scholarly journals Simplified Building Thermal Model Development and Parameters Evaluation Using a Stochastic Approach

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2899
Author(s):  
Abhinandana Boodi ◽  
Karim Beddiar ◽  
Yassine Amirat ◽  
Mohamed Benbouzid
Keyword(s):  
Reference Model ◽  
Model Development ◽  
Network Models ◽  
Real Data ◽  
Stochastic Approach ◽  
Simplified Model ◽  
Model Parameters ◽  
Order Model ◽  
Parameter Values ◽  
Low Order

This paper proposes an approach to develop building dynamic thermal models that are of paramount importance for controller application. In this context, controller requires a low-order, computationally efficient, and accurate models to achieve higher performance. An efficient building model is developed by having proper structural knowledge of low-order model and identifying its parameter values. Simplified low-order systems can be developed using thermal network models using thermal resistances and capacitances. In order to determine the low-order model parameter values, a specific approach is proposed using a stochastic particle swarm optimization. This method provides a significant approximation of the parameters when compared to the reference model whilst allowing low-order model to achieve 40% to 50% computational efficiency than the reference one. Additionally, extensive simulations are carried to evaluate the proposed simplified model with solar radiation and identified model parameters. The developed simplified model is afterward validated with real data from a case study building where the achieved results clearly show a high degree of accuracy compared to the actual data.

Variational Bayes Inference for the DINA Model

2020 ◽  
Vol 45 (5) ◽  
pp. 569-597
Author(s):  
Kazuhiro Yamaguchi ◽  
Kensuke Okada
Keyword(s):  
Real Data ◽  
Variational Bayes ◽  
Diagnostic Assessment ◽  
Model Parameters ◽  
Mcmc Method ◽  
Inference Method ◽  
Bayes Inference ◽  
Dina Model ◽  
Mcmc Estimation ◽  
Parameter Values

In this article, we propose a variational Bayes (VB) inference method for the deterministic input noisy AND gate model of cognitive diagnostic assessment. The proposed method, which applies the iterative algorithm for optimization, is derived based on the optimal variational posteriors of the model parameters. The proposed VB inference enables much faster computation than the existing Markov chain Monte Carlo (MCMC) method, while still offering the benefits of a full Bayesian framework. A simulation study revealed that the proposed VB estimation adequately recovered the parameter values. Moreover, an example using real data revealed that the proposed VB inference method provided similar estimates to MCMC estimation with much faster computation.

Fast 2-D ray+Born migration/inversion in complex media

Geophysics ◽  
1999 ◽  
Vol 64 (1) ◽  
pp. 162-181 ◽  
Author(s):  
Philippe Thierry ◽  
Stéphane Operto ◽  
Gilles Lambaré
Keyword(s):  
Numerical Approximation ◽  
Reference Model ◽  
Real Data ◽  
Model Parameters ◽  
Complex Media ◽  
Inversion Algorithm ◽  
Numerical Approximations ◽  
Data Set ◽  
Cpu Time ◽  
Computational Evaluation

In this paper, we evaluate the capacity of a fast 2-D ray+Born migration/inversion algorithm to recover the true amplitude of the model parameters in 2-D complex media. The method is based on a quasi‐Newtonian linearized inversion of the scattered wavefield. Asymptotic Green’s functions are computed in a smooth reference model with a dynamic ray tracing based on the wavefront construction method. The model is described by velocity perturbations associated with diffractor points. Both the first traveltime and the strongest arrivals can be inverted. The algorithm is implemented with several numerical approximations such as interpolations and aperture limitation around common midpoints to speed the algorithm. Both theoritical and numerical aspects of the algorithm are assessed with three synthetic and real data examples including the 2-D Marmousi example. Comparison between logs extracted from the exact Marmousi perturbation model and the computed images shows that the amplitude of the velocity perturbations are recovered accurately in the regions of the model where the ray field is single valued. In the presence of caustics, neither the first traveltime nor the most energetic arrival inversion allow for a full recovery of the amplitudes although the latter improves the results. We conclude that all the arrivals associated with multipathing through transmission caustics must be taken into account if the true amplitude of the perturbations is to be found. Only 22 minutes of CPU time is required to migrate the full 2-D Marmousi data set on a Sun SPARC 20 workstation. The amplitude loss induced by the numerical approximations on the first traveltime and the most energetic migrated images are evaluated quantitatively and do not exceed 8% of the energy of the image computed without numerical approximation. Computational evaluation shows that extension to a 3-D ray+Born migration/inversion algorithm is realistic.

