Relevant bibliographies by topics / Nonlinear projection

Academic literature on the topic 'Nonlinear projection'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Nonlinear projection"

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Dudek, Ewa, and Konstanty Holly. "Nonlinear orthogonal projection." Annales Polonici Mathematici 59, no. 1 (1994): 1–31. http://dx.doi.org/10.4064/ap-59-1-1-31.

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Ashlock, Daniel, and Andrew McEachern. "Evolutionary Nonlinear Projection." IEEE Transactions on Evolutionary Computation 19, no. 6 (December 2015): 857–69. http://dx.doi.org/10.1109/tevc.2015.2395091.

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Jing, Xiao Yuan, Min Li, Yong Fang Yao, Song Hao Zhu, and Sheng Li. "A New Kernel Orthogonal Projection Analysis Approach for Face Recognition." Advanced Materials Research 760-762 (September 2013): 1627–32. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1627.

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In the field of face recognition, how to extract effective nonlinear discriminative features is an important research topic. In this paper, we propose a new kernel orthogonal projection analysis approach. We obtain the optimal nonlinear projective vector which can differentiate one class and its adjacent classes, by using the Fisher criterion and constructing the specific between-class and within-class scatter matrices in kernel space. In addition, to eliminate the redundancy among projective vectors, our approach makes every projective vector satisfy locally orthogonal constraints by using the corresponding class and part of its most adjacent classes. Experimental results on the public AR and CAS-PEAL face databases demonstrate that the proposed approach outperforms several representative nonlinear projection analysis methods.
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Masry, Elias, and Dag Tjøstheim. "Additive Nonlinear ARX Time Series and Projection Estimates." Econometric Theory 13, no. 2 (April 1997): 214–52. http://dx.doi.org/10.1017/s0266466600005739.

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We propose projections as means of identifying and estimating the components (endogenous and exogenous) of an additive nonlinear ARX model. The estimates are nonparametric in nature and involve averaging of kernel-type estimates. Such estimates have recently been treated informally in a univariate time series situation. Here we extend the scope to nonlinear ARX models and present a rigorous theory, including the derivation of asymptotic normality for the projection estimates under a precise set of regularity conditions.
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Atkinson, Kendall E., and Florian A. Potra. "Projection and Iterated Projection Methods for Nonlinear Integral equations." SIAM Journal on Numerical Analysis 24, no. 6 (December 1987): 1352–73. http://dx.doi.org/10.1137/0724087.

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Yuen, P. C., G. C. Feng, and Y. Y. Tang. "Printed Chinese Character Similarity Measurement Using Ring Projection and Distance Transform." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 02 (March 1998): 209–21. http://dx.doi.org/10.1142/s0218001498000142.

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This paper presents a new Chinese character similarity measurement method based on the ring projection algorithm and distance transform. The ring projection algorithm is used to transform a character image with two independent variables into a function of one independent variable in the ring projection space. This representation of character in the ring projection space has been proved to be in orientation and scale invariant. However, this representation will be distorted nonlinearly in the presence of noise. Therefore, common linear metrics such as Euclidean distance, cannot be applied to measure distance. To solve the nonlinear distortion problem, distance transform is proposed as a nonlinear metric. The similarity measurement is performed using the distance transformed image in the ring projection space. A number of Chinese characters are selected to evaluate the capability of the proposed measurement scheme and the results are encouraging.
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Breaban, Mihaela, and Henri Luchian. "Outlier Detection with Nonlinear Projection Pursuit." International Journal of Computers Communications & Control 8, no. 1 (November 13, 2012): 30. http://dx.doi.org/10.15837/ijccc.2013.1.165.

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Agarwal, Ravi P., Yeol Je Cho, and Xiaolong Qin. "Generalized Projection Algorithms for Nonlinear Operators." Numerical Functional Analysis and Optimization 28, no. 11-12 (December 10, 2007): 1197–215. http://dx.doi.org/10.1080/01630560701766627.

