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Journal articles on the topic 'States estimation'

Author: Grafiati

Published: 15 June 2024

Last updated: 31 July 2025

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1

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 (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 t

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Wasim, Muhammad, Ahsan Ali, Muhammad Mateen Afzal Awan, and Inam ul Hasan Shaikh. "Estimation of airship states and model uncertainties using nonlinear estimators." Mehran University Research Journal of Engineering and Technology 43, no. 1 (2024): 55. http://dx.doi.org/10.22581/muet1982.2401.1613.

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This Airships are lighter than air vehicles and due to their growing number of applications, they are becoming attractive for the research community. Most of the applications require an airship autonomous flight controller which needs an accurate model and state information. Usually, airship states are affected by noise and states information can be lost in the case of sensor's faults, while airship model is affected by model inaccuracies and model uncertainties. This paper presents the application of nonlinear and Bayesian estimators for estimating the states and model uncertainties of neutra

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Talla Ouambo, Steve Alan, Alexandre Teplaira Boum, and Adolphe Moukengue Imano. "States and Parameters Estimation for Induction Motors Based on a New Adaptive Moving Horizon Estimation." Journal of Electrical and Computer Engineering 2022 (November 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/8687025.

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This paper investigates the joint states and parameters estimation problem for induction machine. In order to develop new states and parameters estimation methods that greatly improve the estimation bandwidth, this paper proposes an adaptive moving horizon estimation of the crucial states and parameters of the induction machine. The model of the machine under study is the one taking into consideration the magnetic saturation and the iron losses simultaneously. The estimator used is based on a least squares algorithm but includes a dead zone that ensures robustness and a variable forgetting fac

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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 (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 (

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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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Zhao, Dengfeng, Haiyang Li, Fang Zhou, et al. "Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses." World Electric Vehicle Journal 14, no. 6 (2023): 145. http://dx.doi.org/10.3390/wevj14060145.

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Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models,

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Gao, Chao, Guorong Zhao, Jianhua Lu, and Shuang Pan. "Decentralized state estimation for networked spatial-navigation systems with mixed time-delays and quantized complementary measurements: The moving horizon case." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 11 (2017): 2160–77. http://dx.doi.org/10.1177/0954410017712277.

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In this paper, the navigational state estimation problem is investigated for a class of networked spatial-navigation systems with quantization effects, mixed time-delays, and network-based observations (i.e. complementary measurements and regional estimations). A decentralized moving horizon estimation approach, featuring complementary reorganization and recursive procedure, is proposed to tackle this problem. First, through the proposed reorganized scheme, a random delayed system with complementary observations is reconstructed into an equivalent delay-free one without dimensional augment. Se

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Shaw, David A., Vu C. Dinh, and Frederick A. Matsen. "Joint Maximum Likelihood of Phylogeny and Ancestral States Is Not Consistent." Molecular Biology and Evolution 36, no. 10 (2019): 2352–57. http://dx.doi.org/10.1093/molbev/msz128.

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Abstract Maximum likelihood estimation in phylogenetics requires a means of handling unknown ancestral states. Classical maximum likelihood averages over these unknown intermediate states, leading to provably consistent estimation of the topology and continuous model parameters. Recently, a computationally efficient approach has been proposed to jointly maximize over these unknown states and phylogenetic parameters. Although this method of joint maximum likelihood estimation can obtain estimates more quickly, its properties as an estimator are not yet clear. In this article, we show that this

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Abdul Jalil, Nur Raihan, Nur Anisah Mohamed, and Rossita Mohamad Yunus. "Estimation in regret-regression using quadratic inference functions with ridge estimator." PLOS ONE 17, no. 7 (2022): e0271542. http://dx.doi.org/10.1371/journal.pone.0271542.

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In this paper, we propose a new estimation method in estimating optimal dynamic treatment regimes. The quadratic inference functions in myopic regret-regression (QIF-MRr) can be used to estimate the parameters of the mean response at each visit, conditional on previous states and actions. Singularity issues may arise during computation when estimating the parameters in ODTR using QIF-MRr due to multicollinearity. Hence, the ridge penalty was introduced in rQIF-MRr to tackle the issues. A simulation study and an application to anticoagulation dataset were conducted to investigate the model’s pe

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Mageed Hag Elamin, Khalid Abd El. "Particle Filtering for Enhanced Parameter Estimation in Bilinear Systems Under Colored Noise." Current Research in Statistics & Mathematics 3, no. 3 (2024): 01–20. http://dx.doi.org/10.33140/crsm.03.03.01.

