Academic literature on the topic 'Unknown term estimation'
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Journal articles on the topic "Unknown term estimation"
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Florens, Jean-Pierre, and Senay Sokullu. "NONPARAMETRIC ESTIMATION OF SEMIPARAMETRIC TRANSFORMATION MODELS." Econometric Theory 33, no. 4 (June 6, 2016): 839–73. http://dx.doi.org/10.1017/s0266466616000190.
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In this paper we develop a nonparametric estimation technique for semiparametric transformation models of the form:H(Y) =φ(Z) +X′β+UwhereH,φare unknown functions,βis an unknown finite-dimensional parameter vector and the variables (Y,Z) are endogenous. Identification of the model and asymptotic properties of the estimator are analyzed under the mean independence assumption between the error term and the instruments. We show that the estimators are consistent, and a$\sqrt N$-convergence rate and asymptotic normality for$\hat \beta$can be attained. The simulations demonstrate that our nonparametric estimates fit the data well.
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Zhang, Yuanqing. "Estimation of Partially Specified Spatial Autoregressive Model." Journal of Systems Science and Information 2, no. 3 (June 25, 2014): 226–35. http://dx.doi.org/10.1515/jssi-2014-0226.
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Abstract In this paper, we study estimation of a partially specified spatial autoregressive model with heteroskedasticity error term. Under the assumption of exogenous regressors and exogenous spatial weighting matrix, we propose an instrumental variable estimation. Under some sufficient conditions, we show that the proposed estimator for the finite dimensional parameter is root-n consistent and asymptotically normally distributed and the proposed estimator for the unknown function is consistent and also asymptotically distributed though at a rate slower than root-n. Monte Carlo simulations verify our theory and the results suggest that the proposed method has some practical value.
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Zhang, Xiao, Feng Ding, Ling Xu, Ahmed Alsaedi, and Tasawar Hayat. "A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise." Mathematics 7, no. 4 (April 17, 2019): 356. http://dx.doi.org/10.3390/math7040356.
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This paper is concerned with the joint state and parameter estimation methods for a bilinear system in the state space form, which is disturbed by additive noise. In order to overcome the difficulty that the model contains the product term of the system input and states, we make use of the hierarchical identification principle to present new methods for estimating the system parameters and states interactively. The unknown states are first estimated via a bilinear state estimator on the basis of the Kalman filtering algorithm. Then, a state estimator-based recursive generalized least squares (RGLS) algorithm is formulated according to the least squares principle. To improve the parameter estimation accuracy, we introduce the data filtering technique to derive a data filtering-based two-stage RGLS algorithm. The simulation example indicates the efficiency of the proposed algorithms.
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Zhou, Yanli, Shican Liu, Tianhai Tian, Qi He, and Xiangyu Ge. "Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model." Mathematical Problems in Engineering 2021 (January 4, 2021): 1–11. http://dx.doi.org/10.1155/2021/8893940.
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One of the advantages of stochastic differential equations (SDE) is that they can follow a variety of different trends so that they can establish complex dynamic systems in the economic and financial fields. Although some estimation methods have been proposed to identify the unknown parameters in virtue of the results in the SDE model to speed up the process, these solutions only focus on using explicit approach to solve SDEs, and therefore they are not reliable to deal with data source merged being large and varied. Thus, this study makes progress in creating a new implicit way to fill in the gaps of accurately calibrating the unknown parameters in the SDE model. Essentially, the primary goal of the article is to generate rigid SDE simulation. Meanwhile, the particle swarm optimization method serves a purpose to search and simultaneously obtain the optimal estimation of the model unknown parameters in the complicated experiment of parameter space in an effective way. Finally, in an interest rate term structure model, it is verified that the method effectively deals with parameter estimation in the SDE model.
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Zhang, Zhengyu. "SEMIPARAMETRIC ESTIMATION OF PARTIALLY LINEAR TRANSFORMATION MODELS UNDER CONDITIONAL QUANTILE RESTRICTION." Econometric Theory 32, no. 2 (December 19, 2014): 458–97. http://dx.doi.org/10.1017/s0266466614000887.
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This article is concerned with semiparametric estimation of a partially linear transformation model under conditional quantile restriction with no parametric restriction imposed either on the link functional form or on the error term distribution. We describe for the finite-dimensional parameter a$\sqrt n$-consistent estimator which combines the features of Chen (2010)’s maximum integrated score estimator as well as Lee (2003)’s average quantile regression. We show the remaining two infinite-dimensional unknown functions in the model can be separately identified and propose estimators for these functions based on the marginal integration method. Furthermore, a simple approach is proposed to estimate the average partial quantile effect. Two important extensions, i.e., random censoring as well as estimating a transformation model with an endogenous regressor are also considered.
