Simultaneous Determination of Skeletal Model Parameter Values, Motions, and Controls From Noisy Measurement Data

2008 ◽  
Author(s):  
Michael G. Fattey ◽  
Benjamin J. Fregly
Keyword(s):  
Inverse Dynamics ◽  
Optimal Parameter ◽  
Measurement Data ◽  
Biomechanical Model ◽  
Optimization Techniques ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Model Parameter ◽  
Multi Level ◽  
Cadaver Studies

Accurate model parameter value and motion determination is important for obtaining reliable results from inverse dynamics analyses of gait. If the model parameters do not properly match their true values, the predicted motions and loads may lose their clinical significance [1]. Typical approaches to biomechanical model parameter estimation have included the use of scaling rules based on cadaver studies [2] and the use of multi-level optimization routines [3,4]. However, scaling rules do not provide optimal parameter estimates, and multi-level optimization techniques are computationally expensive.

Optimized blood sampling protocols and sequential design of kinetic experiments

1981 ◽  
Vol 240 (5) ◽  
pp. R259-R265 ◽  
Author(s):  
J. J. DiStefano
Keyword(s):  
Endocrine System ◽  
Primary Objective ◽  
Blood Sampling ◽  
Optimal Designs ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Reverse T3 ◽  
Model Parameter ◽  
Maximum Accuracy ◽  
Sampling Protocols

Design of optimal blood sampling protocols for kinetic experiments is discussed and evaluated, with the aid of several examples--including an endocrine system case study. The criterion of optimality is maximum accuracy of kinetic model parameter estimates. A simple example illustrates why a sequential experiment approach is required; optimal designs depend on the true model parameter values, knowledge of which is usually a primary objective of the experiment, as well as the structure of the model and the measurement error (e.g., assay) variance. The methodology is evaluated from the results of a series of experiments designed to quantify the dynamics of distribution and metabolism of three iodothyronines, T3, T4, and reverse-T3. This analysis indicates that 1) the sequential optimal experiment approach can be effective and efficient in the laboratory, 2) it works in the presence of reasonably controlled biological variation, producing sufficiently robust sampling protocols, and 3) optimal designs can be highly efficient designs in practice, requiring for maximum accuracy a number of blood samples equal to the number of independently adjustable model parameters, no more or less.

Can high-frequency data enable better parameterization of water quality models and disentangling of DO processes?

2021 ◽  
Author(s):  
Jingshui Huang ◽  
Pablo Merchan-Rivera ◽  
Gabriele Chiogna ◽  
Markus Disse ◽  
Michael Rode
Keyword(s):  
Water Quality ◽  
High Frequency ◽  
Critical Role ◽  
Measurement Data ◽  
High Frequency Data ◽  
Model Parameters ◽  
Frequency Data ◽  
Quality Models ◽  
Model Parameter ◽  
Water Quality Models

<p>Water quality models offer to study dissolved oxygen (DO) dynamics and resulting DO balances. However, the infrequent temporal resolution of measurement data commonly limits the reliability of disentangling and quantifying instream DO process fluxes using models. These limitations of the temporal data resolution can result in the equifinality of model parameter sets. In this study, we aim to quantify the effect of the combination of emerging high-frequency monitoring techniques and water quality modelling for 1) improving the estimation of the model parameters and 2) reducing the forward uncertainty of the continuous quantification of instream DO balance pathways.</p><p>To this end, synthetic measurements for calibration with a given series of frequencies are used to estimate the model parameters of a conceptual water quality model of an agricultural river in Germany. The frequencies vary from the 15-min interval, daily, weekly, to monthly. A Bayesian inference approach using the DREAM algorithm is adopted to perform the uncertainty analysis of DO simulation. Furthermore, the propagated uncertainties in daily fluxes of different DO processes, including reaeration, phytoplankton metabolism, benthic algae metabolism, nitrification, and organic matter deoxygenation, are quantified.</p><p>We hypothesize that the uncertainty will be larger when the measurement frequency of calibrated data was limited. We also expect that the high-frequency measurements significantly reduce the uncertainty of flux estimations of different DO balance components. This study highlights the critical role of high-frequency data supporting model parameter estimation and its significant value in disentangling DO processes.</p>

scholarly journals Model parameter identification from measurement data for dynamic torque calibration – Measurement results and validation

