scholarly journals Parameter estimation using the falling head infiltration model: Simulation and field experiment

2005 ◽  
Vol 41 (2) ◽  
Author(s):  
Takele B. Zeleke ◽  
Bing C. Si
Keyword(s):  
Parameter Estimation ◽  
Field Experiment ◽  
Model Simulation ◽  
Infiltration Model

scholarly journals Microrisk Lab: an online freeware for predictive microbiology

2020 ◽  
Author(s):  
Yangtai Liu ◽  
Xiang Wang ◽  
Baolin Liu ◽  
Qingli Dong
Keyword(s):  
Parameter Estimation ◽  
Bacterial Growth ◽  
Model Simulation ◽  
Parameter Determination ◽  
Predictive Microbiology ◽  
Isothermal Conditions ◽  
R Programming Language ◽  
Behavior Simulation ◽  
R Programming ◽  
And Behavior

AbstractMicrorisk Lab was designed as an interactive modeling freeware to realize parameter estimation and model simulation in predictive microbiology. This tool was developed based on the R programming language and ‘Shinyapps.io’ server, and designed as a fully responsive interface to the internet-connected devices. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/ inactivation under static and non-isothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/ inactivation under static and non-isothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate dataset and/ or parameter setting. In this research, six case studies were reproduced in Microrisk Lab and compared in parallel to DMFit, GInaFiT, IPMP 2013/ GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling system in most cases. Microrisk Lab allowed for uniform user experience to implement microbial predictive modeling by its friendly interfaces, high-integration, and interconnectivity. It might become a useful tool for the microbial parameter determination and behavior simulation. Non-commercial users could freely access this application at https://microrisklab.shinyapps.io/english/.

scholarly journals SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology

Computation ◽  
2014 ◽  
Vol 2 (4) ◽  
pp. 246-257 ◽  
Author(s):  
Alexander Dörr ◽  
Roland Keller ◽  
Andreas Zell ◽  
Andreas Dräger
Keyword(s):  
Parameter Estimation ◽  
Systems Biology ◽  
Model Simulation

Dynamic modeling and parameter estimation for traction, rolling, and lateral wheel forces to enhance mobile robot trajectory tracking

Robotica ◽  
2014 ◽  
Vol 33 (10) ◽  
pp. 2204-2220 ◽  
Author(s):  
Gokhan Bayar ◽  
A. Bugra Koku ◽  
E. Ilhan Konukseven
Keyword(s):  
Parameter Estimation ◽  
Mobile Robot ◽  
Trajectory Tracking ◽  
Model Simulation ◽  
Ground Truth ◽  
Realistic Model ◽  
Least Square ◽  
Test Surface ◽  
Trajectory Tracking Control ◽  
Camera System

SUMMARYStudying wheel and ground interaction during motion has the potential to increase the performance of localization, navigation, and trajectory tracking control of a mobile robot. In this paper, a differential mobile robot is modeled in a way that (traction, rolling, and lateral) wheel forces are included in the overall system dynamics. Lateral wheel forces are included in the mathematical model together with traction and rolling forces. A least square parameter estimation process is proposed to estimate the parameters of the wheel forces. In order to implement the proposed methodologies, an experimental setup is used. The setup contains a differentially driven mobile robot, a specially constructed test surface, and a camera system attached at the top of surface for obtaining ground truth. Models having one or more wheel forces are simulated to find the most realistic model. Simulation results are verified by experiments.

scholarly journals SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks

2017 ◽  
Author(s):  
Piero Dalle Pezze ◽  
Nicolas Le Novère
Keyword(s):  
Parameter Estimation ◽  
Model Building ◽  
Model Simulation ◽  
Profile Likelihood ◽  
Model Development ◽  
Software Tool ◽  
Model Parameters ◽  
Repeat Sequences ◽  
Parameter Estimations ◽  
Consistent Manner

AbstractBackground: The rapid growth of the number of mathematical models in Systems Biology fostered the development of many tools to simulate and analyse them. The reliability and precision of these tasks often depend on multiple repetitions and they can be optimised if executed as pipelines. In addition, new formal analyses can be performed on these repeat sequences, revealing important insights about the accuracy of model predictions.Results: Here we introduce SBpipe, an open source software tool for automating repetitive tasks in model building and simulation. Using basic configuration files, SBpipe builds a sequence of repeated model simulations or parameter estimations, performs analyses from this generated sequence, and finally generates a LaTeX/PDF report. The parameter estimation pipeline offers analyses of parameter profile likelihood and parameter correlation using samples from the computed estimates. Specific pipelines for scanning of one or two model parameters at the same time are also provided. Pipelines can run on multicore computers, Sun Grid Engine (SGE), or Load Sharing Facility (LSF) clusters, speeding up the processes of model building and simulation. SBpipe can execute models implemented in Copasi, Python or coded in any other programming language using Python as a wrapper module. Future support for other software simulators can be dynamically added without affecting the current implementation.Conclusions: SBpipe allows users to automatically repeat the tasks of model simulation and parameter estimation, and extract robustness information from these repeat sequences in a solid and consistent manner, facilitating model development and analysis. The source code and documentation of this project are freely available at the web site: https://pdp10.github.io/sbpipe/.

scholarly journals CALIBRATION OF WATER‐QUALITY MODEL “WODA” – CASE STUDY OF THE WARTA RIVER

2007 ◽  
Vol 15 (2) ◽  
pp. 93-98 ◽  
Author(s):  
Marek Sowinski ◽  
Anna Neugebauer
Keyword(s):  
Parameter Estimation ◽  
Data Model ◽  
Model Simulation ◽  
Least Square ◽  
Automatic Calibration ◽  
Quality Model ◽  
Concentration Data ◽  
Measured Concentration ◽  
Proposed Model

The main feature of the proposed model implemented by a computer package WODA, that distinguishes it from other commonly used models like QUALE 2E or WASP5, is a possibility of its automatic calibration i e parameter estimation taking into account simultaneously several sets of measured concentration data. Model WODA, developed by A. Kraszewski and R. Soncini‐Sessa, enables fitting simulated values to measured concentrations of BOD and DO based on the least‐square criterion. This model was applied for parameter estimation of the Warta River in Poland. Measured concentration data used for parameter estimation were obtained from monthly monitoring. The results are presented in the form of BOD and DO lines against measured concentrations along the analysed stretch of the Warta River. Adaptation of the model simulation results to measured data is described by quantifying characteristics. They indicate relatively good adjustment. The reasons of some differences are discussed and explained.

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