Articoli di riviste: "States estimation"

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Wasim, Muhammad, Ahsan Ali, Muhammad Mateen Afzal Awan e Inam ul Hasan Shaikh. "Estimation of airship states and model uncertainties using nonlinear estimators". Mehran University Research Journal of Engineering and Technology 43, n. 1 (1 gennaio 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 neutrally buoyant airship. It is considered that minimum sensor measurements are available, and data is corrupted with process and measurement noise. A novel lumped model uncertainty estimation approach is formulated where airship model is augmented with six extra state variables capturing the model uncertainty of the airship. The designed estimator estimates the airship model uncertainty along with its states. Nonlinear estimators, Extended Kalman Filter and Unscented Kalman Filter are designed for estimating airship attitude, linear velocities, angular velocities and model uncertainties. While Particle filter is designed for the estimation of airship attitude, linear velocities and angular velocities. Simulations have been performed using nonlinear 6-DOF simulation model of experimental airship for assessing the estimator performances. 1−𝜎 uncertainty bound and error analysis have been performed for the validation. A comparative study of the estimator's performances is also carried out.

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Talla Ouambo, Steve Alan, Alexandre Teplaira Boum e 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 (12 novembre 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 factor that is based on the constant information principle. The simulation results show that the adaptive estimator can efficiently estimate the states and parameters of the induction machine with a fast convergence rate despite the initial parametric errors.

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Zerdali, Emrah, e Murat Barut. "Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors". Power Electronics and Drives 3, n. 1 (1 dicembre 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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Zhang, Xiao, Feng Ding, Ling Xu, Ahmed Alsaedi e Tasawar Hayat. "A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise". Mathematics 7, n. 4 (17 aprile 2019): 356. http://dx.doi.org/10.3390/math7040356.

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This paper is concerned with the joint state and parameter estimation methods for a bilinear system in the state space form, which is disturbed by additive noise. In order to overcome the difficulty that the model contains the product term of the system input and states, we make use of the hierarchical identification principle to present new methods for estimating the system parameters and states interactively. The unknown states are first estimated via a bilinear state estimator on the basis of the Kalman filtering algorithm. Then, a state estimator-based recursive generalized least squares (RGLS) algorithm is formulated according to the least squares principle. To improve the parameter estimation accuracy, we introduce the data filtering technique to derive a data filtering-based two-stage RGLS algorithm. The simulation example indicates the efficiency of the proposed algorithms.

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Jamal, Alaa, e Raphael Linker. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model". Agriculture 10, n. 12 (7 dicembre 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 priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

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Gao, Chao, Guorong Zhao, Jianhua Lu e 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, n. 11 (8 giugno 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. Second, with this equivalent system, a robust moving horizon estimation scheme is presented as a uniform estimator for the navigational states. Third, for the demand of real-time estimate, the recursive form of decentralized moving horizon estimation approach is developed. Furthermore, a collective estimation is obtained through the weighted fusion of two parts, i.e. complementary measurements based estimation, and regional estimations directly from the neighbors. The convergence properties of the proposed estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, i.e. quantization density and delay occur probability. Finally, an application example to networked unmanned aerial vehicles is presented and comparative simulations demonstrate the main features of the proposed method.

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Zhao, Dengfeng, Haiyang Li, Fang Zhou, Yudong Zhong, Guosheng Zhang, Zhaohui Liu e Junjian Hou. "Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses". World Electric Vehicle Journal 14, n. 6 (2 giugno 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, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods.

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Shaw, David A., Vu C. Dinh e Frederick A. Matsen. "Joint Maximum Likelihood of Phylogeny and Ancestral States Is Not Consistent". Molecular Biology and Evolution 36, n. 10 (23 maggio 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 method of jointly estimating phylogenetic parameters along with ancestral states is not consistent in general. We find a sizeable region of parameter space that generates data on a four-taxon tree for which this joint method estimates the internal branch length to be exactly zero, even in the limit of infinite-length sequences. More generally, we show that this joint method only estimates branch lengths correctly on a set of measure zero. We show empirically that branch length estimates are systematically biased downward, even for short branches.

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Abdul Jalil, Nur Raihan, Nur Anisah Mohamed e Rossita Mohamad Yunus. "Estimation in regret-regression using quadratic inference functions with ridge estimator". PLOS ONE 17, n. 7 (21 luglio 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 performance in parameter estimation. The results show that estimations using rQIF-MRr are more efficient than the QIF-MRr.

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Qin, Yongming, Makoto Kumon e Tomonari Furukawa. "Estimation of a Human-Maneuvered Target Incorporating Human Intention". Sensors 21, n. 16 (6 agosto 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 construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.