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (3) ◽  
pp. 55 ◽  
Author(s):  
Leonard Klaus
Keyword(s):  
Parameter Identification ◽  
Transfer Functions ◽  
Dynamic Properties ◽  
Measurement Data ◽  
Model Parameters ◽  
Dynamic Calibration ◽  
Measuring Device ◽  
Model Parameter ◽  
Calibration Measurement ◽  
Model Parameter Identification

<p><span lang="EN-US">The dynamic calibration of torque transducers requires the </span><span lang="EN-GB">modelling</span><span lang="EN-US"> of the measuring device and of the transducer under test. The transducer's dynamic properties are described by means of model parameters, which are going to be identified from measurement data. To be able to do so, two transfer functions are calculated. In this paper, the transfer functions and the procedure for the model parameter identification are presented. Results of a parameter identification of a torque transducer are also given, and the validity of the identified parameters is </span><span lang="EN-GB">analysed</span><span lang="EN-US"> by comparing the results with independent measurements. The successful parameter identification is a prerequisite for a model-based dynamic calibration of torque transducers.</span></p>

scholarly journals Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 509 ◽  
Author(s):  
Xiangzhong Xie ◽  
René Schenkendorf
Keyword(s):  
Process Design ◽  
Measurement Data ◽  
Pharmaceutical Manufacturing ◽  
Chamber Pressure ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Model Based ◽  
Point Estimate Method ◽  
Robust Process

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.

scholarly journals An application of the ensemble Kalman filter in epidemiological modelling

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256227
Author(s):  
Rajnesh Lal ◽  
Weidong Huang ◽  
Zhenquan Li
Keyword(s):  
Parameter Estimation ◽  
Model Simulation ◽  
Synthetic Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Model Parameter ◽  
Reliable Model ◽  
Reported Data ◽  
Parameter Estimation Methods

Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses’ dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation.

A normalized regression based regional model for generating flows at ungauged basins

2013 ◽  
Vol 68 (1) ◽  
pp. 99-108
Author(s):  
Borislava Blagojević ◽  
Jasna Plavšić
Keyword(s):  
Input Data ◽  
Absolute Error ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Efficiency Coefficient ◽  
Ungauged Basins ◽  
Model Parameter ◽  
Proposed Model ◽  
Two Parameter

Revision of existing methodologies for generating monthly-flow series at ungauged basins based on multivariate nonlinear correlation has led to a simple two-parameter model. While the existing methodology used hydrological, meteorological and geomorphologic input data, the proposed model requires hydrological and geomorphologic input data only. The proposed methodology requires formation of separate pools of donor catchments for model parameter estimates. The proposed two-parameter model and improvement in the sphere of homogeneous region identification were verified using 195 runoff data sets from hydrologic stations in Serbia in the 1961–2005 period, divided into three non-overlapping 15-year periods. Nash-Sutcliffe's model efficiency coefficient (NSE) was used to assess the: (1) quality of proposed model with identified model parameters; (2) quality of a nonlinear multivariate equation for standard normal variables estimation with identified model parameters; (3) quality of proposed model with model parameter estimates. Generated time-series statistics and nonlinear multivariate equation quality are also evaluated. Five model calibration and validation results are shown. Generated flow series variation coefficient is the best replicated statistics with relative absolute error less than 10%.

A DYNAMIC-STOCHASTIC PRINCIPLE OF MATHEMATICAL SIMULATION

2021 ◽  
Author(s):  
Yu. A. Gusman ◽  
◽  
Yu. A. Pichugin ◽  
Keyword(s):  
Probability Distribution ◽  
Mathematical Simulation ◽  
Predictive Models ◽  
Stochastic Approach ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Mathematical Apparatus ◽  
Model Parameter ◽  
Stochastic Nature ◽  
Dynamic Stochastic

The paper considers the dynamic-stochastic approach to the construction and use of predictive models, which is based on the stochastic nature of model parameter estimates. A mathematical apparatus for generating perturbations of model parameters in accordance with their probability distribution is proposed.

scholarly journals Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1265 ◽  
Author(s):  
Johanna Geis-Schroer ◽  
Sebastian Hubschneider ◽  
Lukas Held ◽  
Frederik Gielnik ◽  
Michael Armbruster ◽  
...  
Keyword(s):  
Low Voltage ◽  
Measurement Data ◽  
Analytical Calculation ◽  
Laboratory Measurement ◽  
Model Parameters ◽  
Calculation Methods ◽  
Modeling Approach ◽  
Measurement Results ◽  
Simulation Results ◽  
Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, &gt;0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

Sign in / Sign up

Export Citation Format

Share Document