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Relevant bibliographies by topics / State and parameter estimations

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Academic literature on the topic 'State and parameter estimations'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "State and parameter estimations"

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Andersson, Niklas, Per-Ola Larsson, Johan Åkesson, Niclas Carlsson, Staffan Skålén, and Bernt Nilsson. "Parameter Selection in the Parameter Estimation of Grade Transitions in a Polyethylene Plant." Studies in Engineering and Technology 3, no. 1 (2015): 1. http://dx.doi.org/10.11114/set.v3i1.887.

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A polyethylene plant at Borealis AB is modelled in the Modelica language and considered for parameter estimations at grade transitions. Parameters have been estimated for both the steady-state and the dynamic case using the JModelica.org platform, which offers tools for steady-state parameter estimation and supports simulation with parameter sensitivies. The model contains 31 candidate parameters, giving a huge amount of possible parameter combinations. The best parameter sets have been chosen using a parameter-selection algorithm that identified parameter sets with poor numerical properties.

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Jamal, Alaa, and Raphael Linker. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model." Agriculture 10, no. 12 (2020): 606. http://dx.doi.org/10.3390/agriculture10120606.

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Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for

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Wang, Xiaoyu, Te Chen, Renzhong Wang, Jiankang Lu, and Guowei Dou. "Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms." Sensors 25, no. 13 (2025): 3927. https://doi.org/10.3390/s25133927.

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This paper reviews research on vehicle driving state estimation research. Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the perspective of the estimation objects, including vehicle attitude and driving state estimations, chassis component key dynamic parameter estimations, and vehicle driving environment state estimations; (2) from the perspective of vehicle characteristics, including vehicle dynamics coupling characteristi

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Jung, Youngsun, Ming Xue, and Guifu Zhang. "Simultaneous Estimation of Microphysical Parameters and the Atmospheric State Using Simulated Polarimetric Radar Data and an Ensemble Kalman Filter in the Presence of an Observation Operator Error." Monthly Weather Review 138, no. 2 (2010): 539–62. http://dx.doi.org/10.1175/2009mwr2748.1.

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Abstract The impacts of polarimetric radar data on the estimation of uncertain microphysical parameters are investigated through observing system simulation experiments when the effects of uncertain parameters on the observation operators are also considered. Five fundamental microphysical parameters (i.e., the intercept parameters of rain, snow, and hail and the bulk densities of snow and hail) are estimated individually or collectively using the ensemble square root Kalman filter. The differential reflectivity ZDR, specific differential phase KDP, and radar reflectivity at horizontal polariz

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Wang, Huapeng, Nan Jiang, Ting Liu, and Yangyang Cao. "ADAPTIVE STABLE CONTROL OF MANIPULATOR SYSTEM BASED ON IMMERSION AND INVARIANCE." Mathematical Modelling and Analysis 23, no. 3 (2018): 379–89. http://dx.doi.org/10.3846/mma.2018.023.

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This work focused on the manipulator system containing uncertainties, and proposes an immersion and invariance (I&amp;I) control strategy, in order to avoid the damage on the mechanical and the operation object caused by parameter uncertainty. A stable target system with lower dimension than the manipulator system was chosen to design the control law and estimation laws of uncertain parameters. Then finding an invariant and attractive manifold in state space with internal dynamics a copy of the desired closed-loop dynamics. Finally, design a control law that can steer the state of the syst

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Astharini, Dwi, Muhamad Asvial, and Dadang Gunawan. "Estimation with Angular Parameters on Channel of Visible Light Communication." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 2 (2022): 142–51. http://dx.doi.org/10.37936/ecticit.2022162.245930.

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Performance of visible light communication (VLC) depends highly on channel conditions, which are sensitive to changes in user position. This paper presents estimation schemes of VLC parameters using a Kalman filter (KF), based on angular parameters of user position. The angular dynamic model is established so that the estimation process is directly in accordance with the Lambertian model of VLC channel. The use of angular model also gave way to use two parameters to describe a three-dimensional position. Estimations based on angular position are formulated, that is the KF estimation of positio

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Obiri, Sandra A., Bernard T. Agyeman, Sarupa Debnath, Siyu Liu, and Jinfeng Liu. "Sensor Selection and State Estimation of Continuous mAb Production Processes." Mathematics 11, no. 18 (2023): 3860. http://dx.doi.org/10.3390/math11183860.

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The production of monoclonal antibodies (mAbs) plays a pivotal role in therapeutic treatments, and optimizing their production is crucial for minimizing costs and improving their accessibility to patients. One way of improving the production process is to improve model accuracy through the correct estimation of its states and parameters. The contributions of this paper lie in the provision of guidelines for sensor selection in the upstream production process of mAbs to enhance the accuracy of state estimation. Furthermore, this paper applies an effective variable selection technique for simult

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Li, Zhengbin, Lijun Ma, and Yongqiang Wang. "Parameter estimation for nonlinear sandwich system using instantaneous performance principle." PLOS ONE 17, no. 12 (2022): e0271160. http://dx.doi.org/10.1371/journal.pone.0271160.

