Investigating the Significance of Dynamic Mode Decomposition for Fast and Accurate Parameter Estimation in Power Grids

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

A fast and high accurate initial values obtainment method for Brillouin scattering spectrum parameter estimation

This paper focuses on the methods used for estimating the parameters in Rasch Measurement Model (RMM). These include the MLE and Bayesian Estimation (BE) techniques. The accuracy and precision of the parameter estimates based on these two MLE and BE were discussed and compared. A questionnaire is a well-known measurement instrument used by most of the researchers. It is a powerful tool for collecting data in survey research. It should be noted that the quality of a measurement instrument used plays a key role in ensuring the quality of data obtained in the survey. Therefore, it has become essential for the researchers to carefully design their questionnaire so that the quality of the data obtained can be preserved. Then, it is also vital for the researchers to assess the quality of the data obtained before it can be successfully used for further analysis. Review of the literature shows that RMM is a psychometric approach widely used as an assessment tool of many measurement instruments developed in various fields of study. At present, the Maximum Likelihood Estimation (MLE) techniques were used to estimate the parameters in the RMM. In order to obtain more precise and accurate parameter estimates, a certain number of sample size and normality assumption are usually required. However, in a small sample, MLE could produce bias, imprecise and less accurate estimates with bigger standard error. A proper selection of the parameter estimation techniques to deal with small sample and non-normality of data is required to obtain more precise and accurate parameter estimates. From the review, it reveals that BE has successfully dealt with the issues of small sample and non-normality of the data. It produced a more accurate parameter estimate with smallest Mean Squared Error (MSE), particularly in a small sample compared to MLE.

This paper presents a global task coordinate frame (TCF) based integrated direct/indirect adaptive robust contouring controller (DIARC) for an industrial biaxial gantry that achieves not only excellent contouring performance but also accurate parameter estimations. Contouring control problem is first formulated in a recently proposed global task coordinate frame where the calculation of the contouring error is rather accurate and not affected by the curvature of the desired contour. A physical model based indirect type parameter estimation algorithm is then synthesized to obtain accurate on-line estimates of unknown physical model parameters. An integrated direct/indirect adaptive robust contouring controller with dynamic compensation type fast adaptation is also constructed to preserve the excellent transient and steady-state contouring performance of the direct adaptive robust control (DARC) designs. Comparative experimental results obtained on a high speed industrial biaxial precision gantry show that the proposed algorithm not only achieves the best contouring performance but also has accurate physical parameter estimations.

Integrated direct/indirect adaptive robust contouring control of a biaxial gantry with accurate parameter estimations

Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model

The estimation of time-invariant parameters of noisy nonlinear oscillatory systems

In this paper, the combined parameter and state estimation issues of state-space systems are considered, and the process noises and observation noises are supposed to be coloured noises. By utilising the data filtering technique, we transform the original state-space system into the filtered system for eliminating the interference of the coloured noise in the state equation, and then we derive a filtering-based extended stochastic gradient (F-ESG) algorithm to estimate the system parameters. For estimating the unmeasurable states, we derive a new state estimator by using the preceding parameter estimates to take the place of the unknown system parameters in the Kalman filter. Furthermore, we propose a filtering-based multi-innovation extended stochastic gradient (F-MI-ESG) algorithm to achieve the higher parameter estimation accuracy. Finally, we provide two simulation examples to test and compare the performance of the proposed algorithms. The simulation results indicate that the F-ESG algorithm and the F-MI-ESG algorithm are effective for parameter estimation, and that the F-MI-ESG algorithm is able to achieve more accurate parameter estimates than the F-ESG algorithm.

