scholarly journals Measurement Based Validation of an Electro-Pneumatic Gearbox Actuator

2019 ◽  
Vol 49 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Ádám Szabó ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Dynamic Behavior ◽  
Nonlinear Model ◽  
Real System ◽  
Pneumatic Actuator ◽  
Model Parameters ◽  
The Real ◽  
Friction Parameters

The objective of the research is to analyze the behavior of the developed electro-pneumatic actuator model and compare it to the behavior of the real system. The actuator achieves the requested gear changes by moving the two pistons inside the cylinder and it is operated by three-way two-position solenoid valves. Since not all model parameters are exactly known, such as contraction coefficients and friction parameters, they can be estimated based on literature then they can be further tuned to minimize the error of the simulation. The developed nonlinear model is capable of describing the dynamic behavior of the gearbox actuator, thus it can be used to analyze the effects of constructional modifications and it can serve as Model in the Loop (MIL) environment for controller testing.

Dynamic Characterization of a Highly Flexible Reticular Structure

1996 ◽  
Vol 2 (4) ◽  
pp. 431-446
Author(s):  
Francesco Petrone ◽  
Rosario Sinatra ◽  
Giuseppe Tedoldi
Keyword(s):  
Dynamic Behavior ◽  
Real System ◽  
Experimental Methodology ◽  
Dynamic Characterization ◽  
Unusual Behavior ◽  
The Real ◽  
Acceptable Accuracy ◽  
Reticular Structure

Structural analysis in the dynamic field appears a particularly valid methodology in evaluating the behavior of the constructional solutions to particularly complex systems. The experimental determination of a structure's dynamic response can allow the development of a mathematical model that is able to simulate, with acceptable accuracy, the dynamic behavior of the real system. The model, which characterizes the structure, can then be used to interpret variations in the dynamic behavior subsequently detected in the real system, or to simulate unusual behavior when the corresponding input parameters can be defined. The present paper gives an example of this experimental methodology, analyzing the dynamic behavior of the cantilever roof of Favorita Stadium in Palermo.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

scholarly journals A Tool for the Analysis and Characterization of School Mathematical Models

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1569
Author(s):  
Jesús Montejo-Gámez ◽  
Elvira Fernández-Ahumada ◽  
Natividad Adamuz-Povedano
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Educational Research ◽  
Analysis Procedure ◽  
Real System ◽  
The Real ◽  
School Model ◽  
Corresponding Analysis ◽  
Three Components

This paper shows a tool for the analysis of written productions that allows for the characterization of the mathematical models that students develop when solving modeling tasks. For this purpose, different conceptualizations of mathematical models in education are discussed, paying special attention to the evidence that characterizes a school model. The discussion leads to the consideration of three components, which constitute the main categories of the proposed tool: the real system to be modeled, its mathematization and the representations used to express both. These categories and the corresponding analysis procedure are explained and illustrated through two working examples, which expose the value of the tool in establishing the foci of analysis when investigating school models, and thus, suggest modeling skills. The connection of this tool with other approaches to educational research on mathematical modeling is also discussed.

PSV-14 Using Bayesian Hamiltonian Monte Carlo and a nonlinear model for the estimation of genetic parameters for lactation curves in goats

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 305-307
Author(s):  
Andre C Araujo ◽  
Leonardo Gloria ◽  
Paulo Abreu ◽  
Fabyano Silva ◽  
Marcelo Rodrigues ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Milk Yield ◽  
Nonlinear Model ◽  
Genetic Parameters ◽  
Model Parameters ◽  
Promising Alternative ◽  
Hamiltonian Monte Carlo ◽  
Heritability Estimates ◽  
Correlated Parameters ◽  
Wood Model

Abstract Hamiltonian Monte Carlo (HMC) is an algorithm of the Markov Chain Monte Carlo (MCMC) method that uses dynamics to propose samples that follow a target distribution. This algorithm enables more effective and consistent exploration of the probability interval and is more sensitive to correlated parameters. Therefore, Bayesian-HMC is a promising alternative to estimate individual parameters of complex functions such as nonlinear models, especially when using small datasets. Our objective was to estimate genetic parameters for milk traits defined based on nonlinear model parameters predicted using the Bayesian-HMC algorithm. A total of 64,680 milk yield test-day records from 2,624 first, second, and third lactations of Saanen and Alpine goats were used. First, the Wood model was fitted to the data. Second, lactation persistency (LP), peak time (PT), peak yield (PY), and total milk yield [estimated from zero to 50 (TMY50), 100(TMY100), 150(TMY150), 200(TMY200), 250(TMY250), and 300(TMY300) days-in-milk] were predicted for each animal and parity based on the output of the first step (the individual phenotypic parameters of the Wood model). Thereafter, these predicted phenotypes were used for estimating genetic parameters for each trait. In general, the heritability estimates across lactations ranged from 0.10 to 0.20 for LP, 0.04 to 0.07 for PT, 0.26 to 0.27 for PY, and 0.21 to 0.28 for TMY (considering the different intervals). Lower heritabilities were obtained for the nonlinear function parameters (A, b and l) compared to its predicted traits (except PT), especially for the first and second lactations (range: 0.09 to 0.18). Higher heritability estimates were obtained for the third lactation traits. To our best knowledge, this study is the first attempt to use the HMC algorithm to fit a nonlinear model in animal breeding. The two-step method proposed here allowed us to estimate genetic parameters for all traits evaluated.

