High Level Object Model

Both CORBA and DCOM follow the same high-level object model as follows  

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On top, a high-level Data Model (cf. part 2 of ISO 15926) introduces generic classes like physical object, activity, and event, and defines generic relations, such as composition, connection, containment, and causality. Also, the aforementioned 4D approach is established here. The Data Model is domain-independent and contains roughly 200 classes. 

Blif-mv consists of symbolic hardware, where high level objects are modeled either by wires, if they are non-memorising, or latches, if they are memorising. These objects are very close to those found in Verilog. Blif-mv also contains so called multi-variables, which is a representation of enumerated variables. The next value of latches is the output wire of a circuit representation of the transition function. In Blif-mv, all objects are scalar, and all functions are either a subcircuit (a network of more elementary functions) or a function given in tabular form. No predefined function exists. 

The model we begin with specifies the behavior of our component. Most objects are involved in more than one action Our spreadsheet has addOperand, setNumber, and so on. Some objects are involved in actions with several other objects Whereas the spreadsheet has one user, the bank s ATM has customers, operators, and the bank s host machine to deal with. Each of the actions can be specified at a fairly high level, with details to be worked out later (see Figure 6.32). 

The business case This provides initial requirements, defining the business problem or opportunity that this project addresses. It typically includes a high-level list of numbered functional and nonfunctional requirements2 (lb), the business reasons and risks for the project (la), the scope of the project in terms of things definitely included or excluded, linked clearly to a business model in terms of business objectives, actions or use cases, and user roles that must be supported (la), known requirements on the architecture, design, and implementation (lc), and constraints on project budget and schedules (Id). 

Several factors must be considered in arriving at a level of sophistication of modeling consistent with industrial objectives. This is illustrated in Figure 2, where the cost of model development is expressed as a function of model sophistication. High level models require a smaller data base but are more expensive to develop and formulate. On the other hand, empirical models, although simpler to formulate and solve, rely heavily on an extensive data base which is costly to maintain. 

The last important evolution of PrODHyS is the integration of a dynamic hybrid simulation kernel (Ferret et al., 2004 Olivier et al., 2006, 2007). Indeed, the nature of the studied phenomena involves a rigorous description of the continuous and discrete dynamic. The use of Differential and Algebraic Equations (DAE) systems seems obvious for the description of continuous aspects. Moreover the high sequential aspect of the considered systems justifies the use of Petri nets model. This is why the Object Differential Petri Nets (ODPN) formalism is used to describe the simulation model associated with each component. It combines in the same structure a set of DAE systems and high level Petri nets (defining the legal sequences of commutation between states) and has the ability to detect state and time events. More details about the formalism ODPN can be found in previous papers (Ferret et al., 2004). 

The simulation model was implemented using the Java-based high level Petri net simulator Renew [796, 797]. Renew is a tool for the development and execution of object-oriented Petri nets. It provides synchronous channels and seamless Java integration for easy modeling. 

Ideally, at the end of a determination that has made use of hundreds or thousands of observations and is thus amenable to statistical analysis at a reasonably high level, one should not merely take comfort in satisfactory values of parameters such as R and ax but rather undertake an overall statistical analysis of the residuals to ascertain whether the uncertainties in the data were properly assessed and whether there is an opportunity to detect errors in the model or systematic errors in the data. In the past this has rarely been done, in part because it is a lot of work and in part because most crystallographers do not really know how to go about it. Recently, Abrahams and Keve17 (later also Hamilton and Abrahams18 ) showed that the objectives sought can be largely achieved by use of an (normal probability plot. In the application of this technique to the problem at hand, the normalized residual... 

In 1927, Harley Earl started the Art and Color Section at General Motors Company. Henry Ford had the vision to realize the potential for car ownership and in the early part of the century ownership of a Model T Ford has enough to confer a high level of status on these customers. Harly Earl went beyond this, he realized that once car ownership was more universal, Americans would want more than reliability and low price. He transformed the automobile from an engineered object to a stylish artifact. This was a dramatic conceptual shift that has never been overturned. He went beyond the rational needs of consumers to a deeper desire and turned the car from a utility object into a realizable dream. Harley Earl had a talent for visualizing dramatic car bodies. He created a car for Fatty Arbuckle and one for Tom Mix that even came with a saddle. 

This chapter has illustrated several identification methods that are used to determine dynamic parameters or models from experimental plant data. The simple and effective relay feedback test is a powerful tool for practical identification if the objective is the design of feedback controllers. The more complex and elegant statistical methods are currently popular with the theoreticians, but they require a very large amount of data (long test periods) and their effective use requires a high level of technical e q3ertise. It is very easy to get completely inaccurate results from these sophisticated tests if the user is not aware of all the potential pitfalls (both fundamental and numerical). 

Generic intermediate level One of three major hierarchical levels for systems and tasks identified in the elemental resource model. The generic intermediate level represents new systems (e.g., postural maintenance system, object gripper, object lifter, etc.) formed by the combination of functional units at the basic element level (e.g., flexors, extensors, processors, etc.). The term generic is used to imply the high frequency of use of systems at this level in tasks of daily life (i.e., items at the high level in the ERM). 

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