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Dabush, Lital, Ariel Kroizer e Tirza Routtenberg. "State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools". Sensors 23, n. 3 (26 gennaio 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 weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.

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NG, HUI KHOON, e BERTHOLD-GEORG ENGLERT. "A SIMPLE MINIMAX ESTIMATOR FOR QUANTUM STATES". International Journal of Quantum Information 10, n. 04 (giugno 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 motivates us to look for estimation strategies that perform well even when there is not much data. After reviewing the concept of minimax state estimation, we use minimax ideas to construct a simple estimator for quantum states. We demonstrate that, for the case of tomography of a single qubit, our estimator significantly outperforms the ML estimator for small number of copies of the state measured. Our estimator is always full-rank, and furthermore, has a natural dependence on the number of copies measured, which is missing in the ML estimator.

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Sveshnikov, Sergey, Victor Bocharnikov, Anatoly Pavlikovsky e Andrey Prima. "Estimating the potential willingness of the state to use military force based on the Sugeno fuzzy integral". Yugoslav Journal of Operations Research, n. 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 methods have a number of serious shortcomings. We proposed to solve this problem based on the representation of partial estimations of states in the form of fuzzy sets, and the importance of criteria in the form of a fuzzy measure. We also proposed to aggregate the partial estimations using the Sugeno fuzzy integral. We developed a hierarchical structure of estimation criteria, determined the importance of the criteria, built an observation channel based on the Harrington curve to obtain input estimations, and also developed an aggregation algorithm. As a result, we calculated estimations for 137 states and examined their potential willingness to use military force. The results disclose new aspects of using fuzzy-integral calculus to construct hierarchical models of multi-criteria estimating, and also demonstrate the possibility of using artificial intelligence methods to obtain numerical estimations in the sphere of international relations.

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Šafránek, Dominik. "Estimation of Gaussian quantum states". Journal of Physics A: Mathematical and Theoretical 52, n. 3 (18 dicembre 2018): 035304. http://dx.doi.org/10.1088/1751-8121/aaf068.

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Maity, Ananda G., Joshua C. A. Casapao, Naphan Benchasattabuse, Michal Hajdusek, Rodney Van Meter e David Elkouss. "Noise estimation in an entanglement distillation protocol". ACM SIGMETRICS Performance Evaluation Review 51, n. 2 (28 settembre 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 distillation is an unavoidable step.

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Farahani, Abolfazl Varvani, Mohsen Montazeri e Mahdi Pourgholi. "Robust optimal decentralized observer for multi-phase flow measurement". Transactions of the Institute of Measurement and Control 42, n. 4 (4 dicembre 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 the effect of the disturbance on the states’ estimation error becomes minimized. The estimation is conducted using the real-time measurements including lines pressures, single-phase gas flow, and single-phase liquid flow in the refinery outlet. To check the stability and performance of the system against the changes, the Lyapunov theory has been used. Finally, the estimation results are compared with real-world data from the industry showing the high accuracy of this method as the estimations were consistent with the operation data. In all stages, the investigations were based on the data collected from the actual process in the South Pars Gas Complex (SPGC), Iran. Additionally, the Extended Kalman Filter (EKF) based on the simplified drift flux model (DFM) was used to estimate the states then both methods’ results are compared and using the HYSYS simulator with the real process data, it is found that both observers are capable to identify the states with some differences in performance and DFM model is sufficient for estimation of parameters and states of the multiphase flow entering the gas refinery. As a result, these techniques not only can be substituted for the existing system at the gas refinery, but also can be as a backup for available measurement systems.

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Zhang, Daqiao, Xiaolong Zheng, Yangguang Xie e Xiaoxiang Hu. "Angular-Accelerometer-Based Flexible-State Estimation and Tracking Controller Design for Hypersonic Flight Vehicle". Aerospace 9, n. 4 (10 aprile 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 tracking controller was designed for hypersonic flight vehicles. The proposed method provides an effective means of estimating flexible states and conducting the observer-based controller design of hypersonic flight vehicles. Finally, a numeral simulation is given to show the effectiveness of the proposed control method.

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Talla Ouambo, Steve Alan, Alexandre Teplaira Boum, Adolphe Moukengue Imano e Jean-Pierre Corriou. "Enhancement of the Moving Horizon Estimation Performance Based on an Adaptive Estimation Algorithm". Journal of Control Science and Engineering 2021 (29 dicembre 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 but includes a variable forgetting factor which is based on the constant information principle and a dead zone which ensures robustness. We show by this method that a fairly good approximation of the arrival cost guarantees the convergence and stability of estimates. Some simulations are made to show and demonstrate the effectiveness of the proposed method and to compare it with the classical MHE.

