Multi-viewpoint modeling

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 ... 

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)... 

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. 

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. 

Motivated by these considerations, we have recently proposed a multi-chain fermionic dynamical symmetry model (FDSM) which was developed to specifically address the above raised questions. Our starting point is the Ginocchio SO(8) model 10(since from now on only fermion groups will be mentioned, we shall drop the use of the F superscript to denote them). In our opinion, Ginocchio was the first person to seriously pursue the concept of multi-chain dynamical symmetries from a fermionic viewpoint. The main ingredients of the Ginocchio model can be summarized as follows. If one were to take the fermion pair (i.e. a+a+ type of operators) with =0 S) and 2(D) and certain multipole operators (i.e. a+a type of operators), both types are constructed from... 

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. 

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. 

Eor multivariate calibration in analytical chemistry, the partial least squares (PLS) method [19], is very efficient. Here, the relations between a set of predictors and a set (not just one) of response variables are modeled. In multicomponent calibration the known concentrations of / components in n calibration samples are collected to constitute the response matrix Y (n rows, / columns). Digitization of the spectra of calibration samples using p wavelengths yields the predictor matrix X (n rows, p columns). The relations between X and Y are modeled by latent variables for both data sets. These latent variables (PLS components) are constructed to exhaust maximal variance (information) within both data sets on the one hand and to be maximally correlated for the purpose of good prediction on the other hand. From the computational viewpoint, solutions are obtained by a simple iterative procedure. Having established the model for calibration samples. comp>o-nent concentrations for future mixtures can be predicted from their spectra. A survey of multi-component regression is contained in [20],... 

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. 

It hardly needs saying that neither picture is sufficient by itself. Evidently correlation effects are substantial, especially in the underdoped regime. The specific nature of the transition from antiferromagnetic insulator to underdoped superconductor is still an enigma in many respects. A square lattice Hubbard model however fails to be able to address most of the existing data for many HTS, and especially for YBayCusOy, because the properties within the a—b plane are intrinsically anisotropic due to the presence of the Cu-O chains. In this chapter we will discuss data primarily from the viewpoint that one first considers the full multi-atom, multiband (but uncorrelated) band structure, energy, etc., and then considers the effect of correlations. We will concentrate on only a few specific issues and address the various viewpoints as appropriate. 

Fig. 4. Example of viewpoints on a multi-aspect system model 5.1 Structural Viewpoints...

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