Blood pumps are becoming increasingly important for medical devices. They are used to assist and control the blood flow and blood pressure in the patient's body. To accurately control blood pumps, information about important hydrodynamic parameters such as blood flow rate, pressure difference and viscosity is needed. These parameters are difficult to measure online. Therefore, an accurate estimation of these parameters is crucial for the effective operation of implantable blood pumps. In this study, in vitro tests with bovine blood were conducted to collect data about the non-linear dependency of blood flow rate, flow resistance (pressure difference) and whole blood viscosity on motor current and rotation speed of a prototype blood pump. Gaussian process regression models are then used to model the non-linear mappings from motor current and rotation speed to the hydrodynamic variables of interest. The performance of the estimation is evaluated for all three variables and shows very high accuracy. For blood flow rate - correlation coefficient ( = 1, root mean squared error () = 0.31 ml min-1, maximal error () = 9.31 ml min-1; for pressure = 1, = 0.09 mmHg, = 8.34 mmHg; and for viscosity = 1, = 0.09 mPa.s, = 0.31 mPa⋅s. The current findings suggest that this method can be employed for highly accurate online estimation of essential hydrodynamic parameters for implantable blood pumps.

This article compares parameter estimates by 2-stage ML (TSML) and a recently developed 2-stage robust (TSR) method for structural equation modeling (SEM) with missing data. In the design, data are missing at random (MAR) after an auxiliary variable (AV) is included, and they are missing not at random (MNAR) otherwise. Results indicate that, when either the substantive variables or the AV is nonnormally distributed, TSR most likely yields more accurate parameter estimates than TSML; TSML is only slightly preferred to TSR when all variables are normally distributed. Including normally distributed AVs with TSML reduces the bias and improves the accuracy in parameter estimates. However, when the distribution of AVs has heavier tails than that of the normal distribution, including them with TSML could result in less accurate parameter estimates. When the sample size N is medium to large, including AVs with TSR most likely yields more accurate parameter estimates. When N is small, missing data rate is low, and when the AV is nonnromally distributed, TSML or even TSR could yield more accurate parameter estimates under MNAR mechanism than under MAR mechanism.

This study investigates the effects of outliers on the estimates of ARIMA model parameters with particular attention given to the performance of two outlier detection and modeling methods targeted at achieving more accurate estimates of the parameters. The two methods considered are: an iterative outlier detection aimed at obtaining the joint estimates of model parameters and outlier effects, and an iterative outlier detection with the effects of outliers removed to obtain an outlier free series, after which a successful ARIMA model is entertained. We explored the daily closing share price returns of Fidelity bank, Union bank of Nigeria, and Unity bank from 03/01/2006 to 24/11/2016, with each series consisting of 2690 observations from the Nigerian Stock Exchange. ARIMA (1, 1, 0) models were selected based on the minimum values of Akaike information criteria which fitted well to the outlier contaminated series of the respective banks. Our findings revealed that ARIMA (1, 1, 0) models which fitted adequately to the outlier free series outperformed those of the parameter-outlier effects joint- estimated model. Furthermore, we discovered that outliers biased the estimates of the model parameters by reducing the estimated values of the parameters. The implication is that, in order to achieve more accurate estimates of ARIMA model parameters, it is needful to account for the presence of significant outliers and preference should be given to the approach of cleaning the series of outliers before subsequent entertainment of adequate linear time series models.

Pulmonary hypertension (PH), i.e., high blood pressure in the lungs, is a serious medical condition that can damage the right ventricle of the heart and ultimately lead to heart failure. Standard diagnostic procedures are based on right-heart catheterization, which is an invasive technique that can potentially have serious side effects. Recent methodological advancements in fluid dynamics modelling of the pulmonary blood circulation system promise to mathematically predict the blood pressure based on non-invasive measurements of the blood flow. Thus, subsequent to PH diagnostication, further investigations would no longer require catheterization. However, in order for these alternative techniques to be applicable in the clinic, accurate model calibration and parameter estimation are paramount. Medical interventions taken to combat high blood pressure (as predicted from the mathematical model) alter the underlying cardiovascular physiology, thus interfering with the parameter estimation procedure. In the present study, we have carried out a series of cardiovascular simulations to assess the reliability of cardiovascular physiological parameter estimation in the presence of medical interventions. Our principal result is that if the closed-loop effect of medical interventions is accounted for, the model calibration provides accurate parameter estimates. This finding has important implications for the applicability of cardio-physiological modelling in the clinical practice.