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Kou, Junke, Qinmei Huang, and Huijun Guo. "Pointwise Wavelet Estimations for a Regression Model in Local Hölder Space." Axioms 11, no. 9 (September 10, 2022): 466. http://dx.doi.org/10.3390/axioms11090466.
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This paper considers an unknown functional estimation problem in a regression model with multiplicative and additive noise. A linear wavelet estimator is first constructed by a wavelet projection operator. The convergence rate under the pointwise error of linear wavelet estimators is studied in local Hölder space. A nonlinear wavelet estimator is provided by the hard thresholding method in order to obtain an adaptive estimator. The convergence rate of the nonlinear estimator is the same as the linear estimator up to a logarithmic term. Finally, it should be pointed out that the convergence rates of two wavelet estimators are consistent with the optimal convergence rate on pointwise nonparametric estimation.
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Chen, Gang, Yong Zhou, TingTing Gao, and Qicai Zhou. "Unknown Disturbance Estimation for a PMSM with a Hybrid Sliding Mode Observer." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/316360.
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A hybrid sliding mode observer that combines high gain feedback and a high-order sliding mode term is developed to identify the time-varying disturbance for a permanent-magnet synchronous motor (PMSM). Based on the measurable current and the position, the unknown disturbance can be identified from the sliding mode term without digital filter effect. It is then used to enhance the robustness of the speed control dynamics. For ease of implementation in real applications, such as DSP and FPGA, the proposed observer is properly designed to avoid complex mathematical operators. Simulation results are given to illustrate the performance of the proposed observer.
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Papadopoulos, Pavlos, Andreas Tsiartas, and Shrikanth Narayanan. "Long-Term SNR Estimation of Speech Signals in Known and Unknown Channel Conditions." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 12 (December 2016): 2495–506. http://dx.doi.org/10.1109/taslp.2016.2615240.
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Yakoub , Zaineb, Omar Naifar, and Dmitriy Ivanov. "Unbiased Identification of Fractional Order System with Unknown Time-Delay Using Bias Compensation Method." Mathematics 10, no. 16 (August 22, 2022): 3028. http://dx.doi.org/10.3390/math10163028.
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In the field of engineering, time-delay is a typical occurrence. In reality, the inner dynamics of many industrial processes are impacted by delay or after-effect events. This paper discusses the identification of continuous-time fractional order system with unknown time-delay using the bias compensated least squares algorithm. The basic concept is to remove the imposed bias by including a correction term into the least squares estimations. The suggested approach makes a significant contribution by the estimation, iteratively, of fractional order system coefficients as well as the orders and the time-delay using a nonlinear optimization algorithm. The main advantage of this method is to provide a simple and powerful algorithm with good accuracy. The suggest method performances are assessed through two numerical examples.
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Li, Liangliang, Xianpeng Wang, Xiang Lan, Gang Xu, and Liangtian Wan. "Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling." Sensors 23, no. 13 (July 6, 2023): 6196. http://dx.doi.org/10.3390/s23136196.
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In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution’s sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.
Dissertations / Theses on the topic "Unknown term estimation"
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Wang, Zhibo. "Estimations non-asymptotiques et robustes basées sur des fonctions modulatrices pour les systèmes d'ordre fractionnaire." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2023. http://www.theses.fr/2023ISAB0003.
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Cette thèse développe la méthode des fonctions modulatrices pour des estimations non-asymptotiques et robustes pour des pseudo-états des systèmes nonlinéaires d'ordre fractionnaire, des systèmes linéaires d'ordre fractionnaire avec des accélérations en sortie, et des systèmes à retards d'ordre fractionnaire. Les estimateurs conçus sont fournis en termes de formules intégrales algébriques, ce qui assure une convergence non-asymptotique. Comme une caractéristique essentielle des algorithmes d'estimation conçus, les mesures de sorties bruitées ne sont impliquées que dans les termes intégraux, ce qui confère aux estimateurs une robustesse contre les bruits. Premièrement, pour les systèmes nonlinéaires d'ordre fractionnaire et partiellement inconnu, l'estimation de la dérivée fractionnaire du pseudo-état est abordée via la méthode des fonctions modulatrices. Grâce à la loi de l'indice additif des dérivées fractionnaires, l'estimation est décomposée en une estimation des dérivées fractionnaires de la sortie et une estimation des valeurs initiales fractionnaires. Pendant ce temps, la partie inconnue est estimée via une stratégie innovante de fenêtre glissante. Deuxièmement, pour les systèmes linéaires d'ordre fractionnaire avec des accélérations comme sortie, l'estimation de l'intégrale fractionnaire de l'accélération est d'abord considérée pour les systèmes mécaniques de vibration d'ordre fractionnaire, où seules des mesures d'accélération bruitées sont disponibles. Basée sur des approches numériques existantes qui traitent