Model Expansion in Model-Driven Architectures

Author(s):  
Tsung Lee ◽  
Jhih-Syan Hou

In this chapter, the authors introduce a model expansion method that is used in a new methodology of model composition and evolution for broad design domains. In the methodology, hierarchical model compositional relationships are captured in a model composition graph (MCG) as a schema of designs. An MCG schema can be used as a blueprint for systematic and flexible evolution of designs with three hierarchical model refinement operations: expansion, synthesis, and configuration. In this methodology, due to the need of hierarchical sharing in software and hardware domains, the authors designed an algorithm to achieve conditional and recursive model expansion with hierarchical model instance sharing that is not achievable in other expansion methods. Hierarchical model instance sharing complicates the design structure from tree structures to graph structures. The model expansion algorithm was thus designed with enhanced features of maintenance of MCG instance consistency, path-based search of shared submodel instances, and dependency preserving expansion ordering. The expansion specification and the expansion process are integrated with the MCG-based methodology. Model parameters set by designers and other refinement operations can be used to guide each expansion step of design models iteratively.

Author(s):  
Byeng D. Youn ◽  
Byung C. Jung ◽  
Zhimin Xi ◽  
Sang Bum Kim

As the role of predictive models has increased, the fidelity of computational results has been of great concern to engineering decision makers. Often our limited understanding of complex systems leads to building inappropriate predictive models. To address a growing concern about the fidelity of the predictive models, this paper proposes a hierarchical model validation procedure with two validation activities: (1) validation planning (top-down) and (2) validation execution (bottom-up). In the validation planning, engineers define either the physics-of-failure (PoF) mechanisms or the system performances of interest. Then, the engineering system is decomposed into subsystems or components of which computer models are partially valid in terms of PoF mechanisms or system performances of interest. Validation planning will identify vital tests and predictive models along with both known and unknown model parameter(s). The validation execution takes a bottom-up approach, improving the fidelity of the computer model at any hierarchical level using a statistical calibration technique. This technique compares the observed test results with the predicted results from the computer model. A likelihood function is used for the comparison metric. In the statistical calibration, an optimization technique is employed to maximize the likelihood function while determining the unknown model parameters. As the predictive model at a lower hierarchy level becomes valid, the valid model is fused into a model at a higher hierarchy level. The validation execution is then continued for the model at the higher hierarchy level. A cellular phone is used to demonstrate the hierarchical validation of predictive models presented in this paper.


2020 ◽  
Vol 13 (07) ◽  
pp. 2050069 ◽  
Author(s):  
Mohamed El Fatini ◽  
Idriss Sekkak ◽  
Aziz Laaribi ◽  
Roger Pettersson ◽  
Kai Wang

The aim of this paper is to investigate a stochastic threshold for a delayed epidemic model driven by Lévy noise with a nonlinear incidence and vaccination. Mainly, we derive a stochastic threshold [Formula: see text] which depends on model parameters and stochastic coefficients for a better understanding of the dynamical spreading of the disease. First, we prove the well posedness of the model. Then, we study the extinction and the persistence of the disease according to the values of [Formula: see text]. Furthermore, using different scenarios of Tuberculosis disease in Morocco, we perform some numerical simulations to support the analytical results.


1989 ◽  
Vol 12 (4) ◽  
pp. 439-449
Author(s):  
Chuan‐Ch'u Ding ◽  
Yuan‐Shiuan Liao ◽  
Di Chiu

Robotica ◽  
1996 ◽  
Vol 14 (1) ◽  
pp. 91-102 ◽  
Author(s):  
M. O. Tokhi ◽  
A. K. M. Azad

SummaryThis paper presents theoretical and experimental investigations into modelling a single-link flexible manipulator system. An analytical model of the manipulator, characterised by an infinite number of modes, is developed using the Lagrange's equation and modal expansion method. This is used to develop equivalent time-domain and frequency-domain working models of the system in state-space and transfer function forms respectively. The model parameters are then estimated experimentally using system's measured input/output data. The model thus obtained is validated through experimentation and results including the effect of payload on system characteristics presented and discussed.


2015 ◽  
Vol 719-720 ◽  
pp. 324-329
Author(s):  
Zhu Bing Hu ◽  
Li Xin Deng ◽  
Bing Bing Li

The focus of this paper is uncertainty modeling and controller designing of air-breathing hypersonic vehicle. First, the hypersonic vehicle longitudinal dynamics model is made on the base of the fitting of aerodynamic parameters. As to the uncertainty of model parameters, a linear model of the uncertain part is established and the LQR controller parameters are thereupon designed with state transition and a first-order Taylor series expansion method, based on feedback linearization of the system. The simulation results that the hypersonic flight control system stated in this paper can well realize the tracking of speed and trajectory angle input command under the situation of floating model parameters.


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