In a general direct adaptive robust control (DARC) framework, the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in presence of uncertain nonlinearities and parametric uncertainties. Such a direct algorithm suffers from lack of modularity, controller-estimator inseparability, and poor convergence of parameter estimates. In the DARC design the parameters are estimated by gradient law with the sole purpose of reducing tracking error, which is typical of a Lyapunov-type design. However, when the controller-estimator module is expected to assist in secondary purposes such as health monitoring and fault detection, the requirement of having accurate online parameter estimates is as important as the need for the smaller tracking error. In this paper, we consider the trajectory tracking of a robotic manipulator driven by electro-hydraulic actuators. The controller is constructed based on the indirect adaptive robust control (IARC) framework with necessary design modifications required to accommodate uncertain and nonsmooth nonlinearities of the hydraulic system. The online parameter estimates are obtained through a parameter adaptation algorithm that is based on physical plant dynamics rather than the tracking error dynamics. While the new controller preserves the nice properties of the DARC design such as prescribed output tracking transient performance and final tracking accuracy, more accurate parameter estimates are obtained for prognosis and diagnosis purpose. Comparative experimental results are presented to show the effectiveness of the proposed algorithm.

This study presents a comparative analysis of various optimization algorithms—Differential Evolution (DE), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), and Hippopotamus Optimization Algorithm (HOA)—for parameter identification in photovoltaic (PV) models. By utilizing RTC France solar cell data, we demonstrate that accurate parameter estimation is crucial for enhancing the efficiency of PV systems, ultimately supporting sustainable energy solutions. Our results indicate that DE achieves the lowest root mean square error (RMSE) of 0.0001 for the double-diode model (DDM), outperforming other methods in terms of accuracy and convergence speed. Both the HOA and PSO also show competitive RMSE values, underscoring their effectiveness in optimizing parameters for PV models. This research not only contributes to improved PV model precision but also aids in the broader effort to advance renewable energy technologies, thereby fostering a more sustainable future.

Objective. Physiological parameter estimation is affected by intrinsic ambiguity in the data such as noise and model inaccuracies. The aim of this work is to provide a deep learning framework for accurate parameter and uncertainty estimates for DCE-MRI in the liver. Approach. Concentration time curves are simulated to train a Bayesian neural network (BNN). Training of the BNN involves minimization of a loss function that jointly minimizes the aleatoric and epistemic uncertainties. Uncertainty estimation is evaluated for different noise levels and for different out of distribution (OD) cases, i.e. where the data during inference differs strongly to the data during training. The accuracy of parameter estimates are compared to a nonlinear least squares (NLLS) fitting in numerical simulations and in vivo data of a patient suffering from hepatic tumor lesions. Main results. BNN achieved lower root-mean-squared-errors (RMSE) than the NLLS for the simulated data. RMSE of BNN was on overage of all noise levels lower by 33% ± 1.9% for k trans, 22% ± 6% for v e and 89% ± 5% for v p than the NLLS. The aleatoric uncertainties of the parameters increased with increasing noise level, whereas the epistemic uncertainty increased when a BNN was evaluated with OD data. For the in vivo data, more robust parameter estimations were obtained by the BNN than the NLLS fit. In addition, the differences between estimated parameters for healthy and tumor regions-of-interest were significant (p < 0.0001). Significance. The proposed framework allowed for accurate parameter estimates for quantitative DCE-MRI. In addition, the BNN provided uncertainty estimates which highlighted cases of high noise and in which the training data did not match the data during inference. This is important for clinical application because it would indicate cases in which the trained model is inadequate and additional training with an adapted training data set is required.