des intégrales fractionnaires, notre attention se limite principalement à l'estimation des valeurs initiales inconnues en utilisant la méthode des fonctions modulatrices. Sur cette base, le résultat est ensuite généralisé aux systèmes linéaires plus généraux d'ordre fractionnaire. En particulier, le comportement des dérivées fractionnaires à zéro est étudié pour des fonctions absolument continues, ce qui est assez différent de celui de l'ordre entier. Troisièment, pour les systèmes à retards d'ordre fractionnaire, l'estimation du pseudo-état est étudiée en concevant un système dynamique auxiliaire d'ordre fractionnaire, qui fournit un cadre plus général pour générer les fonctions modulatrices requises. Avec l'introduction de l'opérateur de retard et du changement de coordonnées généralisé bicausal, l'estimation du pseudo-état du système considéré peut être réduite à celle de la forme normale correspondante. Contrairement aux travaux précédents le schéma présenté permet une estimation directe du pseudo-état plutôt que d'estimer les dérivées fractionnaires de la sortie et un ensemble de valeurs initiales fractionnaires. De plus, l'efficacité et la robustesse des estimateurs proposés sont vérifiées par des simulations numériques dans cette thèse. Enfin, un résumé de ce travail et un aperçu des travaux futurs sont tirésThis thesis develops the modulating functions method for non-asymptotic and robust estimations for fractional-order nonlinear systems, fractional-order linear systems with accelerations as output, and fractional-order time-delay systems. The designed estimators are provided in terms of algebraic integral formulas, which ensure non-asymptotic convergence. As an essential feature of the designed estimation algorithms, noisy output measurements are only involved in integral terms, which endows the estimators with robustness against corrupting noises. First, for fractional-order nonlinear systems which are partially unknown, fractional derivative estimation of the pseudo-state is addressed via the modulating functions method. Thanks to the additive index law of fractional derivatives, the estimation is decomposed into the fractional derivatives estimation of the output and the fractional initial values estimation. Meanwhile, the unknown part is fitted via an innovative sliding window strategy. Second, for fractional-order linear systems with accelerations as output, fractional integral estimation of the acceleration is firstly considered for fractional-order mechanical vibration systems, where only noisy acceleration measurements are available. Based on the existing numerical approaches addressing the proper fractional integrals of accelerations, our attention is primarily restricted to estimating the unknown initial values using the modulating functions method. On this basis, the result is further generalized to more general fractional-order linear systems. In particular, the behaviour of fractional derivatives at zero is studied for absolutely continuous functions, which is quite different from that of integer order. Third, for fractional-order time-delay systems, pseudo-state estimation is studied by designing a fractional-order auxiliary modulating dynamical system, which provides a more general framework for generating the required modulating functions. With the introduction of the delay operator and the bicausal generalized change of coordinates, the pseudo-state estimation of the considered system can be reduced to that of the corresponding observer normal form. In contrast to the previous work, the presented scheme enables direct estimation for the pseudo-state rather than estimating the fractional derivatives of the output and a bunch of fractional initial values. In addition, the efficiency and robustness of the proposed estimators are verified by numerical simulations in this thesis. Finally, a summary of this work and an insight into future work were drawn
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Mechhoud, Sarah. "Estimation de la diffusion thermique et du terme source du modèle de transport de la chaleur dans les plasmas de tokamaks." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00954183.
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Cette thèse porte sur l'estimation simultanée du coefficient de diffusion et du terme source régissant le modèle de transport de la température dans les plasmas chauds. Ce phénomène physique est décrit par une équation différentielle partielle (EDP) linéaire, parabolique du second-ordre et non-homogène, où le coefficient de diffusion est distribué et le coefficient de réaction est constant. Ce travail peut se présenter en deux parties. Dans la première, le problème d'estimation est traité en dimension finie ("Early lumping approach"). Dans la deuxième partie, le problème d'estimation est traité dans le cadre initial de la dimension infinie ("Late lumping approach"). Pour l'estimation en dimension finie, une fois le modèle établi, la formulation de Galerkin et la méthode d'approximation par projection sont choisies pour convertir l'EDP de transport en un système d'état linéaire, temps-variant et à entrées inconnues. Sur le modèle réduit, deux techniques dédiées à l'estimation des entrées inconnues sont choisies pour résoudre le problème. En dimension infinie, l'estimation en-ligne adaptative est adoptée pour apporter des éléments de réponse aux contraintes et limitations dues à la réduction du modèle. Des résultats de simulations sur des données réelles et simulées sont présentées dans ce mémoire.
Book chapters on the topic "Unknown term estimation"
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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Bayesian Interpretation of Regularization." In Regularized System Identification, 95–134. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_4.
