Relevant bibliographies by topics / Estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 October 2024

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Journal articles on the topic "Estimation"

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Bai, Wenyuan, Xinhui Zhang, Zhen Gao, Shuyu Xie, Ke Peng, and Yu Chen. "Sensorless Coestimation of Temperature and State-of-Charge for Lithium-Ion Batteries Based on a Coupled Electrothermal Model." International Journal of Energy Research 2023 (February 6, 2023): 1–18. http://dx.doi.org/10.1155/2023/4021256.

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Accurate estimations of the temperature and the state-of-charge (SOC) are of extreme importance for the safety of lithium-ion battery operation. Traditional battery temperature and SOC estimation methods often omit the relation between battery temperature and SOC, which may lead to significant errors in the estimations. This study presents a coupled electrothermal battery model and a coestimation method for simultaneously estimating the temperature and SOC of lithium-ion batteries. The coestimation method is performed by a coupled model-based dual extended Kalman filter (DEKF). The coupled estimators utilizing electrochemical impedance spectroscopy (EIS) measurements, rather than utilizing direct battery surface measurements, are adopted to estimate the battery temperature and SOC, respectively. The information being exchanged between the temperature estimator and the SOC estimator effectively improves the estimation accuracy. Extensive experiments show that, in contrast with the EKF-based separate estimation method, the DEKF-based coestimation method is more favorable in reducing errors for estimating both the temperature and SOC even if the battery core temperature has increased by 17°C or more during the process of test.
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IRFAGUTAMI, NI PUTU NIA, I. GUSTI AYU MADE SRINADI, and I. WAYAN SUMARJAYA. "PERBANDINGAN REGRESI ROBUST PENDUGA MM DENGAN METODE RANDOM SAMPLE CONSENSUS DALAM MENANGANI PENCILAN." E-Jurnal Matematika 3, no. 2 (May 31, 2014): 45. http://dx.doi.org/10.24843/mtk.2014.v03.i02.p065.

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The presence of outliers in observation can result in biased in parameter estimation using ordinary least square (OLS). Robust regression MM-estimator is one of the estimations methods that able to obtain a robust estimator against outliers. Random sample consensus (ransac) is another method that can be used to construct a model for observations data and also estimating a robust estimator against outliers. Based on the study, ransac obtained model with less biased estimator than robust regression MM-estimator.
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Thanoon, Shaymaa Riyadh. "A comparison between Bayes estimation and the estimation of the minimal unbiased quadratic Standard of the bi-division variance analysis model in the presence of interaction." Tikrit Journal of Pure Science 25, no. 2 (March 17, 2020): 116. http://dx.doi.org/10.25130/j.v25i2.966.

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In this study, the variance compounds parameters of the mixed bi-division variance analysis sample are estimated. This estimation is obtained, by Bayes quadratic unbiased estimator. The second way to estimate variance compounds parameters of a suggested tow-way analysis of variance mixed model with interaction. estimation is done out by the approach called (MINQUÉ). The estimation approach is conducted on true obtained from departments at the college of agriculture/university of Mosul. These data represent the development of growing various kinds of tomato so that the development represents three factors: the first is tomato kind, this is the first factor (H) and the factor of natural fertilizer rate, and this is the second factor (M), and the interaction between the two factors (HM). A random sample is taken from these data in order to get the random linear sample. The elementary values estimated by Bayes unbiased estimator are very much close to those estimated by variance analysis style when compared with the estimated values of the variance estimation parameters done by minimum standard quadratic unbiased estimation. The elementary values represent random linear sample parameters used to estimate minimum quadratic unbiased standard. The elementary values of the estimations are also obtained via analyzing bi-division variance, then these estimations are employed in estimating minimum quadratic unbiased standard. the estimation results by Bayes approach are very similar to those done by variance analysis http://dx.doi.org/10.25130/tjps.25.2020.038
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Liu, Bing, Zhen Chen, Xiangdong Liu, and Fan Yang. "An Efficient Nonlinear Filter for Spacecraft Attitude Estimation." International Journal of Aerospace Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/540235.

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Increasing the computational efficiency of attitude estimation is a critical problem related to modern spacecraft, especially for those with limited computing resources. In this paper, a computationally efficient nonlinear attitude estimation strategy based on the vector observations is proposed. The Rodrigues parameter is chosen as the local error attitude parameter, to maintain the normalization constraint for the quaternion in the global estimator. The proposed attitude estimator is performed in four stages. First, the local attitude estimation error system is described by a polytopic linear model. Then the local error attitude estimator is designed with constant coefficients based on the robustH2filtering algorithm. Subsequently, the attitude predictions and the local error attitude estimations are calculated by a gyro based model and the local error attitude estimator. Finally, the attitude estimations are updated by the predicted attitude with the local error attitude estimations. Since the local error attitude estimator is with constant coefficients, it does not need to calculate the matrix inversion for the filter gain matrix or update the Jacobian matrixes online to obtain the local error attitude estimations. As a result, the computational complexity of the proposed attitude estimator reduces significantly. Simulation results demonstrate the efficiency of the proposed attitude estimation strategy.
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Wu, Renzhi, Bolin Ding, Xu Chu, Zhewei Wei, Xiening Dai, Tao Guan, and Jingren Zhou. "Learning to be a statistician." Proceedings of the VLDB Endowment 15, no. 2 (October 2021): 272–84. http://dx.doi.org/10.14778/3489496.3489508.