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The vast majority of reports mainly focus on the steady-state performance of parameter estimation. Few findings are reported for the instantaneous performance of parameter estimation because the instantaneous performance is difficult to quantify by using the design algorithm, for example, in the initial stage of parameter estimation, the error of parameter estimation varies in a specific region on the basis of the user’s request. With that in mind, we design an identification algorithm to address the transient performance of the parameter estimations. In this study, the parameter estimation of

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Nie, S., J. Zhu, and Y. Luo. "Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: idealized twin experiments." Hydrology and Earth System Sciences Discussions 8, no. 1 (2011): 1433–68. http://dx.doi.org/10.5194/hessd-8-1433-2011.

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Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, initial soil moisture condition and atmospheric forcing. A physically-based land surface model is used for this purpose. Using a series of idealized twin experiments, model generated near-surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil mois

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Hsieh, Hsien-Yi, Jingyu Ning, Yi-Ru Chen, et al. "Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography." Symmetry 14, no. 5 (2022): 874. http://dx.doi.org/10.3390/sym14050874.

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With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high pre

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Dissertations / Theses on the topic "State and parameter estimations"

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Kubitzki, Marcus. "State and Parameter Estimation in Quantum Theory." [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10806359.

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Evestedt, Magnus. "Parameter and State Estimation with Information-rich Signals." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8315.

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Wenzel, Thomas A. "State and parameter estimation for vehicle dynamic control." Thesis, Coventry University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422507.

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Carr, Matthew J. "State and parameter estimation techniques for stochastic systems." Thesis, University of Salford, 2006. http://usir.salford.ac.uk/26606/.

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This thesis documents research undertaken on state and parameter estimation techniques for stochastic systems in a maintenance context. Two individual problem scenarios are considered. For the first scenario, we are concerned with complex systems and the research involves an investigation into the ability to identify and quantify the occurrence of fault injection during routine preventive maintenance procedures. This is achieved using an appropriate delay time modelling specification and maximum-likelihood parameter estimation techniques. The delay time model of the failure process is paramete

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Hart, S. D. "Parameter optimisation and state estimation for machine control." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/843955/.

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This thesis is concerned with the modelling of electrical machines for use in variable-speed drives. Even when the structure of the model of the machine is known values have to be assigned to the parameters. In addition, it is usual for only some of the state variables to be measured, any others needed being estimated using the model. The present work is a study of methods of making on-line estimates of the model parameters, using a reduced number of measured states. To offer high level dynamic torque control the non-measured state variables must be indirectly estimated to a high degree of acc

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Creveling, Daniel R. "Parameter and state estimation in nonlinear dynamical systems." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3336421.

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Thesis (Ph. D.)--University of California, San Diego, 2008.<br>Title from first page of PDF file (viewed December 16, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 122-124).

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Schwarz, Lucia. "Error Models for Quantum State and Parameter Estimation." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18526.

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Within the field of Quantum Information Processing, we study two subjects: For quantum state tomography, one common assumption is that the experimentalist possesses a stationary source of identical states. We challenge this assumption and propose a method to detect and characterize the drift of nonstationary quantum sources. We distinguish diffusive and systematic drifts and examine how quickly one can determine that a source is drifting. Finally, we give an implementation of this proposed measurement for single photons. For quantum computing, fault-tolerant protocols assume that errors are

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Sawo, Felix. "Nonlinear state and parameter estimation of spatially distributed systems." Karlsruhe Univ.-Verl. Karlsruhe, 2009. http://d-nb.info/994500211/04.

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Evestedt, Magnus. "Parameter and state estimation using audio and video signals." Licentiate thesis, Uppsala : Univ. : Dept. of Information Technology, Univ, 2005. http://www.it.uu.se/research/publications/lic/2005-009/2005-009.pdf.

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Yu, Jia. "Distributed parameter and state estimation for wireless sensor networks." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28929.

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The research in distributed algorithms is linked with the developments of statistical inference in wireless sensor networks (WSNs) applications. Typically, distributed approaches process the collected signals from networked sensor nodes. That is to say, the sensors receive local observations and transmit information between each other. Each sensor is capable of combining the collected information with its own observations to improve performance. In this thesis, we propose novel distributed methods for the inference applications using wireless sensor networks. In particular, the efficient algor

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Books on the topic "State and parameter estimations"

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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. 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, et al. Classification, Parameter Estimation and State Estimation. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119152484.

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Chernousʹko, F. L. State estimation for dynamic systems. CRC Press, 1994.