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AbstractIn the previous chapter, it has been shown that the regularization approach is particularly useful when information contained in the data is not sufficient to obtain a precise estimate of the unknown parameter vector and standard methods, such as least squares, yield poor solutions. The fact itself that an estimate is regarded as poor suggests the existence of some form of prior knowledge on the degree of acceptability of candidate solutions. It is this knowledge that guides the choice of the regularization penalty that is added as a corrective term to the usual sum of squared residuals. In the previous chapters, this design process has been described in a deterministic setting where only the measurement noises are random. In this chapter, we will see that an alternative formalization of prior information is obtained if a subjective/Bayesian estimation paradigm is adopted. The major difference is that the parameters, rather than being regarded as deterministic, are now treated as a random vector. This stochastic setting permits the definition of new powerful tools for both priors selection, e.g., through the maximum entropy principle, and for regularization parameters tuning, e.g., through the empirical Bayes approach and its connection with the concept of equivalent degrees of freedom.
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Hellmundt, Christoph. "‘Indessen wollte ich mich Ihnen gern gefällig beweisen’: On Some Occasional Works, with an Unknown Composition by Mendelssohn." In The Mendelssohns, 169–80. Oxford University PressOxford, 2002. http://dx.doi.org/10.1093/oso/9780198167235.003.0008.
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Abstract My investigation was prompted by a composition by Mendelssohn that has remained virtually unknown until now, and which in my estimation should be considered an ‘occasional work’. What does this term mean? In general, one designates as ‘occasional works’ compositions written for a specific occasion (usually rather hastily) and which achieve no great significance, either in the composer’s œuvre or in musical life in general. Sometimes these are largescale works, but usually they are smaller. If the suggestion or request for the composition comes from an external source, the composer’s own personal impetus plays a subordinate role, since he feels obligated to compose something.
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Wang, Bin, and Tiancong Wang. "An Accurate Cardinality Estimation Scheme for Cloned Tags Under the Capture Effect." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220375.
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The cloning attack is harmful to RFID systems. So, estimating the number of cloned tags is helpful to evaluate potential security risks for RFID systems. This paper studies the problem of estimating the number of cloned tags to present a cardinality estimation scheme CECT when there exist unknown tags and the capture effect. CECT scheme requires a RFID reader to first predict responses of the known tags by a virtual frame executed in terms of the ALOHA protocol. Then the reader collects responses from active tags over a channel with the capture effect. Simulation results show that under the given number of unknown tags and capture effect parameter, CECT can meet the required estimation accuracy and reliability. Under the same parameters, the accuracy is improved by more than 20%.
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Edge, M. D. "Properties of point estimators." In Statistical Thinking from Scratch, 91–110. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198827627.003.0008.
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Point estimation is the attempt to identify a value associated with some underlying process or population using data. The unknown number that is the target of estimation is called an estimand. An estimator is a function that takes in data and produces an estimate. In this chapter, estimators are evaluated according to a number of criteria. An unbiased estimator is one whose expected value is equal to the estimand—in lay terms, it is accurate. Low-variance estimators, which are precise, are also evaluated. Consistent estimators converge to the estimand as the number of data collected approaches infinity. Mean squared error is the expected squared difference between the estimator and the estimand. Efficient estimators are those that converge to the estimand relatively quickly—i.e., fewer data are necessary to get close to the right answer. An optional section discusses statistical decision theory, which is a general framework for evaluating estimators. Finally, some ideas of robustness are discussed. A robust estimator is one that can still provide useful information even if the model is not quite right or the data are contaminated.
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Hutton, J. E., O. T. Ogunyemi,, and Paul I. Nelson. "Simplified and two-stage quasi-likelihood estimators." In Estimating Functions, 169–88. Oxford University PressOxford, 1991. http://dx.doi.org/10.1093/oso/9780198522287.003.0012.
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Abstract Properties of a quasi-likelihood estimator are typically obtained from an expansion of the quasi-likelihood in which the remainder term is asymptotically negligible. The remainder term, however, can be very difficult to control in some important applications, especially in models where both the mean and variance depend on the unknown parameter. In such cases we propose a modified quasi-likelihood based on the use of a simplified covariance matrix. Conditions for the consistency and asymptotic normality of the resulting estimators are developed. We illustrate this approach with a branching process with immigration model and present a simulation study of the small sample behaviour of the estimators.
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Nechval, Nicholas A., and Konstantin N. Nechval. "A New Technique for Optimization of Product Acceptance Process in Terms of Misclassification Probability." In Stochastic Methods for Estimation and Problem Solving in Engineering, 1–32. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5045-7.ch001.