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Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems, such as columnstore compression and data profiling. In this work, we focus on how to derive accurate NDV estimations from random (online/offline) samples. Such efficient estimation is critical for tasks where it is prohibitive to scan the data even once. Existing sample-based estimators typically rely on heuristics or assumptions and do not have robust performance across different datasets as the assumptions on data can easily break. On the other hand, deriving an estimator from a principled formulation such as maximum likelihood estimation is very challenging due to the complex structure of the formulation. We propose to formulate the NDV estimation task in a supervised learning framework, and aim to learn a model as the estimator. To this end, we need to answer several questions: i) how to make the learned model workload agnostic; ii) how to obtain training data; iii) how to perform model training. We derive conditions of the learning framework under which the learned model is workload agnostic , in the sense that the model/estimator can be trained with synthetically generated training data, and then deployed into any data warehouse simply as, e.g. , user-defined functions (UDFs), to offer efficient (within microseconds on CPU) and accurate NDV estimations for unseen tables and workloads. We compare the learned estimator with the state-of-the-art sample-based estimators on nine real-world datasets to demonstrate its superior estimation accuracy. We publish our code for training data generation, model training, and the learned estimator online for reproducibility.
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Sugiyama, Masashi, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, and Ichiro Takeuchi. "Density-Difference Estimation." Neural Computation 25, no. 10 (October 2013): 2734–75. http://dx.doi.org/10.1162/neco_a_00492.

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We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, this procedure does not necessarily work well because the first step is performed without regard to the second step, and thus a small estimation error incurred in the first stage can cause a big error in the second stage. In this letter, we propose a single-shot procedure for directly estimating the density difference without separately estimating two densities. We derive a nonparametric finite-sample error bound for the proposed single-shot density-difference estimator and show that it achieves the optimal convergence rate. We then show how the proposed density-difference estimator can be used in L2-distance approximation. Finally, we experimentally demonstrate the usefulness of the proposed method in robust distribution comparison such as class-prior estimation and change-point detection.
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Talakua, Mozart W., and Jefri Tipka. "ESTIMASI PARAMETER DISTRIBUSI EKPONENSIAL PADA LOKASI TERBATAS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 1, no. 2 (December 1, 2007): 1–7. http://dx.doi.org/10.30598/barekengvol1iss2pp1-7.

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The common method in Estimating Parameter Distribution Exponential at Finite Location is Maximum Likelihood Estimation (MLE).The best estimator is consistent estimator. Because of The Mean Square Error (MSE) can be used in comparing some detectable estimators that it had looking for with Maximum Likelihood Estimation (MLE) so can find the consistent estimator in Estimating Parameter Distribution Exponential At Finite Location
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Chamidah, Nur, Budi Lestari, I. Nyoman Budiantara, and Dursun Aydin. "Estimation of Multiresponse Multipredictor Nonparametric Regression Model Using Mixed Estimator." Symmetry 16, no. 4 (March 25, 2024): 386. http://dx.doi.org/10.3390/sym16040386.

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In data analysis using a nonparametric regression approach, we are often faced with the problem of analyzing a set of data that has mixed patterns, namely, some of the data have a certain pattern and the rest of the data have a different pattern. To handle this kind of datum, we propose the use of a mixed estimator. In this study, we theoretically discuss a developed estimation method for a nonparametric regression model with two or more response variables and predictor variables, and there is a correlation between the response variables using a mixed estimator. The model is called the multiresponse multipredictor nonparametric regression (MMNR) model. The mixed estimator used for estimating the MMNR model is a mixed estimator of smoothing spline and Fourier series that is suitable for analyzing data with patterns that partly change at certain subintervals, and some others that follow a recurring pattern in a certain trend. Since in the MMNR model there is a correlation between responses, a symmetric weight matrix is involved in the estimation process of the MMNR model. To estimate the MMNR model, we apply the reproducing kernel Hilbert space (RKHS) method to penalized weighted least square (PWLS) optimization for estimating the regression function of the MMNR model, which consists of a smoothing spline component and a Fourier series component. A simulation study to show the performance of proposed method is also given. The obtained results are estimations of the smoothing spline component, Fourier series component, MMNR model, weight matrix, and consistency of estimated regression function. In conclusion, the estimation of the MMNR model using the mixed estimator is a combination of smoothing spline component and Fourier series component estimators. It depends on smoothing and oscillation parameters, and it has linear in observation and consistent properties.
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Zerdali, Emrah, and Murat Barut. "Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors." Power Electronics and Drives 3, no. 1 (December 1, 2018): 115–27. http://dx.doi.org/10.2478/pead-2018-0002.

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Abstract This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.
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Note, Yuya, Masahito Watanabe, Hiroaki Yoshimura, Takaharu Yaguchi, and Toshiaki Omori. "Sparse Estimation for Hamiltonian Mechanics." Mathematics 12, no. 7 (March 25, 2024): 974. http://dx.doi.org/10.3390/math12070974.

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Estimating governing equations from observed time-series data is crucial for understanding dynamical systems. From the perspective of system comprehension, the demand for accurate estimation and interpretable results has been particularly emphasized. Herein, we propose a novel data-driven method for estimating the governing equations of dynamical systems based on machine learning with high accuracy and interpretability. The proposed method enhances the estimation accuracy for dynamical systems using sparse modeling by incorporating physical constraints derived from Hamiltonian mechanics. Unlike conventional approaches used for estimating governing equations for dynamical systems, we employ a sparse representation of Hamiltonian, allowing for the estimation. Using noisy observational data, the proposed method demonstrates a capability to achieve accurate parameter estimation and extraction of essential nonlinear terms. In addition, it is shown that estimations based on energy conservation principles exhibit superior accuracy in long-term predictions. These results collectively indicate that the proposed method accurately estimates dynamical systems while maintaining interpretability.
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Dissertations / Theses on the topic "Estimation"

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Yu, Kan Chi Kent. "Harmonic State Estimation and Transient State Estimation." Thesis, University of Canterbury. Electrical and Computer Engineering, 2006. http://hdl.handle.net/10092/1108.