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Cai, Zongwu, and Elias Masry. "NONPARAMETRIC ESTIMATION OF ADDITIVE NONLINEAR ARX TIME SERIES: LOCAL LINEAR FITTING AND PROJECTIONS." Econometric Theory 16, no. 4 (August 2000): 465–501. http://dx.doi.org/10.1017/s0266466600164011.

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We consider the estimation and identification of the components (endogenous and exogenous) of additive nonlinear ARX time series models. We employ a local polynomial fitting scheme coupled with projections. We establish the weak consistency (with rates) and the asymptotic normality of the projection estimates of the additive components. Expressions for the asymptotic bias and variance are given.
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Gu, Guang Hui, and Yong Fu Su. "Generalized System for Relaxed Cocoercive and Involving Projective Nonexpansive Mapping Variational Inequalities." Advanced Materials Research 393-395 (November 2011): 792–95. http://dx.doi.org/10.4028/www.scientific.net/amr.393-395.792.

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Firstly, the concept of projective nonexpansive mappings is presented in this paper. The approximate solvability of a generalized system for relaxed cocoercive and involving projective nonexpansive mapping nonlinear variational inequalities in Hilbert spaces is studied, based on the convergence of projection methods. The results presented in this paper extend and improve the main results of many authors.
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Dissertations / Theses on the topic "Nonlinear projection"

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Lin, Anhua. "Projection algorithms in nonlinear programming." Available to US Hopkins community, 2003. http://wwwlib.umi.com/dissertations/dlnow/3080715.

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Vallance, Scott, and scottvallance@internode on net. "Trilinear Projection." Flinders University. School of Informatics & Engineering, 2005. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20050714.113416.

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In computer graphics a projection describes the mapping of scene geometry to the screen. While linear projections such as perspective and orthographic projection are common, increasing applications are being found for nonlinear projections, which do not necessarily map straight lines in the scene to straight lines on the screen. Nonlinear projections occur in reflections and refractions on curved surfaces, in art, and in visualisation. This thesis presents a new nonlinear projection technique called a trilinear projection that is based on the trilinear interpolation of surface normals used in Phong shading. Trilinear projections can be combined to represent more complicated nonlinear projections. Nonlinear projections have previously been implemented with ray tracing, where rays are generated by the nonlinear projections and traced into the scene. However for performance reasons, most current graphics software uses scanline rendering, where a scene point is imaged on a screen as a function of the projection parameters. The techniques developed in this thesis are of this nature. This thesis presents several algorithms used in trilinear projection: 1. An algorithm to analytically determine which screen locations image a given scene point. 2. An algorithm that correctly connects projected vertices. Each scene point may be imaged multiple times, which means a projected scene triangle may form from one to four different shapes of from two to nine vertices. Once connected, the projected shapes may be rendered with standard scanline algorithms. 3. An algorithm to more accurately render the curved edges between projected vertices. 4. A scene-space edge-clipping algorithm that handles continuity issues for projected shapes across composite projections. The trilinear projection technique is demonstrated in two different application areas: visualisation, and reflections and refractions. Specifically, various nonlinear projections that are congruent with pre-existing visualisation techniques are implemented with trilinear projections and a method for approximating the reflections and refractions on curved surfaces with trilinear projections is presented. Finally, the performance characteristics of the trilinear projection is explored over various parameter ranges and compared with a naive ray tracing approach.
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Betcke, Marta. "Iterative projection methods for symmetric nonlinear eigenvalue problems with applications." Berlin dissertation.de, 2007. http://www.dissertation.de/buch.php3?buch=5233.

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Swinson, Michael D. "Statistical Modeling of High-Dimensional Nonlinear Systems: A Projection Pursuit Solution." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-11232005-204333/.

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Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2006.
Shapiro, Alexander, Committee Member ; Vidakovic, Brani, Committee Member ; Ume, Charles, Committee Member ; Sadegh, Nader, Committee Chair ; Liang, Steven, Committee Member. Vita.
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Rehbein, Nicolai [Verfasser]. "Inexact Iterative Projection Methods for Linear and Nonlinear Eigenvalue Problems / Nicolai Rehbein." München : Verlag Dr. Hut, 2020. http://d-nb.info/1219476250/34.

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Liao, Chwen Kai. "Adaptive Control of a Camera-Projection System using Vision-Based Feedback." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/79563.