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A product acceptance process is an inspecting one in statistical quality control or reliability tests, which are used to make decisions about accepting or rejecting lots of products to be submitted. This process is important for industrial and business purposes of quality management. To determine the optimal parameters of the product acceptance process under parametric uncertainty of underlying lifetime models (in terms of misclassification probability), a new optimization technique is proposed. The most popular lifetime distribution used in the field of product acceptance is a two-parameter Weibull distribution, with the assumption that the shape parameter is known. Such oversimplified assumptions can facilitate the follow-up analyses, but may overlook the fact that the lifetime distribution can significantly affect the estimation of the failure rate of a product. Therefore, the situations are also considered when both Weibull distribution parameters are unknown. An illustrative numerical example is given.
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Clyde, Merlise A., and Robert L. Wolpert. "Nonparametric Function Estimation Using Overcomplete Dictionaries." In Bayesian Statistics 8, 101–24. Oxford University PressOxford, 2007. http://dx.doi.org/10.1093/oso/9780199214655.003.0004.
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Abstract We consider the problem of estimating an unknown function based on noisy data using nonparametric regression. One approach to this estimation problem is to represent the function in a series expansion using a linear combination of basis functions. Overcomplete dictionaries provide a larger, but redundant collection of generating elements than a basis, however, coefficients in the expansion are no longer unique. Despite the non-uniqueness, this has the potential to lead to sparser representations by using fewer non-zero coefficients. Compound Poisson random fields and their generalization to Lévy random fields are ideally suited for construction of priors on functions using these overcomplete representations for the general nonparametric regression problem, and provide a natural limiting generalization of priors for the finite dimensional version of the regression problem. While expressions for posterior modes or posterior distributions of quantities of interest are not available in closed form, the prior construction using Lévy random fields permits tractable posterior simulation via a reversible jump Markov chain Monte Carlo algorithm. Efficient computation is possible because updates based on adding/deleting or updating single dictionary elements bypass the need to invert large matrices. Furthermore, because dictionary elements are only computed as needed, memory requirements scale linearly with the sample size. In comparison with other methods, the Lévy random field priors provide excellent performance in terms of both mean squared error and coverage for out-of-sample predictions.
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Matsopoulos, George K. "Automatic Correspondence Methods towards Point-Based Medical Image Registration." In Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, 407–25. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-314-2.ch026.
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The accurate estimation of point correspondences is often required in a wide variety of medical image processing applications including image registration. Numerous point correspondence methods have been proposed, each exhibiting its own characteristics, strengths and weaknesses. This chapter presents a comparative study of four automatic point correspondence methods. The four featured methods are the Automatic Extraction of Corresponding Points approach, the Trimmed Iterated Closest Points scheme, the Correspondence by Sensitivity to Movement technique and the Self-Organizing Maps network. All methods are presented, mainly focusing on their distinct characteristics. An extensive set of dental images, subject to unknown transformations, was employed for the qualitative and quantitative evaluation of the four methods, which was performed in terms of registration accuracy. After assessing all methods, it was deduced that the Self-Organizing Maps approach outperformed in most cases the other three methods in comparison.
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"Eels at the Edge: Science, Status, and Conservation Concerns." In Eels at the Edge: Science, Status, and Conservation Concerns, edited by Giulio A. De Leo, Paco Melià, Marino Gatto, and Alain J. Crivelli. American Fisheries Society, 2009. http://dx.doi.org/10.47886/9781888569964.ch22.
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<em>Abstract.—</em>We critically review population dynamics models developed for <em>Anguilla </em>spp. eels. Despite the (quasi) panmictic nature of temperate eel species, most modeling effort has focused on subpopulations within specific brackish or inland water bodies. Models have been developed along three major lines: cohort approaches, input-output models that directly relate juvenile recruit abundance to migrating mature eels, and stage- or size-structured population models, or both, some of which explicitly account for the observed variability of eel life traits. More recently, attempts have been made to extend demographic analyses to the oceanic phase of the eel life cycle. We discuss eel population models in terms of mathematical complexity and usability, amount and quality of data required for calibration, realism in the description of life cycle and demographic parameters, potential for analyzing different fisheries management strategies, and inclusion of environmental and interindividual stochasticity and uncertainty in parameter estimation. While site-specific analyses are needed to understand eel life history in the continental phase, the generalized decline of eel recruitment requires a global assessment of metapopulation viability under different hypotheses and scenarios. Given the high number of unknowns and untested hypotheses, we emphasize the need to explicitly model uncertainty in parameter estimation and environmental and interindividual stochasticity (e.g., by using bootstrap techniques and Monte Carlo simulations). There is an urgent need for population models that can be used for conservation-based eel management in broad geographic areas where few data are available.
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Singer, Donald, and W. David Menzie. "Estimating the Number of Undiscovered Deposits." In Quantitative Mineral Resource Assessments. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780195399592.003.0011.