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This thesis describes the algorithms and techniques developed for harmonic state estimation and transient state estimation, which can be used to identify the location of disturbance sources in an electrical power system. The previous harmonic state estimation algorithm is extended to include the estimation of time-varying harmonics using an adaptive Kalman filter. The proposed method utilises two covariance noise models to overcome the divergence problem in traditional Kalman filters. Moreover, it does not require an optimal covariance noise matrix of the Kalman filter to be used. The common problems faced in harmonic state estimation applications due to the influence of measurement bad data associated with measurements and the lack of measurement points, hence the system being partially observable, are investigated with reference to the Lower South Island of the New Zealand system. The state estimation technique is also extended to transient state estimation. Two formulation methods are outlined and the development of the proposed methodology is presented. Fault scenarios with reference to the Lower South Island of the New Zealand system are simulated to demonstrate the ability of transient state estimation in estimating the voltages and currents of the unmeasured locations, and applying the estimated results to search for the fault location. The estimation results are compared with PSCAD/EMTDC simulations to justify their accuracy.
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Telmoudi, Fedya. "Estimation and misspecification Risks in VaR estimation." Thesis, Lille 3, 2014. http://www.theses.fr/2014LIL30061/document.

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Dans cette thèse, nous étudions l'estimation de la valeur à risque conditionnelle (VaR) en tenant compte du risque d'estimation et du risque de modèle. Tout d'abord, nous considérons une méthode en deux étapes pour estimer la VaR. La première étape évalue le paramètre de volatilité en utilisant un estimateur quasi maximum de vraisemblance généralisé (gQMLE) fondé sur une densité instrumentale h. La seconde étape estime un quantile des innovations à partir du quantile empirique des résidus obtenus dans la première étape. Nous donnons des conditions sous lesquelles l'estimateur en deux étapes de la VaR est convergent et asymptotiquement normal. Nous comparons également les efficacités des estimateurs obtenus pour divers choix de la densité instrumentale h. Lorsque l'innovation n'est pas de densité h, la première étape donne généralement un estimateur biaisé de paramètre de volatilité et la seconde étape donne aussi un estimateur biaisé du quantile des innovations. Cependant, nous montrons que les deux erreurs se contrebalancent pour donner une estimation consistante de la VaR. Nous nous concentrons ensuite sur l'estimation de la VaR dans le cadre de modèles GARCH en utilisant le gQMLE fondé sur la classe des densités instrumentales double gamma généralisées qui contient la distribution gaussienne. Notre objectif est de comparer la performance du QMLE gaussien par rapport à celle du gQMLE. Le choix de l'estimateur optimal dépend essentiellement du paramètre d qui minimise la variance asymptotique. Nous testons si le paramètre d qui minimise la variance asymptotique est égal à 2. Lorsque le test est appliqué sur des séries réelles de rendements financiers, l'hypothèse stipulant l'optimalité du QMLE gaussien est généralement rejetée. Finalement, nous considérons les méthodes non-paramétriques d'apprentissage automatique pour estimer la VaR. Ces méthodes visent à s'affranchir du risque de modèle car elles ne reposent pas sur une forme spécifique de la volatilité. Nous utilisons la technique des machines à vecteurs de support pour la régression (SVR) basée sur la fonction de perte moindres carrés (en anglais LS). Pour améliorer la solution du modèle LS-SVR nous utilisons les modèles LS-SVR pondérés et LS-SVR de taille fixe. Des illustrations numériques mettent en évidence l'apport des modèles proposés pour estimer la VaR en tenant compte des risques de spécification et d'estimation
In this thesis, we study the problem of conditional Value at Risk (VaR) estimation taking into account estimation risk and model risk. First, we considered a two-step method for VaR estimation. The first step estimates the volatility parameter using a generalized quasi maximum likelihood estimator (gQMLE) based on an instrumental density h. The second step estimates a quantile of innovations from the empirical quantile of residuals obtained in the first step. We give conditions under which the two-step estimator of the VaR is consistent and asymptotically normal. We also compare the efficiencies of the estimators for various instrumental densities h. When the distribution of is not the density h the first step usually gives a biased estimator of the volatility parameter and the second step gives a biased estimator of the quantile of the innovations. However, we show that both errors counterbalance each other to give a consistent estimate of the VaR. We then focus on the VaR estimation within the framework of GARCH models using the gQMLE based on a class of instrumental densities called double generalized gamma which contains the Gaussian distribution. Our goal is to compare the performance of the Gaussian QMLE against the gQMLE. The choice of the optimal estimator depends on the value of d that minimizes the asymptotic variance. We test if this parameter is equal 2. When the test is applied to real series of financial returns, the hypothesis stating the optimality of Gaussian QMLE is generally rejected. Finally, we consider non-parametric machine learning models for VaR estimation. These methods are designed to eliminate model risk because they are not based on a specific form of volatility. We use the support vector machine model for regression (SVR) based on the least square loss function (LS). In order to improve the solution of LS-SVR model, we used the weighted LS-SVR and the fixed size LS-SVR models. Numerical illustrations highlight the contribution of the proposed models for VaR estimation taking into account the risk of specification and estimation
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Hoff, J. C. "Aircraft parameter estimation by estimation - before - modelling technique." Thesis, Cranfield University, 1995. http://dspace.lib.cranfield.ac.uk/handle/1826/10748.