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Liu, Bing, Zhen Chen e Xiang Dong Liu. "Computationally Efficient Extended Kalman Filter for Nonlinear Systems". Advanced Materials Research 846-847 (novembre 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 rectified equations are constant. It doesnt need to evaluate the Jacobian matrixes and the matrix inversion for updating the gain matrix neither. Another advantage is that a single scale factor is introduced to scale the model approximated error, leading to an improved filter performance. The excellent performance of the proposed filter is demonstrated by an example with the application to the estimation problems for the sensorless permanent magnet synchronous motor direct torque control system.

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Chetan Sheth, Mohmadishak Sheikh,. "Power System State Estimation using Weighted Least Square Method". Proceeding International Conference on Science and Engineering 11, n. 1 (18 febbraio 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 not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

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Mohmadishak Sheikh, Chetan Sheth. "System State Estimation Using Weighted Least Square Method". Proceeding International Conference on Science and Engineering 11, n. 1 (18 febbraio 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 not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

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Kim, HanSung, HeonYong Kang e Moo-Hyun Kim. "Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter". Applied Sciences 9, n. 14 (12 luglio 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 applying the principle of Wiener filter is proposed to suppress those over-estimations. Over-estimation is significantly reduced at high frequencies when the method is applied, and reliable real-time wave spectra and elevations can be obtained. The simulated sensor data was used, but the proposed algorithm has been proved to perform well for various sea states and different vessels. In addition, the proposed Kalman-filter technique is robust when it is applied to time-varying sea states.

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Fiurášek, Jaromír. "Optimal probabilistic estimation of quantum states". New Journal of Physics 8, n. 9 (14 settembre 2006): 192. http://dx.doi.org/10.1088/1367-2630/8/9/192.

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Fröwis, F., M. Skotiniotis, B. Kraus e W. Dür. "Optimal quantum states for frequency estimation". New Journal of Physics 16, n. 8 (5 agosto 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 e Rodrigo Encinas. "States Estimation in Two Mechanical Systems". IEEE Latin America Transactions 14, n. 7 (luglio 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, n. 3 (27 luglio 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, n. 4 (20 aprile 2010): 043034. http://dx.doi.org/10.1088/1367-2630/12/4/043034.

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Cubbage, Frederick W., Paul A. Wojtkowski e Steven H. Bullard. "Cross-sectional estimation of empirical southern United States pulpwood harvesting cost functions". Canadian Journal of Forest Research 19, n. 6 (1 giugno 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 pulpwood harvesting data.

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Li, Jun Hua, e Hong Wei Quan. "Radiate Source Target Localization Techniques Using Airborne ESM Sensor". Applied Mechanics and Materials 602-605 (agosto 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 Matlab simulation environments to validate the effectiveness of the algorithms.

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Suzuki, Jun. "Parameter estimation of qubit states with unknown phase parameter". International Journal of Quantum Information 13, n. 01 (febbraio 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 quantum states to estimate the phase parameter. We also discuss the Holevo bound which can be attained asymptotically by a collective measurement.

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Xia, Qiu, Long Chen, Xing Xu, Yingfeng Cai, Haobin Jiang, Te Chen e Guangxiang Pan. "Running States Estimation of Autonomous Four-Wheel Independent Drive Electric Vehicle by Virtual Longitudinal Force Sensors". Mathematical Problems in Engineering 2019 (9 giugno 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 design, in which the adaptive high-order sliding mode observer is designed to achieve the state estimation, the analytic formula of longitudinal force is obtained by decoupling electric driving wheel model, and the longitudinal force estimator is designed by recurrence estimation method. With the designed virtual longitudinal force sensor, an adaptive attenuated Kalman filtering is proposed to estimate the vehicle running state, in which the time-varying attenuation factor is applied to weaken the past data to the current filter and the covariance of process noise and measurement noise can be adjusted adaptively. Finally, simulations and experiments are conducted and the effectiveness of proposed estimation method is validated.

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Adi, Faya Safirra, Yee Jin Lee e Hwachang Song. "State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks". Applied Sciences 10, n. 9 (26 aprile 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 specifically weighted least square (WLS)-based loss function for training. To demonstrate the performance of the proposed state estimator, a comparison study was done with other SE methods included in this paper. The results showed that the proposed state estimator had high accuracy in estimating the states of DC microgrids. Other than the enhanced accuracy, the deep-learning-based state estimator also provided faster computation speeds than the conventional state estimator.