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The third part of three-part assessments is the estimate of some fixed but unknown number of deposits of each type that exist in the delineated tracts. Until the area being considered is thoroughly and extensively drilled, this fixed number of undiscovered deposits, which could be any number including 0, will not be known with certainty. This number of deposits has meaning only in terms of a grade-and-tonnage model. If this requirement did not exist, any wisp of minerals could be considered worthy of estimation, and even in small regions, we would need to estimate millions of “deposits.” For example, it is not difficult to imagine tens of thousands of fist-sized skarn copper “deposits” in parts of western United States—even in this example, we have used “deposit” size to provide important information. In another example, Wilson et al. (1996) estimated five or more epithermal gold vein deposits at the 90 percent level but provided no grade-and-tonnage model, so these estimated deposits could be any size. To provide critical information to decision-makers, the grade-and-tonnage model is key, and the estimated number of deposits that might exist must be from the grade-and-tonnage frequency distributions. In three-part assessments, the parts and estimates are internally consistent in that delineated tracts are consistent with descriptive models, grade-and-tonnage models are consistent with descriptive models and with known deposits in the area, and estimates of number of deposits are consistent with grade-and-tonnage models. Considerable care must be exercised in quantitative resource assessments to prevent the introduction of biased estimates of undiscovered resources. Biases can be introduced into these estimates either by a flawed grade-and-tonnage model or by the lack of consistency of the grade-and-tonnage model with the number-of-deposit estimates. For this reason, consistency of estimates of number of deposits with the grade-and-tonnage models is the most important guideline. Issues about consistency of mineral deposit models are discussed in chapters 3 through 6. Grade-and-tonnage models (chapter 6), which are the first part of three-part assessments, are of particular concern. In this chapter, the focus is on making unbiased estimates of the number of undiscovered deposits.
Conference papers on the topic "Unknown term estimation"
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Griffith, Tristan D., and Mark J. Balas. "An Adaptive Control Framework for Unknown Input Estimation." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-67484.
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Abstract Many dynamical systems experience inputs that are difficult to measure. Knowledge of these unknown inputs, from estimation techniques, may improve the performance of a system. However, there may be uncertainty in both the linear model of the plant and the unknown input. An architecture for the estimation of an unknown input simultaneously with the plant internal states is presented. The architecture allows for error in the realization of the dynamical model, which is corrected using an adaptive feedback term. This allows the estimator to recover the correct physical structure of the plant dynamics. Crucial to the approach is an internal model of the unknown input which is generated by an ordinary differential equation. Discussion on the advantages and disadvantages of the input generator follow, along with general considerations for the selection of basis functions for an unknown function space. Convergence proofs are presented along with illustrative examples to demonstrate the theoretical results. This novel scheme will allow for the reliable online estimates of an unknown input with known waveform while also recovering the physical structure of the internal dynamics.
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Wang, Zhi-Pu, and Huai-Ning Wu. "Source Term Estimation with Unknown Number of Sources using Improved Cuckoo Search Algorithm." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189067.
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Soize, Christian. "Estimation of the Fuzzy Substructure Model Parameters Using the Mean Power Flow Equation of the Fuzzy Structure." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0505.
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Abstract This paper presents a theoretical approach for identifying the dimensionless mean coefficient of participating fuzzy mass which is the main unknown parameter of the type I or II fuzzy law previously introduced by the author. This method is based on the use of the associated power flow equation, each power term being identified by using a global statistical energy analysis of the fuzzy structure (master structure with its fuzzy substructures). Identification is then carried out by solving a nonlinear constrained optimization problem. An example is given to illustrate the theoretical results.
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Frieden, B. Roy. "Probability-law estimation by a principle of minimum physical information." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.tudd5.
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A principle I–J = min. of minimum Fisher information I = ∫dxp'2(x)/p(x) is often used to estimate an unknown probability density function p(x) in the presence of insufficient information J. The latter is conventionally a Lagrange constraint term. The resulting estimate p(x) may or may not be correct (where there is a "correct" answer), although it is smooth. But suppose the correct answer is required and the user has the resources to choose the particular information J to input. What form J should be chosen, and what form should I – J ≡K take, where K is the "physical" information? The choice of J (and K) follows from three reasonable axioms: (i) K is linear in I, i.e., K measures disorder, (ii) K is a universal value, irrespective of physical phenomenon, i.e., all phenomena p(x) are equivalent in their information content; (iii) there is no preferred space, direct or Fourier, for representing K. The solution is that K = 0 or, numerically, J = 1. However, functionally J depends upon p(x) differently than does I; this dependence, which often arises out of Parseval's theorem, is the required input of information. We show that many of the known laws p(x) of physics may be "estimated" in this way.