scholarly journals A simple parametric representation of the Hodgkin-Huxley model

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254152
Author(s):  
Alejandro Rodríguez-Collado ◽  
Cristina Rueda
Keyword(s):  
Simulation Experiment ◽  
Reference Model ◽  
Real Data ◽  
Parametric Representation ◽  
Model Parameters ◽  
Cell Type ◽  
Machine Learning Methods ◽  
Primary Signal ◽  
Type Classification ◽  
Multicomponent Model

The Hodgkin-Huxley model, decades after its first presentation, is still a reference model in neuroscience as it has successfully reproduced the electrophysiological activity of many organisms. The primary signal in the model represents the membrane potential of a neuron. A simple representation of this signal is presented in this paper. The new proposal is an adapted Frequency Modulated Möbius multicomponent model defined as a signal plus error model in which the signal is decomposed as a sum of waves. The main strengths of the method are the simple parametric formulation, the interpretability and flexibility of the parameters that describe and discriminate the waveforms, the estimators’ identifiability and accuracy, and the robustness against noise. The approach is validated with a broad simulation experiment of Hodgkin-Huxley signals and real data from squid giant axons. Interesting differences between simulated and real data emerge from the comparison of the parameter configurations. Furthermore, the potential of the FMM parameters to predict Hodgkin-Huxley model parameters is shown using different Machine Learning methods. Finally, promising contributions of the approach in Spike Sorting and cell-type classification are detailed.

An approach to computer analysis of the ligand binding assay data on example of radioligand assay data

2020 ◽  
Vol 18 (02) ◽  
pp. 2050014
Author(s):  
S. N. Fedotov
Keyword(s):  
Least Squares Method ◽  
Main Idea ◽  
Simulated Data ◽  
Real Data ◽  
Model Parameters ◽  
Nonlinear Method ◽  
Ligand Interaction ◽  
Receptor Ligand ◽  
Parameter Values ◽  
True Values

As a rule, receptor-ligand assay data are fitted by logistic functions (4PL model, 5PL model, Feldman’s model). The preparation of the initial estimates for parameters of these functions is an important problem for processing receptor-ligand interaction data. This study represents a new mathematical approach to calculate the initial estimates more closely to the true values of parameters. The main idea of this approach is in using the modified linear least squares method for calculations of the parameters for the 4PL model and the Feldman’s model. In this study, the convergence of model parameters to true values is verified for the simulated data with different statistical scatter. Also, the results of processing real data for the 4PL model and the Feldman’s model are presented. A comparison is made of the parameter values calculated by the presented and a nonlinear method. The developed approach has demonstrated its efficiency in calculating the parameters of the complex Feldman”s models up to 4 ligands and 4 sites.

scholarly journals A case study of 2019-nCOV cases in Argentina with the real data based on daily cases from March 03, 2020 to March 29, 2021 using classical and fractional derivatives

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Pushpendra Kumar ◽  
Vedat Suat Erturk ◽  
Marina Murillo-Arcila ◽  
Ramashis Banerjee ◽  
A. Manickam
Keyword(s):  
Fractional Order ◽  
Trust Region ◽  
Simulated Data ◽  
Real Data ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Order Model ◽  
The Real ◽  
The Stability ◽  
Parameter Values

AbstractIn this study, our aim is to explore the dynamics of COVID-19 or 2019-nCOV in Argentina considering the parameter values based on the real data of this virus from March 03, 2020 to March 29, 2021 which is a data range of more than one complete year. We propose a Atangana–Baleanu type fractional-order model and simulate it by using predictor–corrector (P-C) method. First we introduce the biological nature of this virus in theoretical way and then formulate a mathematical model to define its dynamics. We use a well-known effective optimization scheme based on the renowned trust-region-reflective (TRR) method to perform the model calibration. We have plotted the real cases of COVID-19 and compared our integer-order model with the simulated data along with the calculation of basic reproductive number. Concerning fractional-order simulations, first we prove the existence and uniqueness of solution and then write the solution along with the stability of the given P-C method. A number of graphs at various fractional-order values are simulated to predict the future dynamics of the virus in Argentina which is the main contribution of this paper.

