This paper is aimed to develop and test one novel and unexplored enhancement of the classical model reduction method applied to a class of biochemical networks. Both methods, being (i) the standard quasi-steady-state ...
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This paper is aimed to develop and test one novel and unexplored enhancement of the classical model reduction method applied to a class of biochemical networks. Both methods, being (i) the standard quasi-steady-state approximation (QSSA), and (ii) the so-called delayed-QSSA methods are extensively presented. Specially, the numerical issues related to the setting of constant delays are discussed. Finally, for one slightly modified version of an enzyme-substrate reaction network (Michaelis-Menten kinetics), the comparison of the full non-reduced system behavior with respective variants of reduced model is presented and future prospects are proposed.
The state of charge (SoC) is the most commonly used performance indicator of battery used in various applications. A chronic erroneous estimation of battery SoC may result in constant over charging and discharging, wh...
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The state of charge (SoC) is the most commonly used performance indicator of battery used in various applications. A chronic erroneous estimation of battery SoC may result in constant over charging and discharging, which in turn causes permanent damage to the internal structure of the battery cells along with system disruptions. This paper presents a comprehensive review of different techniques for SoC estimation of batteries, followed by a review of Li-ion battery model parameter estimation methods. Then this paper classifies the Kalman filters (KFs) in a systematic manner and conducts a detailed literature review on the linear Kalman filter (LKF) and non-linear Kalman filters (NLKFs). In recent literature, the NLKFs such as extended Kalman filter (EKF), adaptive EKF (AEKF), unscented Kalman filter (UKF), and adaptive UKF (AUKF) are the most extensively established techniques for an accurate and reliable SoC estimation of batteries. However, the precise estimation of battery SoC using the Kalman filters largely relies on accurate battery modeling and its online model parameter estimation. According to the literature, the recursive least square (RLS) and the polynomial regression-based battery model (PRBM) are the most often used techniques for estimating real-time modelparameters of Li-ion batteries. Therefore, this paper performs an experimental comparative performance evaluation of the most popularly used NLKFS and battery modeling techniques in terms of SoC estimation accuracy at constant and varying operating conditions. The EKF, AEKF, UKF, and AUKF techniques augmented with the popularly used RLS or PRBM are first developed and tested with offline measured data in the MATLAB platform. Then they are implemented on the LabVIEW based battery testing platform using the Math-Script feature of MATLAB for real-time parameters and SoC estimation. Rigorous experimental studies have been carried out for comparative performance evaluation of the PRBM-EKF, PRBM-AEKF, PRB
The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and desi...
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The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power-voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg-Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss-Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant modelparameters quickly and with high accuracy, with a total sum of square error of 3.57x10(-7).
modelling and simulation of clutter are important in radar signal processing, the G0 distribution is generally adopted to simulate the ground clutter in radar echoes. In order to improve the modelling accuracy of clut...
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modelling and simulation of clutter are important in radar signal processing, the G0 distribution is generally adopted to simulate the ground clutter in radar echoes. In order to improve the modelling accuracy of clutter modelling, the actual data should be used for model parameter estimation. However, in some special situations, the actual data sample is very small. Existing methods cannot estimate the parameters of G0 distribution efficiently. To solve this problem, an improved recursive expectation-maximisation (EM) method is proposed to estimate the parameters of clutter in this article. This method combines the expectation step and maximisation step in one equation. Through recursive method and simplification of the positive definite matrix, this proposed method can obtain maximum likelihood estimation more efficiently than the conventional EM method and recursive EM method. Simulation results show that the performance of the proposed method is better than that of the conventional methods for a small data sample.
A set of more than 200 gas-permeability data is collected on centimeter-scale plugs drilled in a regular pattern along a core of Majorca limestone to test at the laboratory scale a recent model for the statistical cha...
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A set of more than 200 gas-permeability data is collected on centimeter-scale plugs drilled in a regular pattern along a core of Majorca limestone to test at the laboratory scale a recent model for the statistical characterization of spatial heterogeneity. Data are interpreted as samples of a generalized sub-Gaussian (GSG) random field. The latter results from the subordination of a spatially-correlated Gaussian field by a statistically-independent random field, which we take as log-normal in this application. The GSG model we analyze has been shown to be consistent with non-Gaussian and statistical scaling features that are frequently exhibited by several environmental variables, including hydrological properties. Key objectives of the current study are: (i) to provide a high-quality dataset from which one can assess statistical scaling features displayed by the data and their spatial increments;and (ii) to test the ability of the GSG model to characterize the observed behavior. We apply a statistical inference method to estimate the parameters of the GSG model on the basis of the available data. We then take advantage of formal model-identification criteria to consider the relative skills of diverse variogram models associated with the underlying Gaussian field to interpret the observed behavior of the dataset. Our results corroborate the effectiveness of the GSG modeling framework to characterize the documented aspects of statistical scaling. (C) 2017 Elsevier B.V. All rights reserved.
In this paper,an online parameterestimation method for unmanned surface vessels(USVs) is *** main idea is to establish an augmented system by viewing the parameters as system states,and then estimate the full states ...
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In this paper,an online parameterestimation method for unmanned surface vessels(USVs) is *** main idea is to establish an augmented system by viewing the parameters as system states,and then estimate the full states of the augmented system by using adaptive unscented Kalman filter(AUKF).Nine parameters including the inertial effects,the damping,the thrust allocation,and the current velocity can be online estimated accurately based on the measurements from real-time kinematic(RTK) Global Positioning System(GPS) and inertial measurement unit(IMU).The trajectory tracking control is further studied in the presence of input constraints,where the model predictive control(MPC) is *** simulation results of parameterestimation demonstrate the effectiveness of the proposed method.
The major challenge is to validate software failure dataset by finding unknown modelparameters used. For software assurance, previously many attempts were made based using classical classifiers as decision tree, Naiv...
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The major challenge is to validate software failure dataset by finding unknown modelparameters used. For software assurance, previously many attempts were made based using classical classifiers as decision tree, Naive Bayes, and k-nearest neighbor for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect-free modules. So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm proposed on particle swarm optimization and modified genetic algorithm for feature selection and bagging for effective classification of defective or nondefective modules in a dataset. This paper presents an empirical study on National Aeronautics and Space Administration Metric Data Program datasets, using the proposed hybrid algorithm and results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods.
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