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ZHU, ZIMO, and SONGYE ZHU. "JOINT INPUT AND STATE ESTIMATION OF A SCALED WIND TURBINE MODEL: AN EXPERIMENTAL STUDY." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/37046.
Full textAbstract:
Structural health monitoring (SHM) has gained recognition in civil engineering and is now a well-established technique. Recently, it has received considerable research attention in the application in Wind turbine (WT) structures. A complete sensing system could provide comprehensive information about the structure during long-term operations. In this study, a 1:50 scaled WT model of the 5-MW reference WT presented by the National Renewable Energy Laboratory (NREL) was designed and manufactured, and a comprehensive SHM sensing system was installed on the WT tower. The system included accelerometers, strain gauges, and as well as a camera system that integrated digital image correlation (DIC) technology and binocular stereo vision technology. By employing the joint input and state estimation (JISE) technique, both the unmonitored structural responses and the unknown interface input were estimated. The study found that blade rotation exerted significant effects on the WT tower responses. The feasibility of WT SHM using the camera system was also validated and discussed in the research. These findings establish a basis for future dynamic analysis of WT structures using the estimated responses and input. Moreover, the study results present a cost-effective approach for monitoring the structural health of WT systems.
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Gok, Ihsan Murat, Muharrem Hilmi Cevik, and Murat Cinar. "A New Approach to Estimate Friction Factor for Horizontal Wells from Production Logging Data." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22242-ea.
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Abstract Conventional friction factor estimation is a challenging task for high flow rate horizontal wells since the roughness is an unknown parameter. In addition, pipe roughness may change with operational time due to the development of scale and corrosion in the horizontal section. This study aims to propose a methodology to estimate frictional pressure losses for horizontal wells by using production logging (PL) data. Prediction of pressure loss in horizontal well along perforations or open-hole is quite complicated due to complication in pressure drop. Different components of the pressure drop along the horizontal section are exist: Gravitation, acceleration, inflow/outflow, and finally friction. Estimation of frictional loss in horizontal wells needs to decompose of each term from total pressure drop in horizontal well. Data acquired during production logging provides an opportunity to determine friction factor since it indicates inflow/outflow zone with individual downhole flow rates of each phase. We clearly showed that friction pressure losses can be estimated for horizontal wells using PL data in certain cases. We verified this approach with synthetic PL data.
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Inoue, Tomoya, Tokihiro Katsui, Junya Ishiwata, Miki Y. Matsuo, and C. K. Rheem. "Estimation of Stick-Slip Characteristics by Simulation With Actual Drilling Data." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54319.
Full textAbstract:
Stick-slip, a term for the spontaneous torsional vibrations occurring on a drill bit, is a critical problem in offshore drilling because of the damage it causes to the drill bits. Moreover, it leads to crushing or grinding of the sediment layer, which is a critical problem especially in scientific drilling where the purpose is to recover core samples from the layers. To detect the occurrence of stick-slip and its fundamental characteristics onboard in real-time, we developed an analytical model is developed to express the torsional motions at the drill bit, leading to the stick-slip phenomenon. The analytical model includes the unknown frictional torque at the drill bit, which depends on the properties of the drilling layers and is a dominant factor for demonstrating the stick-slip phenomenon. This study proposes a method to determine the frictional torque by integrating the analytical model with the surface drilling data. It would be advantageous to identify the characteristics of stick-slip from the surface drilling data. The method is validated with the results of small-scale model experiments in a water tank to observe the stick-slip phenomenon. Furthermore, we acquired the actual drilling data, which are surface drilling data and downhole data, during the drilling operation of the scientific drilling vessel Chikyu. This study focuses on attempts to estimate the stick-slip characteristics by the simulation based on the established analytical model with actual surface drilling data acquired during the drilling operation of the scientific drilling vessel Chikyu, and a comparison with the downhole data including the bit motions.
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Yu, Shu-Wen, Sandipan Mishra, and Masayoshi Tomizuka. "On-Line Force Ripple Identification and Compensation in Precision Positioning of Wafer Stages." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42563.
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This paper presents the design and implementation of a composite controller to reduce the effect of force ripple in a linear motor wafer stage system. The composite controller consists of two components: 1) a PID feedback controller and 2) an adaptive feedforward compensator. The feedback controller is tuned to achieve good transient response and enhanced robustness of the system. Force ripples are a major source of tracking error in linear motor precision positioning systems. An approximation of the nonlinear force ripple model can be obtained by expressing the ripple as the sum of a sequence of sinusoidal harmonics, multiplied by the motor current. The force ripple is first approximated by on-line adaptive estimation of the unknown coefficients associated with each harmonic, and then compensated with a feedforward term. Experimental results on a prototype single degree of freedom wafer stage are presented to show the performance improvement obtained by the proposed control scheme.
