A hybrid algorithm for multiple parameters estimation with UCA of electromagnetic vector sensors

2016 ◽  
Author(s):  
Julan Xie ◽  
Xue Yang ◽  
Huiyong Li ◽  
Jinfeng Hu
Keyword(s):  
Hybrid Algorithm ◽  
Parameters Estimation ◽  
Multiple Parameters ◽  
Vector Sensors

scholarly journals Open Data, Models, and Codes for Vanadium Redox Batch Cell Systems: A Systems Approach Using Zero-Dimensional Models

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Seong Beom Lee ◽  
Kishalay Mitra ◽  
Harry D. Pratt ◽  
Travis M. Anderson ◽  
Venkatasailanathan Ramadesigan ◽  
...  
Keyword(s):  
Systems Approach ◽  
Open Data ◽  
Cell System ◽  
Parameters Estimation ◽  
Suitable Model ◽  
Source Codes ◽  
Multiple Parameters ◽  
C 30 ◽  
Vanadium Redox ◽  
Batch Cell

Abstract In this paper, we study, analyze, and validate some important zero-dimensional physics-based models for vanadium redox batch cell (VRBC) systems and formulate an adequate physics-based model that can predict the battery performance accurately. In the model formulation process, a systems approach to multiple parameters estimation has been conducted using VRBC systems at low C-rates (∼C/30). In this batch cell system, the effect of ions' crossover through the membrane is dominant, and therefore, the capacity loss phenomena can be explicitly observed. Paradoxically, this means that using the batch system might be a better approach for identifying a more suitable model describing the effect of ions transport. Next, we propose an efficient systems approach, which enables to help understand the battery performance quickly by estimating all parameters of the battery system. Finally, open source codes, executable files, and experimental data are provided to enable people's access to robust and accurate models and optimizers. In battery simulations, different models and optimizers describing the same systems produce different values of the estimated parameters. Providing an open access platform can accelerate the process to arrive at robust models and optimizers by continuous modification from the users' side.

scholarly journals Loss weight adaptive multi-task learning based optical performance monitor for multiple parameters estimation

2019 ◽  
Vol 27 (25) ◽  
pp. 37041 ◽  
Author(s):  
Zhenming Yu ◽  
Zhiquan Wan ◽  
Liang Shu ◽  
Shaohua Hu ◽  
Yilun Zhao ◽  
...  
Keyword(s):  
Parameters Estimation ◽  
Optical Performance ◽  
Multiple Parameters ◽  
Task Learning ◽  
Performance Monitor

scholarly journals Multiple Parameters Estimation Based on Transmitter Side Information in Wireless Power Transfer System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 164835-164843
Author(s):  
Wenxun Xiao ◽  
Ruigeng Shen ◽  
Bo Zhang ◽  
Dongyuan Qiu ◽  
Yanfeng Chen ◽  
...  
Keyword(s):  
Side Information ◽  
Wireless Power Transfer ◽  
Parameters Estimation ◽  
Transfer System ◽  
Power Transfer ◽  
Wireless Power ◽  
Multiple Parameters ◽  
Wireless Power Transfer System

scholarly journals DOA and Polarization Parameters Estimation by Exploiting Canonical Polyadic Decomposition of Tensors

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Long Liu ◽  
Ling Wang ◽  
Jian Xie ◽  
Zhaolin Zhang
Keyword(s):  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Polarization State ◽  
Parameters Estimation ◽  
Mean Square ◽  
Estimation Errors ◽  
Vector Sensors ◽  
Canonical Polyadic Decomposition ◽  
Parameters Of Signals ◽  
High Level

A new algorithm to estimate the direction of arrival (DOA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector-sensors is presented by exploiting the canonical polyadic decomposition (CPD) of tensors. In addition to spatial and temporal diversities, further information from the polarization domain is considered and used in this paper. Estimation errors of these parameters are evaluated by the Cramér-Rao lower bound (CRB) benchmark, in the presence of additive white Gaussian noise (AWGN). The superiority of the proposed algorithm is shown by comparing with the derivative algorithms of MUSIC and ESPRIT. In the proposed algorithm, the parameters can be estimated by virtue of the diversities of the spatial and polarization belonging to the factor matrices, rather than the conventional subspace which is the foundation of MUSIC and ESPRIT. Additionally, the classical CPD algorithm based on Alternating Least Squares (ALS) is introduced to verify the efficacy of the proposed CPD algorithm. Results demonstrate that when the number of snapshots is greater than 50, the proposed algorithm requires a smaller number of snapshots to achieve a high level of performance, compared against the subspace-based algorithms and the ALS-based algorithm. Furthermore, in the matter of the array with a small number of sensors, the discovered advantage concerning the Root Mean Square Error (RMSE) in estimating the DOA and the polarization state of the signal is noteworthy.

scholarly journals Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter

2021 ◽  
Author(s):  
Mengtian Lu ◽  
Sicheng Lu ◽  
Weihong Liao ◽  
Xiaohui Lei ◽  
Zhaokai Yin ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Assimilation Efficiency ◽  
Estimation Method ◽  
Initial Distribution ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Multiple Parameters ◽  
Basin Surface

Abstract Although field measurements and using long hydrological datasets provide a reliable method for parameters' calibration, changes in the underlying basin surface and lack of hydrometeorological data may affect parameter accuracy in streamflow simulation. The ensemble Kalman filter (EnKF) can be used as a real-time parameter correction method to solve this problem. In this study, five representative Xin'anjiang model parameters are selected to study the effects of the initial parameter ensemble distribution and the specific function form of the parameter on EnKF parameter estimation process for both single and multiple parameters. Results indicate: (1) the method of parameter calibration to determine the initial distribution mean can improve the assimilation efficiency; (2) there is mutual interference among the parameters during multiple parameters' estimation which invalidates some conclusions of single-parameter estimation. We applied and evaluated the EnKF method in Jinjiang River Basin, China. Compared to traditional approaches, our method showed a better performance in both basins with long hydrometeorological dataset (an increase of Kling–Gupta efficiency (KGE) from 0.810 to 0.887 and a decrease of bias from −1.08% to −0.74%); and in basins with a lack of hydrometeorological data (an increase of KGE from 0.536 to 0.849 and a decrease of bias from −15.55% to −11.42%).

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