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The use of the estímation-before-modellíng (EBM) two step identification procedure for the determination of aircraft aerodynamic derivatives from flight test data is analysed and illustrated. In the first step of the identification procedure the usual Extended Kalman Filter (EKF) associated with the Modified Bryson-Frazíer (MBF) smoother is compared with a new alterative filtering and smoothing process. The new smoother is simpler and less computationally demanding than the MBF smoother. However, its main advantage is that it enables simultaneous data smoothing with state derivative estimation, thereby avoiding the need for a separate differentiation algorithm. The new smoother differentiator has an important feature that is the determination of the noise characteristics of the measurement signal under analysis prior to the smoothing process. This is done by variance matching between the theoretical and measured autocorrelation of the innovation process generated by a Kalman filter. The new technique is compared with the old one by determining the aerodynamic models for a EMB-312 Tucano dutch roll manoeuvre. It is demonstrated that the new smoother may be used to replace the MBF. Otherwise the new technique is used in the analysis of the Handley Page Jetstream-100 aircraft low speed controls free phugoid trying to identify the contribution of the power Variation observed during the phugoid to the stability of the oscillation. Finally the models obtained from the phugoid analysis are reprocessed using the Total Least Square regression and the results are compared with those from the ordinary Least Square formulation.
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Reynard, D. M. "Nonlinear estimation." Thesis, University of Newcastle Upon Tyne, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336142.

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Mu, Yingfei. "Boundary Estimation." Diss., North Dakota State University, 2015. http://hdl.handle.net/10365/25195.

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The existing statistical methods do not provide a satisfactory solution to determining the spatial pattern in spatially referenced data, which is often required by research in many areas including geology, agriculture, forestry, marine science and epidemiology for identifying the source of the unusual environmental factors associated with a certain phenomenon. This work provides a novel algorithm which can be used to delineate the boundary of an area of hot spots accurately and e ciently. Our algorithm, rst of all, does not assume any pre-speci ed geometric shapes for the change-curve. Secondly, the computation complexity by our novel algorithm for changecurve detection is in the order of O(n2), which is much smaller than 2O(n2) required by the CUSP algorithm proposed in M uller&Song [8] and Carlstein's [2] estimators. Furthermore, our novel algorithm yields a consistent estimate of the change-curve as well as the underlying distribution mean of observations in the regions. We also study the hypothesis test of the existence of the change-curve in the presence of independence of the spatially referenced data. We then provide some simulation studies as well as a real case study to compare our algorithm with the popular boundary estimation method : Spatial scan statistic.
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Völcker, Björn. "Performance Analysis of Parametric Spectral Estimators." Doctoral thesis, KTH, Signals, Sensors and Systems, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3323.

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Baba, Harra M'hammed. "Estimation de densités spectrales d'ordre élevé." Rouen, 1996. http://www.theses.fr/1996ROUES023.

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Dans cette thèse nous construisons des estimateurs de la densité spectrale du cumulant, pour un processus strictement homogène et centré, l'espace des temps étant l'espace multidimensionnel, euclidien réel ou l'espace multidimensionnel des nombres p-adiques. Dans cette construction nous avons utilisé la méthode de lissage de la trajectoire et un déplacement dans le temps ou la méthode de fenêtres spectrales. Sous certaines conditions de régularité, les estimateurs proposés sont asymptotiquement sans biais et convergents. Les procédures d'estimation exposées peuvent trouver des applications dans de nombreux domaines scientifiques et peuvent aussi fournir des éléments de réponse aux questions relatives à certaines propriétés statistiques des processus aléatoires.
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Verma, Vishash. "Improved Slope Estimation in Organic Field-Effect Transistor Mobility Estimation." Kent State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1618703169092189.

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Chauvin, Jonathan. "Estimation et contrôle d’un moteur HCCI. Estimation des systèmes périodiques." Paris, ENMP, 2006. http://www.theses.fr/2006ENMP1387.

