2024): 1–19. http://dx.doi.org/10.1145/3687895.

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Estimating consistently oriented normals for point clouds enables a number of important applications in computer graphics such as surface reconstruction. While local normal estimation is possible with simple techniques like principal component analysis (PCA), orienting these normals to be globally consistent has been a notoriously difficult problem. Some recent methods exploit various properties of the winding number formula to achieve global consistency with state-of-the-art performance. Despite their exciting progress, these algorithms either have high space/time complexity, or do not produc

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Abu-Hani, J., T. Zhang, and S. Filin. "HIGH FIDELITY EDGE AWARE NORMAL ESTIMATION FOR LOW RESOLUTION AND NOISY POINT CLOUDS OF HERITAGE SITES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 737–43. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-737-2022.

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Abstract. Surface normals play a pivotal role in most shape analysis applications. As such, their accurate computation has received a considerable amount of attention over the years. In heritage sites, where entities of complex shape are prevalent, the development of reliable means to estimate them becomes even more important. When approaching the computation of normals, not only the shape complexity becomes a factor, but also the nature of the data, specifically the point density and noise level. To handle both shape and data quality-related aspects, this paper proposes a new method for the c

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Norets, Andriy, and Justinas Pelenis. "Adaptive Bayesian Estimation of Discrete‐Continuous Distributions Under Smoothness and Sparsity." Econometrica 90, no. 3 (2022): 1355–77. http://dx.doi.org/10.3982/ecta17884.

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We consider nonparametric estimation of a mixed discrete‐continuous distribution under anisotropic smoothness conditions and a possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive lower bounds on the estimation rates. Next, we consider a nonparametric mixture of normals model that uses continuous latent variables for the discrete part of the observations. We show that the posterior in this model contracts at rates that are equal to the derived lower bounds up to a log factor. Thus, Bayesian mixture of normals models can be used f

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Livezey, Robert E., Konstantin Y. Vinnikov, Marina M. Timofeyeva, Richard Tinker, and Huug M. van den Dool. "Estimation and Extrapolation of Climate Normals and Climatic Trends." Journal of Applied Meteorology and Climatology 46, no. 11 (2007): 1759–76. http://dx.doi.org/10.1175/2007jamc1666.1.

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Abstract WMO-recommended 30-yr normals are no longer generally useful for the design, planning, and decision-making purposes for which they were intended. They not only have little relevance to the future climate, but are often unrepresentative of the current climate. The reason for this is rapid global climate change over the last 30 yr that is likely to continue into the future. It is demonstrated that simple empirical alternatives already are available that not only produce reasonably accurate normals for the current climate but also often justify their extrapolation to several years into t

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Rataj, Jan. "Estimation of oriented direction distribution of a planar body." Advances in Applied Probability 28, no. 2 (1996): 394–404. http://dx.doi.org/10.2307/1428064.

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Methods of estimation of the oriented direction distribution (i.e. the distribution of unit outer normals over the boundary) of a planar set from the convex ring are proposed. The methods are based on an estimation of the area dilation of the investigated set by chosen test sets.

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Rataj, Jan. "Estimation of oriented direction distribution of a planar body." Advances in Applied Probability 28, no. 02 (1996): 394–404. http://dx.doi.org/10.1017/s0001867800048540.

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Methods of estimation of the oriented direction distribution (i.e. the distribution of unit outer normals over the boundary) of a planar set from the convex ring are proposed. The methods are based on an estimation of the area dilation of the investigated set by chosen test sets.

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HAMDAN, HASAN, and LING XU. "Estimating variance-mean mixtures of Normals." Journal of Statistical Research 52, no. 2 (2019): 129–50. http://dx.doi.org/10.47302/jsr.2018520202.

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A new semi-nonparametric method for modeling data with Normal variance-mean mixtures (NVMM) is presented. This new method is based on the least-squares programming routine, UNMIX. Density estimates based on random samples of size n from two, three, and four components of NVMM are found using UNMIX. Graphical comparisons of the UNMIX fit found by the EM Algorithm and the Bayesian approaches are done using three real life examples. A quantitative comparison using the AIC and Chi-Square is done for one of the most commonly used examples, the Galaxy Data. The results are promising and the method h

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Sun, Bomin, and Thomas C. Peterson. "Estimating temperature normals for USCRN stations." International Journal of Climatology 25, no. 14 (2005): 1809–17. http://dx.doi.org/10.1002/joc.1220.

