1,328 research outputs found
State estimation in chemometrics : the Kalman filter and beyond /
No full textThis unique text blends together state estimation and chemometrics for the application of advanced data-processing techniques. It further applies system theory in order to develop a modular framework to be implemented on computer for the development of simple intelligent analyzers. Short reviews on the history of state estimation and chemometrics are given, together with examples of the applications described, including classical estimation, state estimation, non-linear estimation, the multi-component, calibration and titration systems and the Kalman filter. The contents are very systematic and build the ideas up logically to appeal to specialist post-graduates working in this area, together with professionals in other areas of chemistry and engineering. Blends together state estimation and chemometrics for the application of advanced data-processing techniquesProvides short reviews on the history of state estimation and chemometrics, together with examples of the applications described.Includes bibliographical references and index.Print version record.This unique text blends together state estimation and chemometrics for the application of advanced data-processing techniques. It further applies system theory in order to develop a modular framework to be implemented on computer for the development of simple intelligent analyzers. Short reviews on the history of state estimation and chemometrics are given, together with examples of the applications described, including classical estimation, state estimation, non-linear estimation, the multi-component, calibration and titration systems and the Kalman filter. The contents are very systematic and build the ideas up logically to appeal to specialist post-graduates working in this area, together with professionals in other areas of chemistry and engineering. Blends together state estimation and chemometrics for the application of advanced data-processing techniquesProvides short reviews on the history of state estimation and chemometrics, together with examples of the applications described.Cover; ABOUT OUR AUTHOR; STATE ESTIMATION IN CHEMOMETRICS: The Kalman Filter and Beyond; Copyright; Contents; 1 Introduction; 1.1 History; 1.2 Chemometrics; 1.3 System view; 2 Classical estimation; 2.1 Linear model; 2.2 Least squares; 2.3 Curve fitting; 2.4 Recursive approach; 2.5 Examples; 3 State estimation; 3.1 State space model; 3.2 Intermezzo; 3.3 Prediction; 3.4 Filtering; 3.5 Kalman filter; 3.6 Smoothing; 3.7 Examples; 4 Statistics; 4.1 Verification; 4.2 Evaluation; 4.3 Selection; 4.4 Normality; 4.5 Example; 5 Non-linear estimation; 5.1 Non-linear state space model.5.2 Extended Kalman filter5. 3 Iterated extended Kalman filter; 5.4 Iterated linear filter-smoother; 5.5 Non-linear smoothing; 5.6 Examples; 6 The multicomponent system; 6.1 Multicomponent analysis; 6.2 Stochastic drift; 6.3 Examples; 7 The calibration system; 7.1 Linear calibration; 7.2 Non-linear calibration; 7.3 Examples; 8 The titration system; 8.1 Discrete titration; 8.2 Continuous titration; 8.3 Non-linear model; 8.4 Examples; 9 Miscellaneous; 9.1 Multiple modeling; 9.2 Principal components; 9.3 Examples; Appendix; Matrix fundamentals; Bibliography; Index.Elsevie
A low dimensional Kalman filter for systems with lagged observables
No full textThis note describes how the Kalman filter can be modified to allow for the vector of observables to be a function of lagged variables without increasing the dimension of the state vector in the filter. This is useful in applications where it is desirable to keep the dimension of the state vector low. The modified filter and accompanying code (which nests the standard filter) can be used to compute (i) the steady state Kalman filter (ii) the log likelihood of a parameterized state space model conditional on a history of observables (iii) a smoothed estimate of latent state variables and (iv) a draw from the distribution of latent states conditional on a history of observables.Kalman filter, lagged observables, Kalman smoother, simulation smoother
Tensor network Kalman filter for LTI systems
No full textAn extension of the Tensor Network (TN) Kalman filter [2], [3] for large scale LTI systems is presented in this paper. The TN Kalman filter can handle exponentially large state vectors without constructing them explicitly. In order to have efficient algebraic operations, a low TN rank is required. We exploit the possibility to approximate the covariance matrix as a TN with a low TN rank. This reduces the computational complexity for general SISO and MIMO LTI systems with TN rank greater than one significantly while obtaining an accurate estimation. Improvements of this method in terms of computational complexity compared to the conventional Kalman filter are demonstrated in numerical simulations for large scale systems.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Raf Van de PlasTeam Jan-Willem van Wingerde