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La combustion homogène Diesel (HCCI : Homogeneous Charge Compression Ignition) est caractérisée par un très fort taux de recirculation de gaz brûlés (EGR : Exhaust Gas Recirculation). Cette technique de combustion permet d’augmenter la qualité de mélange et la dilution dans le cylindre, tout en réduisant la formation des polluants. Malheureusement, ce procédé nuit à la stabilité de la combustion. Un compromis est nécessaire entre la stabilité de combustion et les performances du moteur, quantifiées en terme de couple produit, de bruit et d’émissions polluantes. C’est là le rôle du moteur. À fins d’implémentation de stratégie de contrôle, il est nécessaire d’estimer en temps réel l’évolution des paramètres de combustion qui ne sont pas directement mesurés par des capteurs. Cette thèse, réalisée en collaboration avec l’IFP (Institut Français du Pétrole), propose des algorithmes de contrôle qui ont été validés expérimentalement sur un moteur HCCI quatre cylindres développés par l’IFP. Nous décomposons le problème en trois parties et proposons des solutions, validées sur banc moteur, pour les deux premières. La première étape consiste à réaliser le contrôle de la boucle d’air. Le but est d’estimer et de contrôler les masses aspirées par des cylindres (air frais et gaz brûlés). Ces masses s’expriment directement en fonction de la pression, la composition et les débits du collecteur d’admission. Des observateurs non linéaires permettent d’estimer ces variables, en n’utilisant que les capteurs présents sur les véhicules de séries. La construction de ces observateurs ainsi que leurs preuves de convergence utilisent la méthode dite « d’injection de sortie » ainsi que la théorie de stabilité de Lyapunov. Une technique de génération de trajectoires est utilisée pour définir des consignes de débits (air frais et EGR). Cette loi de commande boucle ouverte prend explicitement en copte les contraintes physiques. Enfin, des contrôleurs de type promotionnel intégral (PI) sont utilisés pour garantir le suivi des consignes prescrites. Nous décrivons les résultats expérimentaux obtenus dans différents cas de figures, tels que des transitoires de charge et le cycle de référence européen. La deuxième étape est l’équilibrage cylindre à cylindre. Le but est d’estimer les paramètres de combustion de chacun des cylindres afin de garantir que les cylindres ont la même combustion en dépit de la variabilité des éléments techniques les constituant. Pour cela, nous créons un observateur de couple instantané et un observateur de richesse cylindre à cylindre à partir de capteurs présents sur les véhicules de séries. Nous exploitons l’information de haute fréquence contenue dans les signaux mesurés (échantillonnage aux 6 degrés vilebrequin). Ces observateurs sont validés expérimentalement. Leur conception est nouvelle. Il s’agit d’un nouveau type d’observateurs asymptotiques reconstituant un nombre arbitraire de fréquences d’un signal périodique inconnu entrant dans un système linéaire périodique. Ces observateurs surpassent (à performances comparables) les filtres de Kalman en terme de temps de calculs. Ils sont inspirés des techniques de moyennisation. Une méthodologie de réglage automatique est proposée et justifiée par l’extension à un nombre infini de fréquences. La troisième étape est le contrôle de la boucle de fuel. Durant des transitoires de couples, la boucle de carburant doit suivre la dynamique plus lente de la boucle d’air (qui est typiquement 10 fois plus lente). Nous décrivons cette problématique et expliquons les principales difficultés
Homogeneous Charge Compression Ignition (HCCI) combustion is characterized by a very high rate of Exhaust Gas Recirculation (EGR). This improves mixing and dulution in the cylinders, reduces polluant formation at the expense of combustion stability. Thus HCCI engines requuires real-time control to ensure a good trade-off between performance (in terms of torque productio and low polluant emissions) and combustion stability. Such closed-loop control are based on estimation of combustion parameters that not directly measured. This thesis, supported by IFP (Institut Français du Péttrole), proposes some control algorithms that have been tested experimentaly on a 4 cylinders HCCI engine developed by IFP. We decompose the control synthesis in three steps. We propose solutions with experimental validations for the first two steps. The first steps is air path control. The goal is to estimate and to control the masses entering in the cylinders (fresh air and burned gas). These masses are directly related to collecctor pressure, compositions and flow-rates. These variables are estimated via nonlinear observers using commercial cars sensors. Design and theoretical convergence proof follow linearization via output injection and Lyapunov argument. Feedforward control based on motion planning for differentially flat systems are used to derive the flow-rate set points (fresh air and EGR). This feedfoward control takes explicitly physical input constraints into account. Finally, fast Proportional Integral (PI) controller are designed to track these step points unsing as measured values the aboves estimations. We describe experimental results for large torque transient and also driving phases of the eurocycle. The second step is cylinders balancing. The goal is to estimate and control the combustion parameters in order to guarantee that all the cylinders have the same combustion in any steady-state regime . For that, we designedinstantaneous torque and cylinder individual air/fuel ratio (AFR) observers using commercial car sensors. We exploit here the highfrequency information contained in the measured signals (sampling of 6 degree crank angle). Experimenal results are reported. These results are based on a new class on asymptotic observers of an arbitrary numbers of Fournier modes associated to an unknown periodic input entering a linear time-periodic system. These observers outperform Kalman filters in terms of computation burden. Design and convergence proof are based on averaging techniques. A gain design methodology is proposed and justified for large numbers of modes via extension to infinite dimension of the finite-dimensional convergence analysis. The third step is the fuel path control. During large transient, the fuel path must follow the slower air path transient. We describe this still open problematic and point out its main difficulties
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Srinivasarengan, Krishnan. "Estimation d'état, estimation paramétrique et identifiabilité des modèles quasi-LPV." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0059/document.

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Dans cette thèse, deux problèmes liés aux approches basées sur des modèles pour le diagnostic de défauts et l'estimation du niveau de dégradation des équipements dans un bâtiment sont étudiés: la conception d'observateurs adaptatifs pour l'estimation de l'état et des paramètres, et l'analyse de l'identifiabilité des paramètres. La classe des modèles considérés est celle des modèles quasi-linéaires à paramètres variants dans le temps (quasi-LPV) avec paramétrisation affine des matrices d'état. Utilisant l'approche polytopique de Takagi-Sugeno (T-S), deux types d'observateurs sont proposés, un pour des systèmes en temps continu et l'autre pour des systèmes en temps discret. La structure de Luenberger (correction de la dynamique à l'aide de l'erreur d'estimation de la sortie) est choisie pour la partie d'estimation d'état de l'observateur pour les deux et leur conception s'appuie sur l'approche de Lyapunov. Pour la partie d'estimation des paramètres, une structure originale est proposée en temps continu et une structure proportionnelle-intégrale (PI) est utilisée en temps discret. La troisième contribution présente succinctement une méthode d'estimation d'état et des paramètres de façon découplée. Elle utilise conjointement l'approche de l'espace de parité et un observateur à mémoire finie. Pour la quatrième contribution relative à l'identifiabilité des paramètres, les états du système sont tout d'abord éliminés en utilisant une approche de type espace de parité. Cela permet d'extraire le `résumé exhaustif' du modèle qui aide à établir l'identifiabilité du modèle. Tous les résultats sont illustrés à l'aide d'exemples
Two problems relevant to the model-based approaches to fault diagnosis and degradation estimation in commissioned buildings are investigated in this thesis: adaptive observers for state and parameter estimation, and parameter identifiability. The system models considered are the quasi-LPV models with affine parameterization. Using the Takagi-Sugeno (T-S) polytopic approach, two observer designs, one for continuous-time models and another for discrete-time models are provided. Both models use a Luenberger structure for the state estimation part and deploy the Lyapunov design approach. An innovative non-linear estimation model is obtained through the design process for the continuous-time parameter estimation whereas a proportional-integral (PI) structure is used for discrete-time. A brief third contribution is a decoupled state and parameter estimation that makes use of the parity-space approach and realized using a finite memory observer strategy. For the fourth contribution of parameter identifiability, a parity-space formulation using null-space computation is used for the elimination of states of the model from which the exhaustive summary of the model is extracted and the identifiability of the model verified. All the results are illustrated using examples
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Books on the topic "Estimation"