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This thesis derives an vision based feedback control strategy for a class of uncertain projector-camera systems that are used to animate two dimensional projected images on complex, three dimensional, articulated target objects. The target object of the robotic system is articulated using an open loop control strategy that generates a desired sequence of target poses that are designed using commercially available geometric modeling software. The ideal or desired image sequences are subsequently rendered in the geometric modeling software using an ideal camera/projector pose and ideal intrinsic parameter camera model. The rendered imagery from the ideal camera and projector pose are subsequently used to define tracking performance for the feedback control of the camera and projector. Uncertainty in actuator models of the camera and projector actuator subsystems in this paper includes contributions due to imprecision in camera pose and in intrinsic camera parameters. A feedback control strategy is derived that employs pixel coordinates of multiple tracked feature points in the target image sequence for pose estimation and tracking control problems. We establish sufficient conditions that guarantee the convergence and asymptotic stability of the pose estimation and tracking control problems for the class of uncertain, nonlinear systems studied in this thesis. Several numerical studies are summarized in the thesis that provide confidence in the derived theoretical results and further suggest robustness of the control strategy for the considered uncertainty class.
Master of Science
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Rehbein, Nicolai [Verfasser], and Heinrich [Akademischer Betreuer] Voß. "Inexact iterative projection methods for linear and nonlinear eigenvalue problems / Nicolai Rehbein ; Betreuer: Heinrich Voß." Hamburg : Universitätsbibliothek der Technischen Universität Hamburg-Harburg, 2020. http://d-nb.info/1206999136/34.

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Cho, Taewon. "Numerical Methods for Separable Nonlinear Inverse Problems with Constraint and Low Rank." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/82929.

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In this age, there are many applications of inverse problems to lots of areas ranging from astronomy, geoscience and so on. For example, image reconstruction and deblurring require the use of methods to solve inverse problems. Since the problems are subject to many factors and noise, we can't simply apply general inversion methods. Furthermore in the problems of interest, the number of unknown variables is huge, and some may depend nonlinearly on the data, such that we must solve nonlinear problems. It is quite different and significantly more challenging to solve nonlinear problems than linear inverse problems, and we need to use more sophisticated methods to solve these kinds of problems.
Master of Science
In various research areas, there are many required measurements which can't be observed due to physical and economical reasons. Instead, these unknown measurements can be recovered by known measurements. This phenomenon can be modeled and be solved by mathematics.
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Niskanen, M. (Matti). "A visual training based approach to surface inspection." Doctoral thesis, University of Oulu, 2003. http://urn.fi/urn:isbn:9514270673.

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Abstract Training a visual inspection device is not straightforward but suffers from the high variation in material to be inspected. This variation causes major difficulties for a human, and this is directly reflected in classifier training. Many inspection devices utilize rule-based classifiers the building and training of which rely mainly on human expertise. While designing such a classifier, a human tries to find the questions that would provide proper categorization. In training, an operator tunes the classifier parameters, aiming to achieve as good classification accuracy as possible. Such classifiers require lot of time and expertise before they can be fully utilized. Supervised classifiers form another common category. These learn automatically from training material, but rely on labels that a human has set for it. However, these labels tend to be inconsistent and thus reduce the classification accuracy achieved. Furthermore, as class boundaries are learnt from training samples, they cannot in practise be later adjusted if needed. In this thesis, a visual based training method is presented. It avoids the problems related to traditional training methods by combining a classifier and a user interface. The method relies on unsupervised projection and provides an intuitive way to directly set and tune the class boundaries of high-dimensional data. As the method groups the data only by the similarities of its features, it is not affected by erroneous and inconsistent labelling made for training samples. Furthermore, it does not require knowledge of the internal structure of the classifier or iterative parameter tuning, where a combination of parameter values leading to the desired class boundaries are sought. On the contrary, the class boundaries can be set directly, changing the classification parameters. The time need to take such a classifier into use is small and tuning the class boundaries can happen even on-line, if needed. The proposed method is tested with various experiments in this thesis. Different projection methods are evaluated from the point of view of visual based training. The method is further evaluated using a self-organizing map (SOM) as the projection method and wood as the test material. Parameters such as accuracy, map size, and speed are measured and discussed, and overall the method is found to be an advantageous training and classification scheme.
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Nigro, Paulo Salvador Britto. "An adaptive model order reduction for nonlinear dynamical problems." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3144/tde-26122014-122046/.

