Generic data, defined

In 2002, IUPAC initiated work in the development of terminology of a standard for analytical data. The standard format, XML, is intended to be universal for all types of analytical instrumentation, without permutations for different techniques. The XML format is designed to have information content of data defined in several layers. The most generic information is in the first layer, or core. More specific information about the instrumentation, sample details and experimental settings are stored in subsequent layers. The layers are defined as core, sample, technique, vendor, enterprise and user.29 The existence of a universal format will aid in the analysis of data from multiple sources, as well as in the archival and retrieval of data from historical processes. 

The analysis of process signals may be facilitated if the time series data can be cast into a symbolic form. The relevant trends and generic data features can then be extracted and monitored using this qualitative representation. Such a transformation is often carried out by defining a set of primitives (alphabet) that define a visual characteristic of the signal [78, 142, 247]. Here, the methodology proposed by Stephanopoulos and coworkers is discussed [9, 34, 35]. They treated the problem of trend representation graphically... 

RDB, thej eliability database module, creates a user-defined database or retrieves data from the IAEA generic reliability database. The design facilitates RDB development for components, human actions, initiating events and the attributes of components. Component unavailabilities can be calculated from the database of reliability parameters using 10 types of predel mcd leliabiliiv models. 

If there are external components—software or hardware—that define objects you need to use, spin off a task to evaluate whether to use these objects exactly as defined or whether to build a layer that offers a model closer and more natural to the one you would like to use internally in your development. If a core component defines widely shared and widely used objects, you may need to design a generic architectural scheme for extensible object data and behaviors. 

In this section, we will discuss compounds with the generic formula, PhCOCgFU—p-R (54) where R = H, Me, Et, Pr and Bu. For most of these compounds, the available enthalpy-of-formation data from the literature is only for the liquid. We have the same options as in the first subsection. The simplest difference quantity we can define is <548(PhCOC6H4, Ph R),... 

This section is focused primarily on source and detector technologies. For some applications the source or the detector actually defines the entire measurement technology, for example tunable lasers (source) and array spectrographs (detector). There are other important technologies to consider, especially in the area of data acquisition, control, computer, and communication technologies. These are rapidly changing areas and, if viewed generically, service all forms of instrumentation. Where practical, companies tend to use standard platforms. But for certain applications where performance is critical, there is still a case for proprietary solutions. 

Comment fields, generic test result records and parameter records are included in the database. These fields and records can be used without recompiling the database to store data, instrument parameters and comments which were not explicitly defined in the original database. The actual format and use of the data and parameters is determined by the application programs which use them. 

This equation can be used to describe the onset of instability, when a suitable mean flow is defined. We note that this equation is very generic for all incompressible flows (steady or unsteady flows), as it is based on full Navier-Stokes equation without making any assumptions. In Sengupta et al. (2006a) this equation has been used to explain the classical linear instability theory for parallel flows showing exactly identical TS waves obtained from Orr-Sommerfeld equation. In section 4.3, this is fully explained with the development of the actual equations and results. For the computational data, a mean flow was taken at t = 20 as representative undisturbed flow and the right hand side of (3.5.2) was calculated and plotted as shown in Fig. 3.9- at some representative times. 

Maximum use temperatures are reported on refractory data sheets in terms of pyrometric cone equivalent (pee). In essence, this pee defines the temperature at which a small standard sized cone of the material slumps due to softening. Table 18-1 lists the pee numbers with the corresponding temperature limits and generic types of brick which fall within the various pee ranges. These reported pce s indicate a refractory s maximum use limit when exposed to a gas-fired environment however, they may actually soften at much lower temperatures due to reactions with the atmosphere they are containing. 

In the past various attempts have been made to determine the property functions for all kinds of products. Almost all of these use linear regression techniques (see for example Powers and Moskovitz, 1974) to deal with the measured data. By the fact that most of the property function is highly non-linear, these techniques fail. In addition to the linear regression, a linear regression on non-linear functions of the attributes can be used. The drawback is that the non-linear functions of the attributes have to be defined by the user. At present this have to be done by traH-and-error and turns out to be a very tedious. Based on the above observations, a successful approach to determine the property function has to be based on a generic non-linear function description. One such approach is to use neural networks as a non-linear describing function. 

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. 

On the next lower layer, approximately 200 Templates are introduced (cf. part 7 of ISO 15926). A Template retrieves classes from the Data Model and correlates them via n-ary relations that way, it defines a configuration of interconnected classes, which jointly represent some generic modeling concept. Thus, Templates are comparable to the Design Patterns described in Subsect. 2.6.2. 

Using the PRIME meta model, situations were then defined, based on the detected signal deviations. In an initial step, generic definitions were used to detect a large number of temporarily grouped deviations, called situation instances. To form abstract situations out of the instances, methods of data mining had to be employed. 

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