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Dowdy, Penny. Estimation. New York: Crabtree Pub., 2008.

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1949-, Gervais Paul, ed. Estimation. Laval, Québec: Beauchemin, 1997.

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Heijden, F. van der, R. P. W. Duin, D. de Ridder, and D. M. J. Tax. Classification, Parameter Estimation and State Estimation. Chichester, UK: John Wiley & Sons, Ltd, 2004. http://dx.doi.org/10.1002/0470090154.

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de Ridder, Dick, David M. J. Tax, Bangjun Lei, Guangzhu Xu, Ming Feng, Yaobin Zou, and Ferdinand van der Heijden. Classification, Parameter Estimation and State Estimation. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119152484.

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Ghosh, Malay, Nitis Mukhopadhyay, and Pranab K. Sen. Sequential Estimation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1997. http://dx.doi.org/10.1002/9781118165928.

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Fourdrinier, Dominique, William E. Strawderman, and Martin T. Wells. Shrinkage Estimation. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02185-6.

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Mislick, Gregory K., and Daniel A. Nussbaum. Cost Estimation. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118802342.

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Ross, Gavin J. S. Nonlinear Estimation. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3412-8.

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Ross, Gavin J. S. Nonlinear estimation. New York: Springer-Verlag, 1990.

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Ghosh, Malay. Sequential estimation. New York: Wiley, 1997.

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Book chapters on the topic "Estimation"

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Seong, Junyeong, Sungjun Park, and Kunsoo Huh. "Robust Lane Keeping Control with Estimation of Cornering Stiffness and Model Uncertainty." In Lecture Notes in Mechanical Engineering, 272–78. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_39.

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AbstractThis paper introduces an adaptive lane-keeping control strategy that adapts to varying cornering stiffness while ensuring robustness against uncertainties. The system consists of three blocks: an Interacting Multiple Model (IMM) cornering stiffness estimator, a cornering stiffness uncertainty estimator, and a Robust Model Predictive Controller (RMPC). Improvements in estimation accuracy are achieved through a novel IMM probability derivation method, and the uncertainty estimator utilizes the IMM probability matrix to obtain reliable uncertainty boundaries. Real-time cornering stiffness estimations are integrated into the RMPC for adaptive model predictions. Uncertainty boundaries provide robustness against estimation error in the RMPC by constraint tightening and smoothing techniques. The performance of the estimator and controller is validated in simulations, where the overall control performance is compared to that of the Model Predictive Control (MPC) based on static cornering stiffness.
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Boos, Denni D., and L. A. Stefanski. "M-Estimation (Estimating Equations)." In Springer Texts in Statistics, 297–337. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4818-1_7.

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Fadali, M. Sami. "Estimation and Estimator Properties." In Introduction to Random Signals, Estimation Theory, and Kalman Filtering, 147–76. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8063-5_5.

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Tobisch, Franziska, Karla Weigelt, Pascal Philipp, and Florian Matthes. "Investigating Effort Estimation in a Large-Scale Agile ERP Transformation Program." In Lecture Notes in Business Information Processing, 70–86. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61154-4_5.

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AbstractAdaptability is vital in today’s rapidly changing business environment, especially within IT. Agile methodologies have emerged to meet this demand and have thereby gained widespread adoption. While successful in smaller, co-located teams and low-criticality projects, applying agile methods in broader contexts poses challenges. Nevertheless, many organizations have started implementing agile methodologies in various areas, including large-scale Enterprise Resource Planning (ERP) projects. In contrast to traditional development, ERP projects involve deploying extensive integrated systems, are substantial in scale, and entail high risks and costs. Accurate predictions, like effort estimations, are crucial to meet customer satisfaction and deliver within plan and budget. However, estimating effort in an agile environment poses its own set of challenges. For instance, coordination efforts and dependencies among teams must be considered. While effort estimation is well-explored in classical software development and small-scale agile contexts, limited research exists in large-scale agile settings, particularly in projects rolling out and customizing standard ERP solutions. To address this gap, we conducted a case study on effort estimation in a large agile ERP transformation program, describing the estimation process, highlighting challenges, and proposing and evaluating mitigations.
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Lin, Stephen. "Illumination Estimation, Illuminant Estimation." In Computer Vision, 371–73. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_516.

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Lin, Stephen. "Illumination Estimation, Illuminant Estimation." In Computer Vision, 599–604. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_516.