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Fourey, Sébastien, and Rémy Malgouyres. "Normals estimation for digital surfaces based on convolutions." Computers & Graphics 33, no. 1 (2009): 2–10. http://dx.doi.org/10.1016/j.cag.2008.11.003.

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Roeder, Kathryn, and Larry Wasserman. "Practical Bayesian Density Estimation Using Mixtures of Normals." Journal of the American Statistical Association 92, no. 439 (1997): 894–902. http://dx.doi.org/10.1080/01621459.1997.10474044.

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Wu, Yunlong, Qing Zhang, and Shuxuan Zhang. "Fast Cylindrical Fitting Method Using Point Cloud’s Normals Estimation." Mathematical Problems in Engineering 2018 (December 12, 2018): 1–7. http://dx.doi.org/10.1155/2018/8904653.

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Cylindrical fitting is an essential step in Large Process Pipeline’s measurement process, and precision of initial values of cylindrical fitting is a key element in getting a correct fitting result. In order to get well initial values, covariance matrixes of all points in cylinder’s three-dimensional laser scanning point cloud should be firstly established to estimate normals of all points, and then cylinder’s axis vector can be calculated by using least squares method. Secondly, remaining parameters’ initial values of the cylinder can be got by coordinate transformation. Finally, Levenberg-Ma

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Reichl, Johannes. "Estimating marginal likelihoods from the posterior draws through a geometric identity." Monte Carlo Methods and Applications 26, no. 3 (2020): 205–21. http://dx.doi.org/10.1515/mcma-2020-2068.

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AbstractThis article develops a new estimator of the marginal likelihood that requires only a sample of the posterior distribution as the input from the analyst. This sample may come from any sampling scheme, such as Gibbs sampling or Metropolis–Hastings sampling. The presented approach can be implemented generically in almost any application of Bayesian modeling and significantly decreases the computational burdens associated with marginal likelihood estimation compared to existing techniques. The functionality of this method is demonstrated in the context of probit and logit regressions, on

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Ye, Chongjie, Lingteng Qiu, Xiaodong Gu, et al. "StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal." ACM Transactions on Graphics 43, no. 6 (2024): 1–18. http://dx.doi.org/10.1145/3687971.

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This work addresses the challenge of high-quality surface normal estimation from monocular colored inputs (i.e., images and videos), a field which has recently been revolutionized by repurposing diffusion priors. However, previous attempts still struggle with stochastic inference, conflicting with the deterministic nature of the Image2Normal task, and costly ensembling step, which slows down the estimation process. Our method, StableNormal, mitigates the stochasticity of the diffusion process by reducing inference variance, thus producing "Stable-and-Sharp" normal estimates without any additio

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Kitanovski, Vlado, and Jon Yngve Hardeberg. "Objective evaluation of relighting models on translucent materials from multispectral RTI images." Electronic Imaging 2021, no. 5 (2021): 133–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.5.maap-133.

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In this paper, we evaluate the quality of reconstruction i.e. relighting from images obtained by a newly developed multispectral reflectance transformation imaging (MS-RTI) system. The captured MS-RTI images are of objects with different translucency and color. We use the most common methods for relighting the objects: polynomial texture mapping (PTM) and hemispherical harmonics (HSH), as well as the recent discrete model decomposition (DMD). The results show that all three models can reconstruct the images of translucent materials, with the reconstruction error varying with translucency but s

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Shi, Ji, Xianghua Ying, Ruohao Guo, Bowei Xing, and Wenzhen Yue. "Normal-NeRF: Ambiguity-Robust Normal Estimation for Highly Reflective Scenes." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 6869–77. https://doi.org/10.1609/aaai.v39i7.32737.

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Neural Radiance Fields (NeRF) often struggle with reconstructing and rendering highly reflective scenes. Recent advancements have developed various reflection-aware appearance models to enhance NeRF's capability to render specular reflections. However, the robust reconstruction of highly reflective scenes is still hindered by the inherent shape ambiguity on specular surfaces. Existing methods typically rely on additional geometry priors to regularize the shape prediction, but this can lead to oversmoothed geometry in complex scenes. Observing the critical role of surface normals in parameteriz

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Holroyd, Michael, Jason Lawrence, Greg Humphreys, and Todd Zickler. "A photometric approach for estimating normals and tangents." ACM Transactions on Graphics 27, no. 5 (2008): 1–9. http://dx.doi.org/10.1145/1409060.1409086.