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This paper addresses the challenging problem of parameter estimation in bilinear systems under colored noise. A novel approach, termed B-PF-RLS, is proposed, combining a particle filter (PF) with a recursive least squares (RLS) estimator. The B-PF-RLS algorithm tackles the complexities arising from system nonlinearities and colored noise by effectively estimating unknown system states using the particle filter, which are then integrated into the RLS parameter estimation process. Furthermore, the paper introduces an enhanced particle filter that eliminates the need for explicit knowledge of the

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Qin, Yongming, Makoto Kumon, and Tomonari Furukawa. "Estimation of a Human-Maneuvered Target Incorporating Human Intention." Sensors 21, no. 16 (2021): 5316. http://dx.doi.org/10.3390/s21165316.

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This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually constru

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Dabush, Lital, Ariel Kroizer, and Tirza Routtenberg. "State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools." Sensors 23, no. 3 (2023): 1387. http://dx.doi.org/10.3390/s23031387.

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This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weigh

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NG, HUI KHOON, and BERTHOLD-GEORG ENGLERT. "A SIMPLE MINIMAX ESTIMATOR FOR QUANTUM STATES." International Journal of Quantum Information 10, no. 04 (2012): 1250038. http://dx.doi.org/10.1142/s0219749912500384.

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Quantum tomography requires repeated measurements of many copies of the physical system, all prepared by a source in the unknown state. In the limit of very many copies measured, the often-used maximum-likelihood (ML) method for converting the gathered data into an estimate of the state works very well. For smaller data sets, however, it often suffers from problems of rank deficiency in the estimated state. For many systems of relevance for quantum information processing, the preparation of a very large number of copies of the same quantum state is still a technological challenge, which motiva

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Sveshnikov, Sergey, Victor Bocharnikov, Anatoly Pavlikovsky, and Andrey Prima. "Estimating the potential willingness of the state to use military force based on the Sugeno fuzzy integral." Yugoslav Journal of Operations Research, no. 00 (2022): 2. http://dx.doi.org/10.2298/yjor210515002s.

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Estimation of the potential willingness of the state to use military force is an integral part of the analysis of international relations and the preparation of key decisions in security sphere. Our problem was to develop a method for numerically estimating the potential willingness of any state to use military force. This method should take into account a large number of quantitative and qualitative criteria, the uncertainty of their relationships, as well as the uncertainty of the initial data, some of which can only be obtained with the help of experts. Our analysis has shown that the known

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15

Šafránek, Dominik. "Estimation of Gaussian quantum states." Journal of Physics A: Mathematical and Theoretical 52, no. 3 (2018): 035304. http://dx.doi.org/10.1088/1751-8121/aaf068.

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Farahani, Abolfazl Varvani, Mohsen Montazeri, and Mahdi Pourgholi. "Robust optimal decentralized observer for multi-phase flow measurement." Transactions of the Institute of Measurement and Control 42, no. 4 (2019): 904–16. http://dx.doi.org/10.1177/0142331219884807.

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In this paper, a non-fragile optimal observer is proposed for the decentralized multiphase flow measurement based on the interconnections between the two subsystems, that is, gas and liquid, constituting the whole system. Due to the dynamic model of system and presence of disturbances and slowly varying quantities, a non-fragile decentralized observer is designed and the states of the condensate and gas sub-systems were separately estimated. Lyapunov-based stability conditions are converted to linear matrix inequality (LMI) and observer gains are optimally selected from solution set such that

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17

Maity, Ananda G., Joshua C. A. Casapao, Naphan Benchasattabuse, Michal Hajdusek, Rodney Van Meter, and David Elkouss. "Noise estimation in an entanglement distillation protocol." ACM SIGMETRICS Performance Evaluation Review 51, no. 2 (2023): 66–68. http://dx.doi.org/10.1145/3626570.3626594.