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Lin, Stephen. "Illumination Estimation, Illuminant Estimation." In Computer Vision, 1–6. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_516-1.

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Ross, Gavin J. S. "Models, Parameters, and Estimation." In Nonlinear Estimation, 1–11. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3412-8_1.

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Ross, Gavin J. S. "Transformations of Parameters." In Nonlinear Estimation, 12–43. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3412-8_2.

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Ross, Gavin J. S. "Inference and Stable Transformations." In Nonlinear Estimation, 44–72. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3412-8_3.

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Conference papers on the topic "Estimation"

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Mballo, Chams, and J. V. R. Prasad. "A Real Time Scheme for Rotating System Component Load Estimation Using Fixed System Measurements." In Vertical Flight Society 74th Annual Forum & Technology Display, 1–12. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12768.

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Accurate and real-time load monitoring of vital components located in the rotor system is important for not only inferring usage and estimating fatigue in those components, but also for developing load alleviation /limiting control schemes. An approach previously developed for online estimation of rotor component loads is evaluated in this paper in which a linear time invariant (LTI) model of helicopter coupled body/rotor dynamics is combined with a Linear Quadratic Estimator (LQE), that is designed to correct LTI model state response using fixed system measurements. The estimation fidelity of the LTI/LQE scheme is evaluated in simulations using a nonlinear model of a generic helicopter for online prediction of rotor blade pitch link loads arising from vehicle maneuvers.
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Li, Yonghua, and R. Dyche Anderson. "Switching Adaptive Observer for Lithium-Ion Battery State of Charge Estimation." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6061.

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A switching adaptive observer is proposed for estimation of state of charge (SOC) for lithium ion batteries used in electrified automotive propulsion systems. The base observer includes (i) a parameter estimation subsystem including a recursive parameter estimator for identifying battery parameters and (ii) an open circuit voltage (OCV) estimation subsystem including a nonlinear adaptive observer for estimating battery OCV. A timer as well as excitation level determination decides when the ampere-hour integration based SOC or estimated OCV based SOC is used as output. Using this approach, transient response of the adaptive SOC estimator is greatly improved. Examples are used to show the effectiveness of the proposed approach.
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Gallego-Mejia, Joseph, and Fabio Gonzalez. "Robust Estimation in Reproducing Kernel Hilbert Space." In LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019120829.

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Our work shows that estimating the mean in a feature space induced by certain kinds of kernels is the same as doing a robust mean estimation using an M-estimator in the original problem space. In particular, we show that calculating the average on a feature space induced by a Gaussian kernel is equivalent to perform robust mean estimation with the Welsch M-estimator. Besides, a new framework is proposed that was used to build four new robust kernels: Tukey’s, Andrews’, Huber’s and Cauchy’s robust kernels. The new robust kernels, combined with kernel matrix factorization clustering algorithm, were compared to state-of-the-art algorithms in clustering tasks. The result shows that some of the new robust kernels perform in a par with state-of-the-art algorithms.
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Huang, Xiaoyu, and Junmin Wang. "Payload Parameter Real-Time Estimation for Lightweight Vehicles." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6045.

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This paper proposes a payload parameter estimation method for lightweight vehicles (LWVs), whose dynamics and control are substantially affected by their payload variations due to the LWVs’ significantly reduced sizes and weights. Accurate and real-time estimation of payload parameters, including payload mass and its onboard planar location, will be helpful for controller designs and load condition monitoring. The proposed payload parameter estimator (PPE) is divided into two parts: tire nominal normal force estimator (NNFE) based on a recursive least squares (RLS) algorithm using signals measured from LWV constant speed maneuvers, and parameter calculator based on estimated nominal normal forces. The prototype LWV is an electric ground vehicle with separable torque control of the four wheels by in-wheel motors, which allow redundant input injections in the designed maneuvers. Simulation results, based on a CarSim® model, show that the proposed PPE is capable of accurately and quickly estimating payload parameters, and is independent of the road condition as long as the tire forces are kept within their linear ranges.
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Zhang, Pushi, Li Zhao, Guoqing Liu, Jiang Bian, Minlie Huang, Tao Qin, and Tie-Yan Liu. "Independence-aware Advantage Estimation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/461.

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Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon. To address this challenge, we propose to identify the independence property between current action and future states in environments, which can be further leveraged to effectively reduce the variance of the advantage estimation. In particular, the recognized independence property can be naturally utilized to construct a novel importance sampling advantage estimator with close-to-zero variance even when the Monte-Carlo return signal yields a large variance. To further remove the risk of the high variance introduced by the new estimator, we combine it with existing Monte-Carlo estimator via a reward decomposition model learned by minimizing the estimation variance. Experiments demonstrate that our method achieves higher sample efficiency compared with existing advantage estimation methods in complex environments.
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Nguyen, Thang, Holly Warner, Hanieh Mohammadi, Dan Simon, and Hanz Richter. "On the State Estimation of an Agonistic-Antagonistic Muscle System." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5304.

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In this paper, an agonistic-antagonistic muscle system is presented. This dual muscle system is based on the Hill muscle model. The problem of estimating the state variables and activation signals of the dual muscle system is addressed. A proposed estimation scheme which combines a super-twisting observer and an input estimator is given to provide a solution to the problem. A backstepping control method is used to track a reference trajectory. Numerical results are conducted to show that the relative error for state estimation is about 1% and that for the unknown inputs is about 3% when the measurements of the length of a muscle and its nonlinear spring force are affected by noise profiles whose normalized amplitude is 0.005.
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Achicanoy M., Wilson O., and Carlos F. Rodriguez H. "Integration of GPS and Accelerometer Uncertainties to Improve the Estimation of the Pose of Autonomous Vehicles." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40463.