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Rigal, Alix, Jean-Marc Azaïs, and Aurélien Ribes. "Estimating daily climatological normals in a changing climate." Climate Dynamics 53, no. 1-2 (2018): 275–86. http://dx.doi.org/10.1007/s00382-018-4584-6.

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Kim, S., H. G. Kim, and T. Kim. "MESH MODELLING OF 3D POINT CLOUD FROM UAV IMAGES BY POINT CLASSIFICATION AND GEOMETRIC CONSTRAINTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 507–11. http://dx.doi.org/10.5194/isprs-archives-xlii-2-507-2018.

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The point cloud generated by multiple image matching is classified as an unstructured point cloud because it is not regularly point spaced and has multiple viewpoints. The surface reconstruction technique is used to generate mesh model using unstructured point clouds. In the surface reconstruction process, it is important to calculate correct surface normals. The point cloud extracted from multi images contains position and color information of point as well as geometric information of images used in the step of point cloud generation. Thus, the surface normal estimation based on the geometric

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Almanza, Diego, Takeshi Sugio, Emily Hamilton, et al. "Abstract 4565: CANARy-TF: an improved copy number and tumor fraction estimator for liquid biopsy applications." Cancer Research 85, no. 8_Supplement_1 (2025): 4565. https://doi.org/10.1158/1538-7445.am2025-4565.

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Abstract Background: Accurate detection of copy number alterations (CNAs) and reliable estimation of tumor fraction from liquid biopsies are crucial for effectively triaging patient samples in large clinical trials. This is particularly vital in scenarios where variant allele frequency (VAF) data is unavailable, yet an estimation of tumor fraction remains necessary. We hypothesize that specific characteristics of circulating free DNA (cfDNA) can enhance the performance of algorithms aimed at detecting CNAs and estimating tumor fraction. Methods: We introduce CANARy-TF (Copy number ANomaly Asse

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Soto Molina, Victor Hugo, and Hugo Delgado Granados. "Estimación de la temperatura del aire en la alta montaña mexicana mediante un modelo de elevación del terreno: caso del volcán Nevado de Toluca (México) / Estimation of the air temperature in the Mexican high mountain environment by means of a model of elevation of the terrain, case of the Nevado de Toluca volcano (Mexico)." Ería 2, no. 2 (2020): 167–82. http://dx.doi.org/10.17811/er.2.2020.167-182.

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La carencia de estaciones climatológicas por encima de 3.500 metros sobre el nivel del mar (msnm) en México condiciona que los estudios sobre ecosistemas de alta montaña sean realizados con datos de estaciones cercanas, pero sin considerar la diferencia altitudinal de la temperatura debida al relieve y al gradiente vertical. Por tal razón, se realiza un modelo mensual y anual de la distribución espacial de la temperatura del aire en superficie para el volcán Nevado de Toluca (4.680 msnm) y zonas adyacentes, mediante el uso de un Modelo Digital de Elevaciones y el Gradiente Vertical de la Tempe

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Verhoeven, J. T. M., and J. M. Thijssen. "Potential of Fractal Analysis for Lesion Detection in Echographic Images." Ultrasonic Imaging 15, no. 4 (1993): 304–23. http://dx.doi.org/10.1177/016173469301500403.

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The application of fractal analysis to parametric imaging of B-mode echograms and to differentation of echographic speckle textures was investigated. Echograms were obtained from realistic simulations and from a clinical study on diffuse liver disease. The simulations comprised tissue models with randomly positioned scatterers in a 3-D volume in which the number density was varied over a range from 0.5 to 25 mm–3. The clinical echograms comprised both normals and patients with liver cirrhosis. Three methods of estimating the fractal dimension were investigated, two in the spatial image domain

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Camero Jiménez, José W., and Jahaziel G. Ponce Sánchez. "COMPARACION DEL PROMEDIO Y LA MEDIANA COMO ESTIMADORES DE LA MEDIA PARA MUESTRAS NORMALES DEPENDIENDO DEL TAMAÑO DE MUESTRA." Revista Cientifica TECNIA 23, no. 2 (2017): 33. http://dx.doi.org/10.21754/tecnia.v23i2.73.