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Sudry, Matan, e Erez Karpas. "Learning to Estimate Search Progress Using Sequence of States". Proceedings of the International Conference on Automated Planning and Scheduling 32 (13 giugno 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 input sequences of nodes expanded by the search algorithm, and predicts how far along with the search we are. Importantly, our approach still treats the search algorithm as a black box and does not look into the contents of search nodes. An empirical evaluation shows our technique outperforms previous search progress estimation techniques.

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Jiang, Bing, Zeqi Chen e Feifan Chen. "Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method". Energies 12, n. 10 (16 maggio 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 mathematical model derived from the first-order RC model and the approximation method of first-order derivative are optimized. The recursive least squares (RLS) algorithm is used for identifying the parameters of the model. In order to verify the proposed method, a modular battery test system with high sampling frequency and high synchronization accuracy is developed. The experiment results indicate that the sampling delay would cause the estimation process to fluctuate, and the optimized method effectively improves the tolerance range of sampling delay.

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Janzing, D. "Quantum algorithm for measuring the energy of n qubits with unknown pair-interactions". Quantum Information and Computation 2, n. 3 (aprile 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 be useful for finding energy gaps in solid states.

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KRAMER, KATHLEEN A., e STEPHEN C. STUBBERUD. "ANALYSIS AND IMPLEMENTATION OF A NEURAL EXTENDED KALMAN FILTER FOR TARGET TRACKING". International Journal of Neural Systems 16, n. 01 (febbraio 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 behavior of the true plant dynamics. At each sample step, this new model is added to the existing model to improve the state estimate. The neural extended Kalman filter has also been investigated as a target tracking estimation routine. Implementation issues for this adaptive modeling technique, including neural network training parameters, were investigated and an analysis was made of the quality of performance that the technique can have for tracking maneuvering targets.

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Pei, Xiaofei, Zhenfu Chen, Bo Yang e Duanfeng Chu. "Estimation of states and parameters of multi-axle distributed electric vehicle based on dual unscented Kalman filter". Science Progress 103, n. 1 (3 ottobre 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 unscented Kalman filter framework. Simulation results show that the dual unscented Kalman filter estimator has a high estimation accuracy for multi-axle distributed electric vehicle’s state and key parameters. Therefore, it is reliable for vehicle dynamics control without the influence of unknown or varying parameters.

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Gong, Qingrui, Ping Wang e 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, n. 3 (1 marzo 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 10 consecutive voltage sampling points as input and the estimated value of SOC at the last sampling moment as output. When estimating SOH, the LSTM is set to have a corresponding output only at the last input. The model takes the sequence of 150 sampling points on the charging voltage curve as input and the SOH value at the current cycle as output. Experiments are carried out on the Oxford battery degradation dataset, and the results show that the proposed model framework can obtain accurate and reliable estimates of SOC and SOH.

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Ströbel, Marco, Julia Pross-Brakhage, Mike Kopp e Kai Peter Birke. "Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks". Batteries 7, n. 4 (12 dicembre 2021): 85. http://dx.doi.org/10.3390/batteries7040085.

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Tracking the cell temperature is critical for battery safety and cell durability. It is not feasible to equip every cell with a temperature sensor in large battery systems such as those in electric vehicles. Apart from this, temperature sensors are usually mounted on the cell surface and do not detect the core temperature, which can mean detecting an offset due to the temperature gradient. Many sensorless methods require great computational effort for solving partial differential equations or require error-prone parameterization. This paper presents a sensorless temperature estimation method for lithium ion cells using data from electrochemical impedance spectroscopy in combination with artificial neural networks (ANNs). By training an ANN with data of 28 cells and estimating the cell temperatures of eight more cells of the same cell type, the neural network (a simple feed forward ANN with only one hidden layer) was able to achieve an estimation accuracy of ΔT= 1 K (10 ∘C <T< 60 ∘C) with low computational effort. The temperature estimations were investigated for different cell types at various states of charge (SoCs) with different superimposed direct currents. Our method is easy to use and can be completely automated, since there is no significant offset in monitoring temperature. In addition, the prospect of using the above mentioned approach to estimate additional battery states such as SoC and state of health (SoH) is discussed.

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Stull, Kyra E., Elaine Y. Chu, Louise K. Corron e Michael H. Price. "Subadult Age Estimation Using the Mixed Cumulative Probit and a Contemporary United States Population". Forensic Sciences 2, n. 4 (10 novembre 2022): 741–79. http://dx.doi.org/10.3390/forensicsci2040055.