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Estimating noise processes is an essential step for practical quantum information processing. Standard estimation tools require consuming valuable quantum resources. Here we ask the question of whether the noise affecting entangled states can be learned solely from the measurement statistics obtained during a distillation protocol. As a first step, we consider states of the Werner form and find that the Werner parameter can be estimated efficiently from the measurement statistics of an idealized distillation protocol. Our proposed estimation method can find application in scenarios where disti

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18

Zhang, Daqiao, Xiaolong Zheng, Yangguang Xie, and Xiaoxiang Hu. "Angular-Accelerometer-Based Flexible-State Estimation and Tracking Controller Design for Hypersonic Flight Vehicle." Aerospace 9, no. 4 (2022): 206. http://dx.doi.org/10.3390/aerospace9040206.

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The controller design of hypersonic flight vehicles is a challenging task, especially when its flexible states are immeasurable. Unfortunately, the flexible states are difficult to measure directly. In this paper, an angular-accelerometer-based method for the estimation of flexible states is proposed. By adding a pitch angel angular accelerometer and designing an Extended Kalman Filter-based online estimation method, the flexible states could be obtained in real time. Then, based on the estimated flexible states, a stable inversion-based controller-design method was utilized, and a robust trac

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19

Chetan Sheth, Mohmadishak Sheikh,. "Power System State Estimation using Weighted Least Square Method." Proceeding International Conference on Science and Engineering 11, no. 1 (2023): 1721–27. http://dx.doi.org/10.52783/cienceng.v11i1.327.

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State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are no

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Mohmadishak Sheikh, Chetan Sheth. "System State Estimation Using Weighted Least Square Method." Proceeding International Conference on Science and Engineering 11, no. 1 (2023): 1294–99. http://dx.doi.org/10.52783/cienceng.v11i1.276.

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State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are no

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21

Kim, HanSung, HeonYong Kang, and Moo-Hyun Kim. "Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter." Applied Sciences 9, no. 14 (2019): 2797. http://dx.doi.org/10.3390/app9142797.

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The real-time inverse estimation of the ocean wave spectrum and elevation from a vessel-motion sensor is of significant practical importance, but it is still in the developing stage. The Kalman-filter method has the advantages of real-time estimation, cost reduction, and easy installation than other methods. Reasonable estimation of high-frequency waves is important in view of covering various sea states. However, if the vessel is less responsive for high-frequency waves, amplified noise may occur and cause overestimation problem there. In this paper, a configuration of Kalman filter with appl

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Liu, Bing, Zhen Chen, and Xiang Dong Liu. "Computationally Efficient Extended Kalman Filter for Nonlinear Systems." Advanced Materials Research 846-847 (November 2013): 1205–8. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1205.

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A computationally efficient extended Kalman filter is developed for nonlinear estimation problems in this paper. The filter is performed in three stages. First, the state predictions are evaluated by the dynamic model of the system. Then, the dynamic equations of the rectification quantities for the predicted states are designed. Finally, the state estimations are updated by the predicted states with the rectification quantities multiplied by a single scale factor. One advantage of the filter is that the computational cost is reduced significantly, because the matrix coefficients of the rectif

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Talla Ouambo, Steve Alan, Alexandre Teplaira Boum, Adolphe Moukengue Imano, and Jean-Pierre Corriou. "Enhancement of the Moving Horizon Estimation Performance Based on an Adaptive Estimation Algorithm." Journal of Control Science and Engineering 2021 (December 29, 2021): 1–14. http://dx.doi.org/10.1155/2021/3776506.

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Although moving horizon estimation (MHE) is a very efficient technique for estimating parameters and states of constrained dynamical systems, however, the approximation of the arrival cost remains a major challenge and therefore a popular research topic. The importance of the arrival cost is such that it allows information from past measurements to be introduced into current estimates. In this paper, using an adaptive estimation algorithm, we approximate and update the parameters of the arrival cost of the moving horizon estimator. The proposed method is based on the least-squares algorithm bu

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Li, Jun Hua, and Hong Wei Quan. "Radiate Source Target Localization Techniques Using Airborne ESM Sensor." Applied Mechanics and Materials 602-605 (August 2014): 1056–59. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1056.