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Actualmente los métodos para estimar la media son los basados en el intervalo de confianza del promedio o media muestral. Este trabajo pretende ayudar a escoger el estimador (promedio o mediana) a usar dependiendo del tamaño de muestra. Para esto se han generado, vía simulación en excel, muestras con distribución normal y sus intervalos de confianza para ambos estimadores, y mediante pruebas de hipótesis para la diferencia de proporciones se demostrará que método es mejor dependiendo del tamaño de muestra. Palabras clave.-Tamaño de muestra, Intervalo de confianza, Promedio, Mediana. ABSTRACTCu

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Li, Shujuan, Junsheng Zhou, Baorui Ma, Yu-Shen Liu, and Zhizhong Han. "NeAF: Learning Neural Angle Fields for Point Normal Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 1396–404. http://dx.doi.org/10.1609/aaai.v37i1.25224.

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Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors or learning local surface fitting using neural networks. However, these methods are not generalized well to unseen scenarios and are sensitive to parameter settings. To resolve these issues, we propose an implicit function to learn an angle field around the normal of each point in the spherical coordinate system, which is dubbed as Neural Angle Fields (NeAF). Instead of directly predicting the normal of an input point

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Wilks, D. S. "Projecting “Normals” in a Nonstationary Climate." Journal of Applied Meteorology and Climatology 52, no. 2 (2013): 289–302. http://dx.doi.org/10.1175/jamc-d-11-0267.1.

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AbstractClimate “normals” are statistical estimates of present and/or near-future climate means for such quantities as seasonal temperature or precipitation. In a changing climate, simply averaging a large number of previous years of data may not be the best method for estimating normals. Here eight formulations for climate normals, including the recently proposed “hinge” function, are compared in artificial- and real-data settings. Although the hinge function is attractive conceptually for representing accelerating climate changes simply, its use is in general not yet justified for divisional

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Giordani, Paolo, and Robert Kohn. "Adaptive Independent Metropolis–Hastings by Fast Estimation of Mixtures of Normals." Journal of Computational and Graphical Statistics 19, no. 2 (2010): 243–59. http://dx.doi.org/10.1198/jcgs.2009.07174.

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Stavropoulou, Eleni, Christophe Dano, Marc Boulon, Matthieu Briffaut, Ankit Sharma, and Alain Puech. "Résistance au cisaillement des interfaces roche / coulis représentatives de pieux offshore." Revue Française de Géotechnique, no. 158 (2019): 6. http://dx.doi.org/10.1051/geotech/2019012.

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Dans le cadre des projets éoliens offshore, la conception optimisée des fondations de type monopieux forés dans des massifs rocheux carbonatés demande une estimation réaliste de la résistance au cisaillement mobilisable. Dans cette optique, une campagne d’essais de cisaillement d’interface roche / coulis est conduite sur la machine BCR3D qui permet, en particulier, d’appliquer une condition de rigidité imposée sur la normale à l’interface, condition jugée représentative de ce qui se passe au droit du pieu. On présente dans une première partie le dispositif expérimental, la préparation des épro

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Nugroho, Didit Budi, Agus Priyono, and Bambang Susanto. "SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL." MEDIA STATISTIKA 14, no. 1 (2021): 21–32. http://dx.doi.org/10.14710/medstat.14.1.21-32.

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The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to d

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Norton. "A MULTIVARIATE TECHNIQUE FOR ESTIMATING NEW ZEALAND TEMPERATURE NORMALS." Weather and Climate 5, no. 2 (1985): 64. http://dx.doi.org/10.2307/44279988.

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Wu, Yingrui, Mingyang Zhao, Keqiang Li, et al. "CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-Scale Geometry." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 6171–79. http://dx.doi.org/10.1609/aaai.v38i6.28434.

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This work presents an accurate and robust method for estimating normals from point clouds. In contrast to predecessor approaches that minimize the deviations between the annotated and the predicted normals directly, leading to direction inconsistency, we first propose a new metric termed Chamfer Normal Distance to address this issue. This not only mitigates the challenge but also facilitates network training and substantially enhances the network robustness against noise. Subsequently, we devise an innovative architecture that encompasses Multi-scale Local Feature Aggregation and Hierarchical

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Aloqaili, Murtadha Jaafar, and Rahim Alhamzawi. "Bayesian Composite Quantile Regression with Composite Group Bridge Penalty." NeuroQuantology 20, no. 2 (2022): 173–79. http://dx.doi.org/10.14704/nq.2022.20.2.nq22269.