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Gullaksen, Joannes. "Implementing a Method for Meteorological and Oceanographic Climate and Data Evaluation." In ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-101799.
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Abstract During the initial phase of the design of an offshore renewable or sustainable energy project, it may prove useful to carry out environmental climate assessment as part of a methodological study to assess the suitability of an offshore renewable energy project. This paper presents description of extreme design mode implemented in a software application designed to support screening of meteorological and oceanographic climates. The method of estimating extreme values is controlled by the availability of data, such as known or unknown probability distributions, maximum wave height, and available time series of sample data. The implementation takes into account both global models and event models in the analysis strategy. The global model strategy describes the long-term variation of the environmental climate in terms of generic distributions, such as marginal distribution models established by adapting the entire dataset from the geographical location using different fitting techniques. In the event model strategy, the extreme value analysis includes selection of data for statistical analysis, sample selection method, model distributions, fitting methods, goodness of fit test and return period values. The criteria for choosing the fitting method are based on minimizing uncertainty and bias in the extreme value estimation, where the test of whether the adaptation method meets the criteria is carried out using a simulation technique. Other important topics, though not covered in this paper, include load analysis, which involves numerical methods for complete models. This application is based on available meteorological and oceanographic data as input to establish climate, estimate design conditions, and select and apply a theory. However, the application can also be used where time series analysis and spectral analysis are already in place. This can have benefits, such as improving the quality of environmental impact analysis.
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Schumann, Dorothea, Michael Wohlmuther, and Jo¨rg Neuhausen. "Accelerator Waste: A New Challenge for Radioanalytics." In The 11th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2007. http://dx.doi.org/10.1115/icem2007-7008.
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The Paul Scherrer Institute (PSI) at Villigen (Switzerland) operates the most powerful accelerator facility in Europe. Due to the increasing quantities of accelerator waste with almost unknown radionuclide inventory, the development of new radio-analytical methods is an urgent task. Besides the characterization by γ-measurements and dose rate determinations, also the investigation of long-lived radionuclides, being probably essential for a final disposal, is required from Swiss authorities. Chemical separation is necessary for the determination of the majority of these isotopes. As a representive example for such studies, the analytics of a beam dump assembly is introduced. Samples were taken from the target E beam dump station from the 590 MeV proton accelerator facility. The content of several radionuclides with half-lives between 2 and 107 years was determined by γ-spectrometry and, after chemical separation, by Liquid Scintillation Counting (LSC) as well as Accelerator Mass Spectrometry (AMS). The results are compared with theoretical predictions. Long-term object of these studies is the elaboration of nuclide vectors, which allow the estimation of nuclide inventories by simple calculations.
Reports on the topic "Unknown term estimation"
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Rahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.
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This study presents a major development to the wave method, a methodology used for structural identification and monitoring. The research team tested the method for use in structural damage detection and damage localization in bridges, the latter being a challenging task. The main goal was to assess capability of the improved method by applying it to a shake-table-tested prototype bridge with sparse instrumentation. The bridge was a 4-span reinforced concrete structure comprising two columns at each bent (6 columns total) and a flat slab. It was tested to failure using seven biaxial excitations at its base. Availability of a robust and verified method, which can work with sparse recording stations, can be valuable for detecting damage in bridges soon after an earthquake. The proposed method in this study includes estimating the shear (cS) and the longitudinal (cL) wave velocities by fitting an equivalent uniform Timoshenko beam model in impulse response functions of the recorded acceleration response. The identification algorithm is enhanced by adding the model’s damping ratio to the unknown parameters, as well as performing the identification for a range of initial values to avoid early convergence to a local minimum. Finally, the research team detect damage in the bridge columns by monitoring trends in the identified shear wave velocities from one damaging event to another. A comprehensive comparison between the reductions in shear wave velocities and the actual observed damages in the bridge columns is presented. The results revealed that the reduction of cS is generally consistent with the observed distribution and severity of damage during each biaxial motion. At bents 1 and 3, cS is consistently reduced with the progression of damage. The trends correctly detected the onset of damage at bent 1 during biaxial 3, and damage in bent 3 during biaxial 4. The most significant reduction was caused by the last two biaxial motions in bents 1 and 3, also consistent with the surveyed damage. In bent 2 (middle bent), the reduction trend in cS was relatively minor, correctly showing minor damage at this bent. Based on these findings, the team concluded that the enhanced wave method presented in this study was capable of detecting damage in the bridge and identifying the location of the most severe damage. The proposed methodology is a fast and inexpensive tool for real-time or near real-time damage detection and localization in similar bridges, especially those with sparsely deployed accelerometers.
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Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, November 2004. http://dx.doi.org/10.32747/2004.7587220.bard.
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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.