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Murphy, Katherine A. Estimation of time- and state-dependent delays and other parameters in functional differential equations. ICASE, 1988.

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der, Heijden Ferdinand van, ed. Classification, parameter estimation, and state estimation: An engineering approach using MATLAB. Wiley, 2004.

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Cecilia, Andreu. Advances in Nonlinear Observer Design for State and Parameter Estimation in Energy Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38924-5.

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Jaulin, Luc. Applied Interval Analysis: With Examples in Parameter and State Estimation, Robust Control and Robotics. Springer London, 2001.

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Luc, Jaulin, ed. Applied interval analysis: With examples in parameter and state estimation, robust control and robotics. Springer, 2001.

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Du, Tan. Joint state-and-parameter estimation of induction-motor drives with application to adaptive field-oriented control. University of Birmingham, 1994.

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M, Rasmuson Dale, Mosleh A, U.S. Nuclear Regulatory Commission. Office for Analysis and Evaluation of Operational Data. Division of Safety Programs., Idaho National Engineering and Environmental Laboratory., Lockheed Idaho Technologies Company, and U.S. Nuclear Regulatory Commission., eds. Common-cause failure parameter estimations. Safety Programs Division, Office for Analysis and Evaluation of Operational Data, U.S. Nuclear Regulatory Commission, 1998.

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Book chapters on the topic "State and parameter estimations"

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Young, Peter C. "State-Dependent Parameter (SDP) Estimation." In Recursive Estimation and Time-Series Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21981-8_11.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "H-Infinity-Based Observer." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-11.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "Interactive Multiple Modelling for Target Tracking with New Algorithms." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-15.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "Least-Squares and Maximum-Likelihood Methods." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-2.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "Image Centroid Tracking with Square-Root Kalman Filters." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-7.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "Neural Networks for Parameter Estimation with Lyapunov Stability." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-14.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "Gaussian-Sum Information Filter with Lyapunov Stability Analysis." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-6.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "Introduction." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-1.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "Machine Learning for Estimation." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-16.

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Raol, Jitendra R., P. Parimala, V. Reshma, and Sara M. George. "Hybrid Global H-Infinity Fusion Filter." In Advances in State and Parameter Estimation. CRC Press, 2025. https://doi.org/10.1201/9781003620211-13.

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Conference papers on the topic "State and parameter estimations"

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Hsieh, Hsien-Yi, Yi-Ru Chen, Jingyu Ning, et al. "Machine Learning-Enhanced Quantum State Tomography with Direct Parameter Estimations." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jw2a.160.

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Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly.

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Hsieh, Hsien-Yi, Yi-Ru Chen, Jingyu Ning, et al. "Machine Learning-Enhanced Quantum State Tomography with Direct Parameter Estimations." In 2024 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR). IEEE, 2024. http://dx.doi.org/10.1109/cleo-pr60912.2024.10676693.

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Tang, Fangzhou, Zhaohui Wang, and Yayun Cheng. "Simultaneous Parameter and State Estimation with Extended Kalman Filter for Dynamic Parameters." In 2024 IEEE MTT-S International Wireless Symposium (IWS). IEEE, 2024. http://dx.doi.org/10.1109/iws61525.2024.10713488.

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Lee, Yonggu, Gyul Lee, Austin White, and Yong-June Shin. "Oscillation Parameter Estimation via State-Space Modeling of Synchrophasors." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688881.

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Sadnan, Rabayet, Rounak Meyur, Daniel Glover, Andrew Reiman, and Jim Follum. "Parameter Estimation of Unreported PVs using Distribution System State Estimation Algorithm." In 2025 IEEE Green Technologies Conference (GreenTech). IEEE, 2025. https://doi.org/10.1109/greentech62170.2025.10977689.

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AlMutawa, Jaafar. "Parameter estimation of multisensor state space models with outlier contamination." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708366.

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Romero, Irabiel, Roummel Marcia, Ignacio Aravena, and Noemi Petra. "Optimal PMU Placement for State Estimation with Grid Parameter Uncertainty." In 2025 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge). IEEE, 2025. https://doi.org/10.1109/gridedge61154.2025.10887511.

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Castillo, Brian, and Masood Shahverdi. "Battery Parameter Extraction and Real-Time State of Charge Estimation." In 2025 7th Global Power, Energy and Communication Conference (GPECOM). IEEE, 2025. https://doi.org/10.1109/gpecom65896.2025.11061954.

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COT, LÉA, SÉBASTIEN DEJEAN, and CAROLE SAUDEJAUD. "A STATE-PARAMETER ESTIMATION IN TWO ENGINEERING DOMAINS: AN EXTENDED KALMAN FILTER APPROACH." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36286.