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Uncertainty fusion techniques based on Kalman filtering are commonly used to provide a better estimation of the state of a system. A comparison between three different methods to combine the sensor information in order to improve the estimation of the pose of an autonomous vehicle is presented. Two sensors and their uncertainty models are used to measure the observables states of a process: a Global Positioning System (GPS) and an accelerometer. Given that GPS has low sampling rate and the uncertainty of the position, calculated by double integration from the accelerometer signal, increases with time, first a resetting of the estimator based on accelerometer by the GPS measurement is done. Next, a second method makes the fusion of both sensor uncertainties to calculate the estimation. Finally, a double estimation is done, one for each sensor, and a estimated state is calculated joining the individual estimations. These methods are explained by a case study of a guided bomb.
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Liu, Chenchen, Yongzhi Li, Kangqi Ma, Duo Zhang, Peijun Bao, and Yadong Mu. "Learning 3-D Human Pose Estimation from Catadioptric Videos." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/118.

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3-D human pose estimation is a crucial step for understanding human actions. However, reliably capturing precise 3-D position of human joints is non-trivial and tedious. Current models often suffer from the scarcity of high-quality 3-D annotated training data. In this work, we explore a novel way of obtaining gigantic 3-D human pose data without manual annotations. In catedioptric videos (\emph{e.g.}, people dance before a mirror), the camera records both the original and mirrored human poses, which provides cues for estimating 3-D positions of human joints. Following this idea, we crawl a large-scale Dance-before-Mirror (DBM) video dataset, which is about 24 times larger than existing Human3.6M benchmark. Our technical insight is that, by jointly harnessing the epipolar geometry and human skeleton priors, 3-D joint estimation can boil down to an optimization problem over two sets of 2-D estimations. To our best knowledge, this represents the first work that collects high-quality 3-D human data via catadioptric systems. We have conducted comprehensive experiments on cross-scenario pose estimation and visualization analysis. The results strongly demonstrate the usefulness of our proposed DBM human poses.
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Zhang, Jianwu, and Defeng Xu. "Hierarchical Estimator of Dual Clutch Torques for a Power-Split Hybrid Electric Vehicle." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-8927.

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Abstract For fast drive mode transitions by shifting clutches equipped in the dedicated compound power-split hybrid transmission, correct estimations of pressure and torque of the clutches are crucial for control strategies. A hierarchical estimator is proposed herein for individual estimation of the clutch torques, consisting of not only the reference layer containing the unknown input observer of vehicle resistance and the reduced-order observer of drive shaft torque, but also the estimation layer combining the unknown input observer with the reduced-order observer. The estimator is implemented to strike a balance between estimation accuracy in the steady state and real time response in the transient state. For validation of the estimator, simulations and real car tests are carried out in specific drive conditions. By numerical results, it’s demonstrated that excellent predictive abilities are found including reasonably small estimation error and adaptive capability and, as a result, shift to shift induced driveline oscillations and vehicle jerks are reduced significantly.
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Devezeaux, Jean-Guy. "L’économie globale, le coût du KWh et la sécurisation des fonds." In Méthodologie : estimation du coût du démantèlement. Les Ulis, France: EDP Sciences, 2015. http://dx.doi.org/10.1051/jtsfen/2015met01.

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Reports on the topic "Estimation"

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Banks, H. T., and Kathleen L. Bihari. Modeling and Estimating Uncertainty in Parameter Estimation. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada447550.

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Bacharach, Miguel, and William J. Vaughan. Household Water Demand Estimation. Inter-American Development Bank, March 1994. http://dx.doi.org/10.18235/0011616.

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This working paper addresses the issues: 1) whether there is a simultaneous equations problem when estimating demand for water with multipart rate schedules and, if there is one, what techniques should be used to correct for it; and 2) whether average or marginal price is the relevant measure in estimating the demand function. In addition, the issue is raised of sample selection bias in a rate schedule, combined with a fixed charge for consumption below the level where a block rate tariff per unit consumed. This study uses results from a sample of 685 families from 34 localities in rural Argentina (taken in 1987) and presents an analysis of the results with different estimation techniques.
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Candy, J. V. MULTICHANNEL SPECTRAL ESTIMATION: An Approach to Estimating/Analyzing Vibrational Systems. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1592017.

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Li, Ta-Hsin, Benjamin Kedem, and Sid Yakowitz. Asymptotic Normality of the Contraction Mapping Estimator for Frequency Estimation. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada453892.

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Owen, Arthur B. Nonparametric Conditional Estimation. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1454025.

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Bryan, Michael, Stephen Cecchetti, and Rodney L. Wiggins II. Efficient Inflation Estimation. Cambridge, MA: National Bureau of Economic Research, September 1997. http://dx.doi.org/10.3386/w6183.

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Owen, Arthur B. Nonparametric Conditional Estimation. Fort Belvoir, VA: Defense Technical Information Center, February 1987. http://dx.doi.org/10.21236/ada590998.

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Uhlig, Harald, Toru Kitagawa, and Raffaella Giacomini. Estimation Under Ambiguity. The IFS, May 2019. http://dx.doi.org/10.1920/wp.cem.2019.2419.

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Tibshirani, R. Local Likelihood Estimation. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1453998.

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Hall, Peter, and R. J. Carroll. Variance Function Estimation in Regression: The Effect of Estimating the Mean. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada198228.

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