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Model order reduction is necessary even in a time where the parallel processing is usual in almost any personal computer. The recent Model Reduction Methods are useful tools nowadays on reducing the problem processing. This work intends to describe a combination between POD (Proper Orthogonal Decomposition) and Ritz vectors that achieve an efficient Galerkin projection that changes during the processing, comparing the development of the error and the convergence rate between the full space and the projection space, in addition to check the stability of the projection space, leading to an adaptive model order reduction for nonlinear dynamical problems more efficient. This model reduction is supported by a secant formulation, which is updated by BFGS (Broyden - Fletcher - Goldfarb - Shanno) method to accelerate convergence of the model, and a tangent formulation to correct the projection space. Furthermore, this research shows that this method permits a correction of the reduced model at low cost, especially when the classical POD is no more efficient to represent accurately the solution.
A Redução de ordem de modelo é necessária, mesmo em uma época onde o processamento paralelo é usado em praticamente qualquer computador pessoal. Os recentes métodos de redução de modelo são ferramentas úteis nos dias de hoje para a redução de processamento de um problema. Este trabalho pretende descrever uma combinação entre POD (Proper Orthogonal Decomposition) e vetores de Ritz para uma projecção de Galerkin eficiente que sofre alterações durante o processamento, comparando o desenvolvimento do erro e a taxa de convergência entre o espaço total e o espaço de projeção, além da verificação de estabilidade do espaço de projeção, levando a uma redução de ordem do modelo adaptativo mais eficiente para problemas dinâmicos não-lineares. Esta redução de modelo é assistida por uma formulação secante, que é atualizado pela formula de BFGS (Broyden - Fletcher- Goldfarb - Shanno) com o intuito de acelerar a convergência do modelo, e uma formulação tangente para a correção do espaço de projeção. Além disso, esta pesquisa mostra que este método permite a correção do modelo reduzido com baixo custo, especialmente quando o clássico POD não é mais eficiente para representar com precisão a solução.
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Books on the topic "Nonlinear projection"

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E, Spedicato, ed. ABS projection algorithms: Mathematical techniques for linear and nonlinear equations. Chichester, West Sussex, England: Ellis Horwood, 1989.

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1862-1943, Hilbert David, ed. Hilbert's projective metric and iterated nonlinear maps. Providence, R.I., USA: American Mathematical Society, 1988.

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Nussbaum, Roger D. Iterated nonlinear maps and Hilbert's projective metric, II. Providence, R.I., USA: American Mathematical Society, 1989.

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Gradient projection method in linear and nonlinear programming. Nonantum, Mass: Hadronic Press, 1988.

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Coleman, Patrick. Interactive control of nonlinear projection for complex animated scenes. 2004.

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Du, Ding-Zhu, and Frank D. Hsu. Gradient Projection Method in Linear and Nonlinear Programming (Advances in Discrete Mathematics and Computer Science, Vol 3). Hadronic Press, Incorporated, 1989.

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Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.
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Ferrari, Matthew. Using disease dynamics and modeling to inform control strategies in low-income countries. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789833.003.0008.

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The incidence infectious disease is inherently dynamic in time and space. Mathematical models that account for the dynamic processes that give rise to fluctuations in disease incidence are powerful tools in disease management and control. We describe the use of dynamic models for surveillance, evaluation and prediction of disease control efforts in low-income countries. Dynamic models can help to anticipate trends owing to intrinsic (e.g., herd immunity) or extrinsic (e.g., seasonality) forces that may confound efforts to isolate the impact of specific interventions. Infectious disease dynamics are frequently nonlinear, meaning that future outcomes are difficult to predict through simple extrapolation of present conditions. Thus, dynamic models can help to explore the potential consequences of proposed interventions. These projections can alert managers to the potential for unintended consequences of control and help to define effect sizes for the design of conventional studies of the impact of interventions.
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Fonarev, Anatoliy. Projective iterative methods of solution of the equations and variation inequalities with nonlinear operators of the theory of monotone operators. Infra-M Academic Publishing House, 2014. http://dx.doi.org/10.12737/2471.