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We study composite quantile regression (CQReg) with composite group bridge penalty for model selection and estimation. Compared to conventional mean regression, composite quantile regression (CQR) is an efficient and robust estimation approach. A simple and efficient algorithm was developed for posterior inference using a pseudo composite asymmetric Laplace distribution which can be formulated as a location-scale mixture of normals. The composite group bridge priors were formulated as a scale mixture of multivariate uniforms. We assess the performance of the proposed method using simulation st

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SRIVASTAVA, A. K., P. GUHATHAKURTA, and S. R. KSHIRSAGAR. "Estimation of annual and seasonal temperatures over Indian stations using optimal normals." MAUSAM 54, no. 3 (2022): 615–22. http://dx.doi.org/10.54302/mausam.v54i3.1552.

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Zhao, Can, and Xianglin Meng. "An improved algorithm for k-nearest-neighbor finding and surface normals estimation." Tsinghua Science and Technology 14, S1 (2009): 77–81. http://dx.doi.org/10.1016/s1007-0214(09)70071-9.

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Yoon, Jong-In. "Estimation of the Mixture of Normals of Saving Rate Using Gibbs Algorithm." Journal of Digital Convergence 13, no. 10 (2015): 219–24. http://dx.doi.org/10.14400/jdc.2015.13.10.219.

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Guo, Ruibin, Keju Peng, Weihong Fan, Yongping Zhai, and Yunhui Liu. "RGB-D SLAM Using Point–Plane Constraints for Indoor Environments." Sensors 19, no. 12 (2019): 2721. http://dx.doi.org/10.3390/s19122721.

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Pose estimation and map reconstruction are basic requirements for robotic autonomous behavior. In this paper, we propose a point–plane-based method to simultaneously estimate the robot’s poses and reconstruct the current environment’s map using RGB-D cameras. First, we detect and track the point and plane features from color and depth images, and reliable constraints are obtained, even for low-texture scenes. Then, we construct cost functions from these features, and we utilize the plane’s minimal representation to minimize these functions for pose estimation and local map optimization. Furthe

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Hopkinson, Ron F., Michael F. Hutchinson, Daniel W. McKenney, Ewa J. Milewska, and Pia Papadopol. "Optimizing Input Data for Gridding Climate Normals for Canada." Journal of Applied Meteorology and Climatology 51, no. 8 (2012): 1508–18. http://dx.doi.org/10.1175/jamc-d-12-018.1.

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AbstractSpatial models of 1971–2000 monthly climate normals for daily maximum and minimum temperature and total precipitation are required for many applications. The World Meteorological Organization’s recommended standard for the calculation of a normal value is a complete 30-yr record with a minimal amount of missing data. Only 650 stations (~16%) in Canada meet this criterion for the period 1971–2000. Thin-plate smoothing-spline analyses, as implemented by the Australian National University Splines (ANUSPLIN) package, are used to assess the utility of differing amounts of station data in es

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Bernton, Espen, Pierre E. Jacob, Mathieu Gerber, and Christian P. Robert. "On parameter estimation with the Wasserstein distance." Information and Inference: A Journal of the IMA 8, no. 4 (2019): 657–76. http://dx.doi.org/10.1093/imaiai/iaz003.

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Abstract Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein distance between model distributions and the empirical distribution of the data. We study asymptotic properties of such minimum Wasserstein distance estimators, complementing results derived by Bassetti, Bodini and Regazzini in 2006. In particular, our results cover the misspecified setting, in which the data-generating process is not assumed to be part of the family of distributions described by the model. Our results are motivated by recent applications of minimum Wasserstein estimators t

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Xu, Rui, Zhiyang Dou, Ningna Wang, et al. "Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-Number Field." ACM Transactions on Graphics 42, no. 4 (2023): 1–15. http://dx.doi.org/10.1145/3592129.

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Estimating normals with globally consistent orientations for a raw point cloud has many downstream geometry processing applications. Despite tremendous efforts in the past decades, it remains challenging to deal with an unoriented point cloud with various imperfections, particularly in the presence of data sparsity coupled with nearby gaps or thin-walled structures. In this paper, we propose a smooth objective function to characterize the requirements of an acceptable winding-number field, which allows one to find the globally consistent normal orientations starting from a set of completely ra

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Zhang Jinghua, 张景华, 张焱 Zhang Yan, 石志广 Shi Zhiguang та ін. "基于法向量估计的透明物体表面反射光分离". Acta Optica Sinica 41, № 15 (2021): 1526001. http://dx.doi.org/10.3788/aos202141.1526001.

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