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Multi-viewpoint system model

The applicative case-study that supports the evaluation of the methodology and its associated tools is a system function called Compute traction orders . While being limited to one single system function, this case-study is representative of the system since it contains both critical and non-critical sub-functions and considers both realtime and operational constraints. The use-case will allow to structure and strengthen the development platform and framework of such systems, especially in the scope of multi-viewpoint system modelling (e.g. operational, functional, constructional, dysfunctional...). A simplified view of interoperability needs between system model, safety analysis and requirement management is represented in the following workflow ... [Pg.132]

Fig. 13.1 Multi-viewpoint system modeling in heterogeneous settings... Fig. 13.1 Multi-viewpoint system modeling in heterogeneous settings...
Running Example For the remainder of this chapter, we consider the development and maintenance of a multi-viewpoint system model within the realm of computer networks as our running example. This fictitious system is based on three individual system components/tools that all rely on partially different but overlapping conceptualizations of the domain of interest, which are expressed as ontologies as depicted in Fig. 13.2. [Pg.330]

Fig. 4. Example of viewpoints on a multi-aspect system model 5.1 Structural Viewpoints... Fig. 4. Example of viewpoints on a multi-aspect system model 5.1 Structural Viewpoints...
From the viewpoint of modeling, the ultimate goal of the kinetic analysis of pressure-dependent reaction systems is to provide reliable time-independent rate expressions k(T, p) which can be incorporated into large kinetic models. The functional forms of these rate expressions can be rather complicated for multi-channel multiple wells systems, since—as we saw from the examples—the competition of product channels leads to strongly non-Arrhenius behavior. On the other hand, pressure-dependent rate constants for single-well single-channel reaction systems are comparably easy to describe. Therefore, we will divide this discussion into two sections going from simple fall-off systems to complex systems. [Pg.175]

Benveniste, A., Caillaud, B., Passerone, R. Multi-viewpoint state machines for rich component models. In Model-Based Design for Embedded Systems. CRC Press (November 2009)... [Pg.146]

An alternate approach is to utilize the chromatogram heights as representative of individual concentrations of molecular size. From the kinetic modeling viewpoint, this leads to treating the polymerization as a well-characterized, multi-component reaction system. [Pg.179]

Naturally, the type of controller plays an important role. In this chapter we limit the analysis to classical PID controllers. These form over 90% of the control loops in industry. As mentioned, from a plantwide control viewpoint multi-SISO controllers are the most adapted. Naturally, we do not exclude more sophisticated MIMO control systems, as DMC or Model Based Control systems, but these are typically applied to stand-alone complex units, as FCC reactors, complex distillation units in refining, etc. Hence, the controllability analysis presented here aims more to get a conceptual insight in the dynamics of a process related to its design than to offer a high-performance control solution. [Pg.464]

Since the discovery of HTS, there has been active discussion about how to describe the electronic structure. The strongly correlated viewpoint has been that the strong intra-atomic repulsion (Hubbard U term) is so important, and the two-dimensionality so clear, that a 2D Hubbard model on a square lattice will provide the basic behavior that must be understood. A secondary question is whether a single band is sufficient, or other degrees of freedom present only in a several-band model are necessary. The band structure viewpoint might be said to be based on the presumption that it is necessary to consider the full complexity of the multi-atom, multiband character of the system, even at the cost of neglecting important correlations. [Pg.456]


See other pages where Multi-viewpoint system model is mentioned: [Pg.293]    [Pg.328]    [Pg.346]    [Pg.40]    [Pg.328]    [Pg.385]    [Pg.129]    [Pg.742]    [Pg.25]    [Pg.169]    [Pg.63]    [Pg.90]    [Pg.347]   
See also in sourсe #XX -- [ Pg.330 ]




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