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This paper intends to present a synthesis of works based on the study of the behavior of the Kalman filters in two different domains. The first area is dedicated to the flocculation process occurring in water treatment. The second one covers aircraft structural damage based on SHM approach. The general methodology consists in modeling a state-parameter observer to perform estimations using the joint EKF. The robustness and efficiency of the Kalman filters is addressed in order to allow a cross-fertilization in the two area. Model propagation and accurate prognostics are therefore allowed due t

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Nielsen, Ulrik D. "Response-Based Estimation of Sea State Parameters." In ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29672.

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Reliable estimation of the on-site sea state parameters is essential to decision support systems for safe navigation of ships. The sea state parameters can be estimated by Bayesian Modelling which uses complex-valued frequency response functions (FRF) to estimate the wave spectrum on the basis of measured ship responses. It is therefore interesting to investigate how the filtering aspect, introduced by FRF, affects the final outcome of the estimation procedures. The paper contains a study based on numerical generated time series, and the study shows that filtering has an influence on the estim

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Reports on the topic "State and parameter estimations"

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Tuffner, Francis K., Tess L. Williams, Kevin P. Schneider, et al. Improving Distribution Resiliency with Microgrids and State and Parameter Estimation. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1334056.

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Meliopoulos, Sakis, George Cokkinides, Bruce Fardanesh, and Clinton Hedrington. Distributed Dynamic State Estimator, Generator Parameter Estimation and Stability Monitoring Demonstration. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1176943.

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Hacker, Joshua P., Cari G. Kaufman, and James Hansen. State-Space Analysis of Model Error: A Probabilistic Parameter Estimation Framework with Spatial Analysis of Variance. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada574466.

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Tunc Aldemir, Don W. Miller, Brian k. Hajek, and Peng Wang. Development of a Probabilistic Technique for On-line Parameter and State Estimation in Non-linear Dynamic Systems. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/793324.

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Ma, Zhegang, and Kellie Kvarfordt. CCF Parameter Estimations 2020 Update. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1846976.

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Ma, Zhegang, and Kellie Kvarfordt. Causal CCF Parameter Estimations 2020. Office of Scientific and Technical Information (OSTI), 2023. https://doi.org/10.2172/2561244.

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Bajwa, Abdullah, and Timothy Jacobs. PR-457-17201-R02 Residual Gas Fraction Estimation Based on Measured Engine Parameters. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011558.

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Gas exchange processes in two-stroke internal combustion engines, commonly referred to as scavenging, are responsible for removing the exhaust gases in the combustion chamber and preparing the combustible fuel-oxidizer mixture that undergoes combustion and converts the chemical energy of the fuel into mechanical work. Scavenging is a complicated phenomenon because of the simultaneous introduction of fresh gases into the engine cylinder through the intake ports, and the expulsion of combustion products from the previous cycles through the exhaust ports. A non-negligible fraction of the gaseous

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Liu and Nixon. L52305 Probabilistic Analysis of Pipeline Uplift Resistance. Pipeline Research Council International, Inc. (PRCI), 2010. http://dx.doi.org/10.55274/r0000002.

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To investigate the feasibility of probabilistic analyses of the peak uplift resistance in frozen soils by varying parameters that are known to be important for the development of the uplift resistance under the upward movement of a pipe. A buried pipeline will be subjected to a variety of forces, both internal and external, including the interaction of the pipe with the surrounding soil. The soil-pipe interaction in permafrost regions have to account for the behavior of frozen and unfrozen soil, and transitions between the two as the pipeline traverses in a discontinuous permafrost zone. The v

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Han, Zhao, and Chengcheng Jia. How Important Is the Information Effect of Monetary Policy? Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202332.

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Is the "information effect" of monetary policy quantitatively important? We first use a simple model to show that under asymmetric information, monetary policy surprises are correlated with the unobserved state of the economy. This correlation implies that monetary policy surprises provide information about the state of the economy, and at the same time, explains why the estimation of the information effect may be biased. We then develop a New Keynesian DSGE model under asymmetric information and calibrate model parameters to match macroeconomic dynamics in the US and forecasting accuracy in t

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Bajwa, Abdullah, Tim Kroeger, and Timothy Jacobs. PR-457-17201-R04 Residual Gas Fraction Estimation Based on Measured Engine Parameters - Phase IV. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012176.

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Based on experimental characterization of the scavenging behavior of a cross-scavenged, piston-aspirated, two-stroke, natural gas engine in phase III of the current project, a computationally inexpensive simple scavenging model was improved in this phase. Experimental results using fast nondispersive infrared (NDIR) CO2 measurements from the cylinder and the exhaust, as well as experiments using unburned fuel pre-mixed in the scavenging chamber as a tracer for short-circuiting during scavenging, were used in this phase to validate the improved model. The model represents the fundamental phenom

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