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This paper investigated the radiate source target localization techniques using airborne ESM sensor. Two algorithms are represented for different application environments. Pseudo linear estimation algorithm is used for estimating static target states with bearings-only measurements. The measurement data are bearings which are affected by random Gaussian observation noises. Iterated least squares estimation algorithm used iterative techniques to enhance the estimation accuracy. It is a kind of estimation algorithm based on the least squares estimation. Finally two scenarios are represented in M

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Ohmoto, Yoshimasa, Shigen Shimojo, Junya Morita, and Yugo Hayashi. "Estimation of ICAP States Based on Interaction Data During Collaborative Learning." Journal of Educational Data Mining 16, no. 2 (2024): 149–76. https://doi.org/10.5281/zenodo.14283893.

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The primary goal of this study is to investigate a method for estimating the state of learners in the near future using nonverbal information used in multimodal interaction as cues to provide adaptive support in collaborative learning. We used interactive-constructive-active-passive (ICAP) theory to classify learners' states in collaborative learning. We attempted to determine whether a learner’s ICAP state was passive based on multimodal data obtained during a collaborative concept-map task. We conducted an experiment on collaborative learning among learners and acquired data on convers

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Xu, Zhiwei, Jianxi Wang, Yongjie Lu, and Haoyu Li. "Estimation Strategy for the Adhesion Coefficient of Arbitrary Pavements Based on an Optimal Adaptive Fusion Algorithm." Machines 13, no. 1 (2024): 17. https://doi.org/10.3390/machines13010017.

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Accurately and quickly estimating the peak pavement adhesion coefficient is crucial for achieving high-quality driving and for optimizing vehicle stability control strategies. However, it also helps with putting forward higher requirements for vehicle driving states, tire model construction, the speed of the convergence, and the precision of the estimation algorithm. This paper unequivocally presents two highly effective methods for accurately estimating the peak pavement adhesion coefficient. Firstly, a new dimensionless tire model is constructed. A relationship between the mechanical tire ch

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Cubbage, Frederick W., Paul A. Wojtkowski, and Steven H. Bullard. "Cross-sectional estimation of empirical southern United States pulpwood harvesting cost functions." Canadian Journal of Forest Research 19, no. 6 (1989): 759–67. http://dx.doi.org/10.1139/x89-116.

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This paper develops and explains an empirical method of using cross-sectional data to determine cost functions for forestry operations. Primary logging firm data, basic production economics theory, and regression analysis were used to estimate aggregate cost functions by harvest system for southern United States pulpwood harvesting operations. Estimation of aggregate total costs first, followed by mathematical derivation of average costs, proved superior to direct estimation of average costs. Exponential functions were best at estimating the cost relationships observed in the empirical pulpwoo

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Cao, Yang, A. R. Subhashri, A. Chandrasekar, T. Radhika, and Krzysztof Przybyszewski. "Exponential State Estimation for Delayed Competitive Neural Network Via Stochastic Sampled-Data Control with Markov Jump Parameters Under Actuator Failure." Journal of Artificial Intelligence and Soft Computing Research 14, no. 4 (2024): 373–85. http://dx.doi.org/10.2478/jaiscr-2024-0020.

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Abstract This article examines the problem of estimating the states of Markovian jumping competitive neural networks, where the estimation is done using stochastic sampled-data control with time-varying delay. Instead of continuously measuring the states, the network relies on sampled measurements, and a sampled-data estimator is proposed. The estimator uses probabilistic sampling during two sampling periods, following a Bernoulli distribution. The article also takes into account the possibility of actuator failure in real systems. To ensure the exponentially mean-square stability of the delay

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29

Fiurášek, Jaromír. "Optimal probabilistic estimation of quantum states." New Journal of Physics 8, no. 9 (2006): 192. http://dx.doi.org/10.1088/1367-2630/8/9/192.

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Fröwis, F., M. Skotiniotis, B. Kraus, and W. Dür. "Optimal quantum states for frequency estimation." New Journal of Physics 16, no. 8 (2014): 083010. http://dx.doi.org/10.1088/1367-2630/16/8/083010.