Henri Temianka Correspondence; (kalman)
No full textThis collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/3818/thumbnail.jp
Kalman filter data assimilation: Targeting observations and parameter estimation
No full textabstract: This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.Copyright 2014 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. along with the following message: The following article appeared in 24, 2 (2014) and may be found at http://dx.doi.org/10.1063/1.487191
Henri Temianka Correspondence; (kalman)
No full textThis collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/3816/thumbnail.jp
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
No full textThis is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
Degrees of Kalman varieties of tensors
No full textFunding Information: The idea of this project was conceived during Shahidi's postdoc at Università di Firenze. We thank Giorgio Ottaviani for very useful discussions and encouragement. We thank Jan Draisma for explaining to us a way of deriving the equations in Example 41 . The first author thanks Dr. Alireza Firoozfar and Dr. Mohsen Afsharchi for their support. The second author is partially supported by the Academy of Finland Grant 323416 . During most of the preparation of the manuscript, the third author was a postdoc at Universität Bern, supported by Vici Grant 639.033.514 of Jan Draisma from the Netherlands Organisation for Scientific Research . We thank two anonymous referees for their useful comments and questions that also helped to improve the presentation. Publisher Copyright: © 2022 The Author(s)Kalman varieties of tensors are algebraic varieties consisting of tensors whose singular vector k-tuples lay on prescribed subvarieties. They were first studied by Ottaviani and Sturmfels in the context of matrices. We extend recent results of Ottaviani and the first author to the partially symmetric setting. We describe a generating function whose coefficients are the degrees of these varieties and we analyze its asymptotics, providing analytic results à la Zeilberger and Pantone. We emphasize the special role of isotropic vectors in the spectral theory of tensors and describe the totally isotropic Kalman variety as a dual variety.Peer reviewe
The implementation and evaluation of a Kalman filter in MIAS
No full textThis report describes least squares estimation (LSE) and Kalman filtering in integrated navigation. The Kalman filter and its underlying principles are treated. The implementation of a Kalman filter in MIAS is investigated, and two different Kalman filters are developed. The problems that occur in a hybrid environment as the asynchronicity of the measurement sources and correction for sensor displacement from the aircraft center of gravity are solved. A flight test is performed to test MIAS. This flight test forms the basis for an analysis that is done to evaluate the performance of the two Kalman filters relative to weighted least squares estimation. These results indicate that MIAS can satisfy CAT III landing system requirements without DME/P, while the Kalman filter gives a relative improvement of accuracy at far range from the MLS datum point with respect to the weighted least squares estimator.Electrical Engineering, Mathematics and Computer ScienceTelecommunicatie- en Verkeersbegeleidingssysteme
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Constraint based personalization
No full textPersonalization is defined as a process that facilitates interaction among consumers and providers such that individual consumers are enabled to more readily access the content and services of providers, and individual providers are enabled to more effectively and easily deliver their content and services to consumers. This project presents a personalization framework for web and e-business applications that builds on the architectural model presented by In.stone in [3] as well as foundation work presented by Toth in [1]. This personalization framework incorporates consumer-provider attributes; considers system/network variables; and models personalization as a constraint-based problem. An initial working prototype constraint engine for processing personalization preferences is also built for a prolific messaging application. In this messaging application users collaborate with each other and with web applications using email, instant messaging and short text messaging services employing their PCs, Personal Digital Assistants (PDAs) and cell phones. The users define all the communication channels have a single ID for all their communication channels.
manage their messaging channels by defining preferences 011 how to use their channels. These preferences are then processed by the application through the constraint engine to personalize their communication through the messaging channels
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