scholarly journals Stochastic Approach

1980 ◽  
Vol 51 ◽  
pp. 183-194
Author(s):  
H.-P. Gail
Keyword(s):  
Markov Processes ◽  
Radiation Transfer ◽  
Stochastic Approach ◽  
Degree Of Approximation ◽  
Velocity Fields ◽  
Order Model ◽  
Preliminary Results ◽  
Model Equations ◽  
Arbitrary Velocity ◽  
Low Order

A general formalism for describing the radiation transfer in a medium with arbitrary velocity fields is presented. It is demonstrated that classical microturbulence and mesoturbulent models based on Markov processes can be considered as the two lowest order members within a hierarchy of model equations with an increasing degree of approximation to reality. Some preliminary results concerning the relevance of low order model equations are presented.

Low Order Model Dynamics of the Forced Cylinder Wake

2009 ◽  
Author(s):  
N. W. Mureithi ◽  
K. Huynh ◽  
A. Pham
Keyword(s):  
Mode Locking ◽  
Model Parameters ◽  
Cylinder Wake ◽  
Flow State ◽  
Order Model ◽  
2D Simulation ◽  
Equivariant Bifurcation Theory ◽  
Preliminary Experimental Data ◽  
Pod Analysis ◽  
Low Order

The periodically forced cylinder wake exhibits complex but highly symmetrical patterns. In recent work, the authors have exploited symmetry-group equivariant bifurcation theory to derive low order equations describing, approximately, the dominant nonlinear dynamics of wake mode interactions. The models have been shown to qualitatively predict the observed bifurcations suggesting that the Karman wake remains, dynamically, a fairly simple system at least when viewed in 2D. Preliminary experimental data are presented supporting the feasibility of using 2D simulation results for the derivation of the low order model parameters. A POD analysis of the wake PIV velocity field yields flow modes closely similarly to those obtained via 2D CFD computations for Re in the 1000 range. The paper presents new results of simulations for Re = 200. For this low Reynolds number, the forced Karman wake exhibits rich dynamics dominated by quasi-periodicity, mode locking, torus doubling and chaos. The low Re torus breakdown may be explained by the Afraimovich-Shilnikov theorem. Interestingly, in a previous analysis for the higher Re number, Re = 1000, transition to a period-doubled flow state was found to occur via a route akin to the Takens-Bogdanov bifurcation scenario.

Generalized Flexible Weibull Extension Distribution

2017 ◽  
Vol 2 (4) ◽  
pp. 68-75
Author(s):  
Zubair Ahmad ◽  
Brikhna Iqbal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Data Sets ◽  
Suggested Model ◽  
Proposed Model ◽  
Parameter Values ◽  
Family Of Distributions

In this article, a four parameter generalization of the flexible Weibull extension distribution so-called generalized flexible Weibull extension distribution is studied. The proposed model belongs to T-X family of distributions proposed by Alzaatreh et al. [5]. The suggested model is much flexible and accommodates increasing, unimodal and modified unimodal failure rates. A comprehensive expression of the numerical properties and the estimates of the model parameters are obtained using maximum likelihood method. By appropriate choice of parameter values the new model reduces to four sub models. The proposed model is illustrated by means of three real data sets.

Hybrid Reduced Model of Rotor

2013 ◽  
Vol 60 (3) ◽  
pp. 319-333
Author(s):  
Rafał Hein ◽  
Cezary Orlikowski
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Hybrid Model ◽  
Rotor System ◽  
Reduced Model ◽  
Order Model ◽  
Gyroscopic Effect ◽  
Reduced Models ◽  
Rigid Finite Element Method ◽  
Low Order

Abstract In the paper, the authors describe the method of reduction of a model of rotor system. The proposed approach makes it possible to obtain a low order model including e.g. non-proportional damping or the gyroscopic effect. This method is illustrated using an example of a rotor system. First, a model of the system is built without gyroscopic and damping effects by using the rigid finite element method. Next, this model is reduced. Finally, two identical, low order, reduced models in two perpendicular planes are coupled together by means of gyroscopic and damping interaction to form one model of the system. Thus a hybrid model is obtained. The advantage of the presented method is that the number of gyroscopic and damping interactions does not affect the model range

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