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Rubio, Jose de Jesus, Jesus Lopez, Jaime Pacheco, and Rodrigo Encinas. "States Estimation in Two Mechanical Systems." IEEE Latin America Transactions 14, no. 7 (2016): 3159–67. http://dx.doi.org/10.1109/tla.2016.7587616.

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Braun, D. "Parameter estimation with mixed quantum states." European Physical Journal D 59, no. 3 (2010): 521–23. http://dx.doi.org/10.1140/epjd/e2010-00195-3.

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Blume-Kohout, Robin. "Optimal, reliable estimation of quantum states." New Journal of Physics 12, no. 4 (2010): 043034. http://dx.doi.org/10.1088/1367-2630/12/4/043034.

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Zhang, Cheng-Yang, Zhi-Hua Guo, Ying Yang, and Huai-Xin Cao. "Coherence estimation of quantum superposition states." Laser Physics Letters 22, no. 3 (2025): 035206. https://doi.org/10.1088/1612-202x/adba40.

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Abstract Quantum coherence is a fundamental resource in quantum information science and one of the most distinctive features of quantum mechanics. In this paper, we derive a tight upper bound for the coherence of the superposition composed of two orthogonal states using ℓ 1 -norm coherence measure, demonstrating that our upper bound is tighter than the one presented by Yue et al (2017 Sci. Rep. 7 4006). Furthermore, we extend the result to the superposition of n orthogonal states and establish the optimal upper bound for its coherence. Our results provide a more accurate estimate of the cohere

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Xia, Qiu, Long Chen, Xing Xu, et al. "Running States Estimation of Autonomous Four-Wheel Independent Drive Electric Vehicle by Virtual Longitudinal Force Sensors." Mathematical Problems in Engineering 2019 (June 9, 2019): 1–17. http://dx.doi.org/10.1155/2019/8302943.

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Exact sideslip angle estimation is significant to the dynamics control of four-wheel independent drive electric vehicles. It is costly and difficult-to-popularize to equip vehicular sensors for real-time sideslip angle measurement; therefore, the reliable sideslip angle estimation method is investigated in this paper. The electric driving wheel model is proposed and applied to the longitudinal force estimation. Considering that electric driving wheel model is a nonlinear model with unknown input, an unknown input estimation method is proposed to facilitate the longitudinal force observer desig

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Janzing, D. "Quantum algorithm for measuring the energy of n qubits with unknown pair-interactions." Quantum Information and Computation 2, no. 3 (2002): 198–207. http://dx.doi.org/10.26421/qic2.3-3.

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The well-known algorithm for quantum phase estimation requires that the considered unitary is available as a conditional transformation depending on the quantum state of an ancilla register. We present an algorithm converting an unknown n-qubit pair-interaction Hamiltonian into a conditional one such that standard phase estimation can be applied to measure the energy. Our essential assumption is that the considered system can be brought into interaction with a quantum computer. For large n the algorithm could still be applicable for estimating the density of energy states and might therefore b

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KRAMER, KATHLEEN A., and STEPHEN C. STUBBERUD. "ANALYSIS AND IMPLEMENTATION OF A NEURAL EXTENDED KALMAN FILTER FOR TARGET TRACKING." International Journal of Neural Systems 16, no. 01 (2006): 1–13. http://dx.doi.org/10.1142/s0129065706000457.

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Having a better motion model in the state estimator is one way to improve target tracking performance. Since the motion model of the target is not known a priori, either robust modeling techniques or adaptive modeling techniques are required. The neural extended Kalman filter is a technique that learns unmodeled dynamics while performing state estimation in the feedback loop of a control system. This coupled system performs the standard estimation of the states of the plant while estimating a function to approximate the difference between the given state-coupling function model and the behavio

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Suzuki, Jun. "Parameter estimation of qubit states with unknown phase parameter." International Journal of Quantum Information 13, no. 01 (2015): 1450044. http://dx.doi.org/10.1142/s0219749914500440.