scholarly journals Learning Personalized Itemset Mapping for Cross-Domain Recommendation

2020 ◽  
Author(s):  
Yinan Zhang ◽  
Yong Liu ◽  
Peng Han ◽  
Chunyan Miao ◽  
Lizhen Cui ◽  
...  
Keyword(s):  
Single Domain ◽  
Model Parameters ◽  
The Real ◽  
Cross Domain ◽  
Proposed Model ◽  
Latent Space ◽  
Learning Parameter ◽  
Generation Procedure ◽  
Over Time

Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.

scholarly journals Use of Statistical Models for Simulating Transactions on the Real Estate Market

2015 ◽  
Vol 23 (2) ◽  
pp. 102-111 ◽  
Author(s):  
Radosław Cellmer ◽  
Katarzyna Szczepankowska
Keyword(s):  
Probability Distribution ◽  
Real Estate ◽  
Statistical Models ◽  
Real Estate Market ◽  
Model Parameters ◽  
The Real ◽  
The Real Estate ◽  
Transaction Prices ◽  
The Real Estate Market ◽  
Real Estate Prices

Abstract The regularities and relations between real estate prices and the factors that shape them may be presented in the form of statistical models, thanks to which the diagnosis and prediction of prices is possible. A formal description of empirical observation presented in the form of regressive models also offers a possibility for creating certain phenomena in a virtual dimension. Market phenomena cannot be fully described with the use of determinist models, which clarify only a part of price variation. The predicted price is, in this situation, a special case of implementing a random function. Assuming that other implementations are also possible, regressive models may constitute a basis for simulation, which results in the procurement of a future image of the market. Simulation may refer both to real estate prices and transaction prices. The basis for price simulation may be familiarity with the structure of the analyzed market data. Assuming that this structure has a static character, simulation of real estate prices is performed on the basis of familiarity with the probability distribution and a generator of random numbers. The basis for price simulation is familiarity with model parameters and probability distribution of the random factor. The study presents the core and theoretical description of a transaction simulation on the real estate market, as well as the results of an experiment regarding transaction prices of office real estate located within the area of the city of Olsztyn. The result of the study is a collection of virtual real properties with known features and simulated prices, constituting a reflection of market processes which may take place in the near future. Comparison between the simulated characteristic and actual transactions in turn allows the correctness of the description of reality by the model to be verified.

scholarly journals Nonlinear Model and Dynamic Behavior of Photovoltaic Grid-Connected Inverter

2020 ◽  
Vol 10 (6) ◽  
pp. 2120 ◽  
Author(s):  
Zhi-Xian Liao ◽  
Dan Luo ◽  
Xiao-Shu Luo ◽  
Hai-Sheng Li ◽  
Qin-Qin Xiang ◽  
...  
Keyword(s):  
Bifurcation Diagram ◽  
Dynamic Behavior ◽  
Nonlinear Model ◽  
Nonlinear Dynamic ◽  
Optimization Design ◽  
Stable Operation ◽  
Control Parameters ◽  
Nonlinear Dynamic Behavior ◽  
Predictive Controller ◽  
Grid Connected Inverter

A photovoltaic grid-connected inverter is a strongly nonlinear system. A model predictive control method can improve control accuracy and dynamic performance. Methods to accurately model and optimize control parameters are key to ensuring the stable operation of a photovoltaic grid-connected inverter. Based on the nonlinear characteristics of photovoltaic arrays and switching devices, we established a nonlinear model of photovoltaic grid-connected inverters using the state space method and solved its model predictive controller. Then, using the phase diagram, folded diagram, and bifurcation diagram methods, we studied the nonlinear dynamic behavior under the influence of control parameters on both fast and slow scales. Finally, we investigated the methods of parameter selection based on the characteristics of nonlinear dynamic behavior. Our research shows that the predictive controller parameters are closely related to the bifurcation and chaos behaviors of the grid-connected photovoltaic inverter. The three-dimensional bifurcation diagram can be used to observe the periodic motion region of the control parameters. After selecting the optimization target, the bifurcation diagram can be used to guide the selection of control parameters for inverter design. The research results can be used to guide the modeling, stability analysis, and optimization design of photovoltaic grid-connected inverters.

scholarly journals Dynamic Emulation of a PEM Electrolyzer by Time Constant Based Exponential Model

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 750 ◽  
Author(s):  
Damien Guilbert ◽  
Gianpaolo Vitale
Keyword(s):  
Dynamic Behavior ◽  
Proton Exchange Membrane ◽  
Exponential Model ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Experimental Investigations ◽  
Model Parameters ◽  
Dynamic Operating ◽  
Pem Electrolyzer ◽  
Current Interval

The main objective of this paper is to develop a dynamic emulator of a proton exchange membrane (PEM) electrolyzer (EL) through an equivalent electrical model. Experimental investigations have highlighted the capacitive effect of EL when subjecting to dynamic current profiles, which so far has not been reported in the literature. Thanks to a thorough experimental study, the electrical domain of a PEM EL composed of 3 cells has been modeled under dynamic operating conditions. The dynamic emulator is based on an equivalent electrical scheme that takes into consideration the dynamic behavior of the EL in cases of sudden variation in the supply current. The model parameters were identified for a suitable current interval to consider them as constant and then tested with experimental data. The obtained results through the developed dynamic emulator have demonstrated its ability to accurately replicate the dynamic behavior of a PEM EL.

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