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Book chapters on the topic "Nonlinear projection"

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Galántai, Aurél. "Projection Methods for Nonlinear Algebraic Equations." In Projectors and Projection Methods, 155–80. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9180-5_5.

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Abbas, M., and S. Z. Németh. "Isotone Projection Cones and Nonlinear Complementarity Problems." In Nonlinear Analysis, 323–47. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1883-8_10.

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Hochman, Amit, Dmitry M. Vasilyev, Michał J. Rewieński, and Jacob K. White. "Projection-Based Nonlinear Model Order Reduction." In Advanced Micro and Nanosystems, 237–62. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527647132.ch10.

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Lee, John A., and Michel Verleysen. "Nonlinear Projection with the Isotop Method." In Artificial Neural Networks — ICANN 2002, 933–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_151.

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Yin, Hujun. "Nonlinear Multidimensional Data Projection and Visualisation." In Intelligent Data Engineering and Automated Learning, 377–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45080-1_49.

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Gracia, Javier, Ville-Pekka Seppä, Anna Pelkonen, Anne Kotaniemi-Syrjänen, Mika Mäkelä, Pekka Malmberg, and Jari Viik. "Nonlinear Local Projection Filter for Impedance Pneumography." In EMBEC & NBC 2017, 306–9. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5122-7_77.

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Marinai, Simone, Emanuele Marino, and Giovanni Soda. "Nonlinear Embedded Map Projection for Dimensionality Reduction." In Image Analysis and Processing – ICIAP 2009, 219–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_25.

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Diniz-Ehrhardt, Maria A., and José Mario Martinez. "Successive Projection Methods for the Solution of Overdetermined Nonlinear Systems." In Nonlinear Optimization and Applications, 75–84. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-0289-4_6.

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Zhao, Shi Jian, Yong Mao Xu, and Jie Zhang. "A Novel Nonlinear Projection to Latent Structures Algorithm." In Lecture Notes in Computer Science, 773–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28648-6_124.

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Brown, Peter N., and Youcef Saad. "Projection Methods for Solving Nonlinear Systems of Equations." In Nematics, 341–55. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3428-6_25.

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Conference papers on the topic "Nonlinear projection"

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Su, Sen, Gang Chen, Xiang Cheng, and Rong Bi. "Deep Supervised Hashing with Nonlinear Projections." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/388.

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Hashing has attracted broad research interests in large scale image retrieval due to its high search speed and efficient storage. Recently, many deep hashing methods have been proposed to perform simultaneous nonlinear feature learning and hash projection learning, which have shown superior performance compared to hand-crafted feature based hashing methods. Nonlinear projection functions have shown their advantages over the linear ones due to their powerful generalization capabilities. To improve the performance of deep hashing methods by generalizing projection functions, we propose the idea of implementing a pure nonlinear deep hashing network architecture. By consolidating the above idea, this paper presents a Deep Supervised Hashing architecture with Nonlinear Projections (DSHNP). In particular, soft decision trees are adopted as the nonlinear projection functions, since they can generate differentiable nonlinear outputs and can be trained with deep neural networks in an end-to-end way. Moreover, to make the hash codes as independent as possible, we design two regularizers imposed on the parameter matrices of the leaves in the soft decision trees. Extensive evaluations on two benchmark image datasets show that the proposed DSHNP outperforms several state-of-the-art hashing methods.
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Eckardt, Robert C. "Scene projection by nonlinear optics." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Wendell R. Watkins and Dieter Clement. SPIE, 1995. http://dx.doi.org/10.1117/12.210583.

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Ren, Ming-Rong, Pu Wang, and Hui-Qing Zhang. "Nonlinear Local Projection Technique for ECG." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.879.