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We discuss a problem of parameter estimation for quantum two-level system, qubit system, in presence of unknown phase parameter. We analyze trade-off relations for mean square errors (MSEs) when estimating relevant parameters with separable measurements based on known precision bounds; the symmetric logarithmic derivative (SLD) Cramér–Rao (CR) bound and Hayashi–Gill–Massar (HGM) bound. We investigate the optimal measurement which attains the HGM bound and discuss its properties. We show that the HGM bound for relevant parameters can be attained asymptotically by using some fraction of given n

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Sudry, Matan, and Erez Karpas. "Learning to Estimate Search Progress Using Sequence of States." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 362–70. http://dx.doi.org/10.1609/icaps.v32i1.19821.

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Many problems of interest can be solved using heuristic search algorithms. When solving a heuristic search problem, we are often interested in estimating search progress, that is, how much longer until we have a solution. Previous work on search progress estimation derived formulas based on some relevant features that can be observed from the behavior of the search algorithm. In this paper, rather than manually deriving such formulas we leverage machine learning to learn more accurate search progress predictors automatically. We train a Long Short-Term Memory (LSTM) network, which takes as inp

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Adi, Faya Safirra, Yee Jin Lee, and Hwachang Song. "State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks." Applied Sciences 10, no. 9 (2020): 3028. http://dx.doi.org/10.3390/app10093028.

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The development of state estimators for local electrical energy supply systems is inevitable as the role of the system’s become more important, especially with the recent increased interest in direct current (DC) microgrids. Proper control and monitoring requires a state estimator that can adapt to the new technologies for DC microgrids. This paper mainly deals with the DC microgrid state estimation (SE) using a modified long short-term memory (LSTM) network, which until recently has been applied only in forecasting studies. The modified LSTM network for the proposed state estimator adopted a

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Pei, Xiaofei, Zhenfu Chen, Bo Yang, and Duanfeng Chu. "Estimation of states and parameters of multi-axle distributed electric vehicle based on dual unscented Kalman filter." Science Progress 103, no. 1 (2019): 003685041988008. http://dx.doi.org/10.1177/0036850419880083.

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Distributed electric drive technology has become an important trend because of its ability to enhance the dynamic performance of multi-axle heavy vehicle. This article presents a joint estimation of vehicle’s state and parameters based on the dual unscented Kalman filter. First, a 12-degrees-of-freedom dynamic model of an 8 × 8 distributed electric vehicle is established. Considering the dynamic variation of some key parameters for heavy vehicle, a real-time parameter estimator is introduced, based on which simultaneous estimation of vehicle’s state and parameters is implemented under the dual

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Jiang, Bing, Zeqi Chen, and Feifan Chen. "Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method." Energies 12, no. 10 (2019): 1878. http://dx.doi.org/10.3390/en12101878.

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The equivalent-circuit model (ECM) is widely used in online estimating the parameters and states of lithium-ion batteries. However, the sampling delay between the voltage and current of a battery is generally overlooked, which is unavoidable in a modular battery management system (BMS) and would lead to wrong results in the estimation of battery parameters and states. In this paper, with the first-order resistor–capacitor (RC) model as our battery model, we analyze the influence mechanism of sampling delay and then propose an optimized method for online estimating battery parameters. The mathe

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Chen, Liang, Peng Jin, Jing Yang, Yang Li, and Yi Song. "Robust Kalman Filter-Based Dynamic State Estimation of Natural Gas Pipeline Networks." Mathematical Problems in Engineering 2021 (March 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/5590572.

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To obtain the accurate transient states of the big scale natural gas pipeline networks under the bad data and non-zero mean noises conditions, a robust Kalman filter-based dynamic state estimation method is proposed using the linearized gas pipeline transient flow equations in this paper. Firstly, the dynamic state estimation model is built. Since the gas pipeline transient flow equations are less than the states, the boundary conditions are used as supplementary constraints to predict the transient states. To increase the measurement redundancy, the zero mass flow rate constraints at the sink

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Gong, Qingrui, Ping Wang, and Ze Cheng. "A Data-Driven Model Framework Based on Deep Learning for Estimating the States of Lithium-Ion Batteries." Journal of The Electrochemical Society 169, no. 3 (2022): 030532. http://dx.doi.org/10.1149/1945-7111/ac5bac.