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The mixed cumulative probit (MCP), a new, flexible algorithm that accommodates a variety of mean and shape parameters in univariate models and conditional dependence/independence in multivariate models, was used to develop subadult age estimation models. Sixty-two variables were collected on computed tomography (CT) images of 1317 individuals (537 females and 780 males) aged between birth and 21 years from the United States sample in the Subadult Virtual Anthropology Database (SVAD). Long bone measurements (n = 18), stages of epiphyseal fusion and ossification (n = 28), and stages of dental development of permanent teeth (n = 16) were used in univariate, multivariate, and mixed models and compared using test mean log posterior (TMNLP), root mean squared error (RMSE), and percent accuracy on an independent test sample. Out of the six possible parameter combinations, all combinations were accounted for at least once in the data and conditionally dependent models outperformed the conditionally independent models. Overall, multivariate models exhibited smaller TMNLP and RMSE, and an overall greater stability in the age estimations compared to univariate models across all ages and independent of indicator type. Pre-optimized subadult age estimation models are freely available for immediate application through MCP-S-Age, a graphical user interface.

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Erkeç, Tuncay Yunus, e Chingiz Hajiyev. "Fault-tolerant estimation of satellite orbital states". International Journal of Sustainable Aviation 7, n. 3 (2021): 203. http://dx.doi.org/10.1504/ijsa.2021.119170.

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Ma, Xinran, Z. C. Tu e Shi-Ju Ran. "Deep Learning Quantum States for Hamiltonian Estimation". Chinese Physics Letters 38, n. 11 (1 dicembre 2021): 110301. http://dx.doi.org/10.1088/0256-307x/38/11/110301.

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Human experts cannot efficiently access physical information of a quantum many-body states by simply “reading” its coefficients, but have to reply on the previous knowledge such as order parameters and quantum measurements. We demonstrate that convolutional neural network (CNN) can learn from coefficients of many-body states or reduced density matrices to estimate the physical parameters of the interacting Hamiltonians, such as coupling strengths and magnetic fields, provided the states as the ground states. We propose QubismNet that consists of two main parts: the Qubism map that visualizes the ground states (or the purified reduced density matrices) as images, and a CNN that maps the images to the target physical parameters. By assuming certain constraints on the training set for the sake of balance, QubismNet exhibits impressive powers of learning and generalization on several quantum spin models. While the training samples are restricted to the states from certain ranges of the parameters, QubismNet can accurately estimate the parameters of the states beyond such training regions. For instance, our results show that QubismNet can estimate the magnetic fields near the critical point by learning from the states away from the critical vicinity. Our work provides a data-driven way to infer the Hamiltonians that give the designed ground states, and therefore would benefit the existing and future generations of quantum technologies such as Hamiltonian-based quantum simulations and state tomography.

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Erkeç, Tuncay Yunus, e Chingiz Hajiyev. "Fault-tolerant estimation of satellite orbital states". International Journal of Sustainable Aviation 7, n. 3 (2021): 1. http://dx.doi.org/10.1504/ijsa.2021.10040510.

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Gao, X. H., e S. M. Fei. "Estimation of concurrence for multipartite mixed states". European Physical Journal Special Topics 159, n. 1 (giugno 2008): 71–77. http://dx.doi.org/10.1140/epjst/e2008-00694-x.

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Fujiwara, Akio, e Hiroshi Nagaoka. "An estimation theoretical characterization of coherent states". Journal of Mathematical Physics 40, n. 9 (settembre 1999): 4227–39. http://dx.doi.org/10.1063/1.532962.

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46

Raol, J. R., e N. K. Sinha. "Estimation of Orbital States of a Satellite". IFAC Proceedings Volumes 18, n. 5 (luglio 1985): 675–80. http://dx.doi.org/10.1016/s1474-6670(17)60638-4.

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Watanabe, Sumio. "Equations of states in singular statistical estimation". Neural Networks 23, n. 1 (gennaio 2010): 20–34. http://dx.doi.org/10.1016/j.neunet.2009.08.002.

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48

Combes, Joshua, e H. M. Wiseman. "States for phase estimation in quantum interferometry". Journal of Optics B: Quantum and Semiclassical Optics 7, n. 1 (4 dicembre 2004): 14–21. http://dx.doi.org/10.1088/1464-4266/7/1/004.

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Ely, Paul B., e John C. Peters. ""Probable Maximum Flood Estimation ? Eastern United States"". Journal of the American Water Resources Association 22, n. 1 (febbraio 1986): 139. http://dx.doi.org/10.1111/j.1752-1688.1986.tb01872.x.

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Scali, Stefano, e Roberto Franzosi. "Entanglement estimation in non-optimal qubit states". Annals of Physics 411 (dicembre 2019): 167995. http://dx.doi.org/10.1016/j.aop.2019.167995.

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