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Clement, Gregory T. "Nonlinear planar forward and backward projection." In 2008 IEEE Ultrasonics Symposium (IUS). IEEE, 2008. http://dx.doi.org/10.1109/ultsym.2008.0442.

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Choudhury, Sanjiban, and Sebastian Scherer. "The Dynamics Projection Filter (DPF) - real-time nonlinear trajectory optimization using projection operators." In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139247.

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Derzhypolska, L. A., N. A. Davidenko, N. V. Medved, and L. D. Pryadko. "Holographic projection interferometer with photorefractive recording media." In Tenth International Conference on Nonlinear Optics of Liquid and Photorefractive Crystals. SPIE, 2005. http://dx.doi.org/10.1117/12.648209.

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Lima, Clodoaldo A. M., Pablo A. D. Castro, Andre L. V. Coelho, Cynthia Junqueira, and Fernando J. Von Zuben. "Controlling Nonlinear Dynamic Systems with Projection Pursuit Learning." In 2006 3rd International IEEE Conference Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/is.2006.348441.

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Daum, V., D. Hahn, and J. Hornegger. "A nonlinear projection scheme for fast rigid registration." In 2007 IEEE Nuclear Science Symposium Conference Record. IEEE, 2007. http://dx.doi.org/10.1109/nssmic.2007.4436995.

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Xianyu Zhao, Yuan Dong, Hao Yang, Jian Zhao, Liang Lu, and Haila Wang. "Nonlinear kernel nuisance attribute projection for speaker verification." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518562.

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Wang, Dan, and Yong-ming Zhang. "Improvement of Gradient Projection Algorithm for Nonlinear Programming." In 2nd International Conference on Teaching and Computational Science. Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/ictcs-14.2014.33.

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Reports on the topic "Nonlinear projection"

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Yoshida, Masashi, Jun Tajima, and Naohiro Yuhara. Perspective Projection With Nonlinear Mapping for Scene Generation of Driving Simulator. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0552.

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Elguedj, T., Y. Bazilevs, V. M. Calo, and T. J. Hughes. B and F Projection Methods for Nearly Incompressible Linear and Nonlinear Elasticity and Plasticity using Higher-order NURBS Elements. Fort Belvoir, VA: Defense Technical Information Center, August 2007. http://dx.doi.org/10.21236/ada478310.

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Grant, Michael. Nonline Officer Projection Model. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada235547.

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Costello, Mark, and Ryan Letniak. A Nonlinear Model Predictive Observer for Smart Projectile Applications. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada478975.

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Dmitriy Y. Anistratov, Adrian Constantinescu, Loren Roberts, and William Wieselquist. Nonlinear Projective-Iteration Methods for Solving Transport Problems on Regular and Unstructured Grids. Office of Scientific and Technical Information (OSTI), April 2007. http://dx.doi.org/10.2172/909188.

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Sparks, Paul, Jesse Sherburn, William Heard, and Brett Williams. Penetration modeling of ultra‐high performance concrete using multiscale meshfree methods. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41963.

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Abstract:
Terminal ballistics of concrete is of extreme importance to the military and civil communities. Over the past few decades, ultra‐high performance concrete (UHPC) has been developed for various applications in the design of protective structures because UHPC has an enhanced ballistic resistance over conventional strength concrete. Developing predictive numerical models of UHPC subjected to penetration is critical in understanding the material's enhanced performance. This study employs the advanced fundamental concrete (AFC) model, and it runs inside the reproducing kernel particle method (RKPM)‐based code known as the nonlinear meshfree analysis program (NMAP). NMAP is advantageous for modeling impact and penetration problems that exhibit extreme deformation and material fragmentation. A comprehensive experimental study was conducted to characterize the UHPC. The investigation consisted of fracture toughness testing, the utilization of nondestructive microcomputed tomography analysis, and projectile penetration shots on the UHPC targets. To improve the accuracy of the model, a new scaled damage evolution law (SDEL) is employed within the microcrack informed damage model. During the homogenized macroscopic calculation, the corresponding microscopic cell needs to be dimensionally equivalent to the mesh dimension when the partial differential equation becomes ill posed and strain softening ensues. Results of numerical investigations will be compared with results of penetration experiments.
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