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The accurate estimation of state of charge (SOC) and state of health (SOH) of lithium-ion battery is crucial to ensure the safe and stable operation of the battery. In this paper, a data-driven model framework based on deep learning for estimating SOC and SOH is proposed, which mainly consists of long short-term memory (LSTM) neural network and back propagation (BP) neural network. The switch between SOC estimation model and SOH estimation model can be realized by adjusting the output mode of LSTM. When estimating SOC, the LSTM is set to have corresponding output at each input. The model takes

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Meera, R. Karamta, and G. Jamnani J. "Dynamic Mathematical Modeling of Unified Power Flow Controller Integrated into Single Machine Power System for Dynamic State Estimation." European Journal of Advances in Engineering and Technology 5, no. 2 (2018): 122–27. https://doi.org/10.5281/zenodo.10702279.

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<strong>ABSTRACT </strong> Flexible AC Transmission System (FACTS) is a progressive technology that has allowed power system utilities to make the most out of the existing grid and control the governing parameters that affect the power flow and/or stability of the transmission and power system broadly.  One of the most important FACTS devices is Unified Power Flow Controller (UPFC) which provides fast-acting reactive power compensation on high-voltage electricity transmission networks. The aim of this paper is to discuss in detail the dynamic model of UPFC. It is integrated to a Single Ma

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Li, Yunji, Wenzhuo Zhou, and Yajun Wu. "Event-Triggered Fault Estimation and Fault Tolerance for Cyber-Physical Systems with False Data Injection Attacks." Actuators 12, no. 5 (2023): 197. http://dx.doi.org/10.3390/act12050197.

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This paper investigates an event-triggered framework for addressing fault estimation and fault tolerance issues in discrete-time cyber-physical systems (CPSs) with partial state saturations and random false data injection attacks (FDIAs). A stochastic variable is introduced to characterize the random FDIAs and to establish the corresponding model. A reduced-order fault estimator and an event condition are co-derived to reconstruct system states and actuator faults. The proposed event-triggered transmission scheme helps reduce network utilization in the sensor-to-estimator channel. A sufficient

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Altin, Hakan. "Volatility Analysis in International Indices." International Journal of Sustainable Economies Management 11, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijsem.304461.

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Modeling and the estimation of volatility in financial time series are important research subjects for which ARCH family models are recommended. These models are widely used to analyze volatility and manage risk in financial assets. In this study, share indices from the BRIC countries, Europe and the United States were analyzed to determine volatility in international indices. Current data was used to examine the period 1982-2021. Within this framework, the existence of asymmetric information, the leverage effect and the permanence of shocks were examined. Estimation results show the existence

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Demir, Ridvan, and Murat Barut. "Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control." Transactions of the Institute of Measurement and Control 40, no. 13 (2017): 3884–98. http://dx.doi.org/10.1177/0142331217734631.

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This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance ([Formula: see text]) and rotor resistance ([Formula: see text]) for speed-sensorless induction motor control. The EKF simultaneously estimates the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator currents, the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator fluxes, r

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Zhang, Tao, Ningyuan Guo, Xiaoxia Sun, et al. "A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles." Sustainability 13, no. 9 (2021): 5166. http://dx.doi.org/10.3390/su13095166.

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Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation of its states like state of charge (SOC), capacity and peak power is indispensable. This paper proposes a systematic co-estimation framework for the lithium-ion battery in electric vehicle applications. First, a linearized equivalent circuit-based battery model, together with an affine projection

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Hakimian, Vahid, and Hossein Seifi. "An Optimization-Based Robust Dynamic State Estimation for Power Systems with Synchronized Phasor Measurement Units, Involving Disturbance Rejection." International Transactions on Electrical Energy Systems 2023 (March 23, 2023): 1–22. http://dx.doi.org/10.1155/2023/1820478.

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State estimation provides the best estimate of the system states using a set of measurements that is divided into two main classes; static and dynamic types. The state estimator should provide an acceptable response in the presence of system changes and/or disturbances. To achieve this aim, considering the high rate of data changes and the high volume of exchanging information, the phasor measurement units could play an outstanding role. Furthermore, due to the inevitable errors of the measurements, and to provide an authentic estimate of the system states using the measured data, a state esti

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