Input and Output Parameters

Working from your initial type diagram, sketch a pair of snapshots before and after each operation. Draw them on one diagram, using highlights to show newly created objects and links and X and for objects or links that do not exist in the after snapshot. Name the input and output parameters to the action occurrence consistently with the snapshots. 

Create parameter models. Describe (by a type model) any input and output parameter types and their attributes and operations to the extent that the client and the implementor need to understand and agree on them. 

Many types are used for both purposes. For example, Date is often used in specifications and also has many implementations, and a good design would often have direct implementation of the specification types. Also, in some situations the specification requires an implementation not only of a primary type but also of related types required for input and output parameters. An example is shown in Step 8 in Section 3.4.1. 

A localized operation is a degenerate case of a joint action with a distinct receiver, in which nothing is known or stated about the initiator s identity or attributes. All relevant aspects of the initiator are abstracted into the input and output parameters of the operation. The following is a fully localized operation that cannot refer to the initiator at all. 

We have seen how an entire sequence of interactions between objects can be abstracted and described as a single joint action. We will next see how even in program code, an operation invocation itself has two sides the sender and the receiver. By using input and output parameters, a localized operation specification decouples the effect on the receiver from any information about the initiator. 

The primary reason for requiring transitivity is that individual types and classes almost never stand on their own. If you depend on a particular class, then you also depend on at least the type definitions of all its method input and output parameters, which could often be in its imported packages if these definitions change or if a new version is released, your work will need examination. 

This type of network is composed of an input layer, an output layer and one or more hidden layers (figure 1). Bias term in each layer is analogous to the constant term of any polynomial. The number of neurons in the input and the output layer depends on the respective number of input and output parameters taken into consideration. However, the hidden layer may contain zero or more neurons. All the layers are interconnected as shown in the figure and the strength of these interconnections is determined by the weights associated with them. The output from a neuron in the hidden layer is the transformation of the weighted sum of output from the input layers and is given as (1)... 

In addition to the linear sensitivity coefficients described above, various other types of sensitivity coefficients have been studied to probe underlying relationships between input and output parameters of chemical kinetic models. These include higher-order coefficients, Green s function coefficients, derived coefficients, feature coefficients, and principal components. Their descriptions and applications can be found in the literature [22,23, 27, 28],... 

The first step is to determine whether a classical additive model, an outranking approach or DEA should be used. The criteria to be used for the site selection/site ranking task do not lend themselves to a clear classification into input and output parameters. Furthermore, the relatively complex mathematical procedure underlying DEA was perceived to be inappropriate for support of top management decisions that are to a large extent driven by qualitative factors. Hence, DEA was ruled out first. 

Due to its flexible nature, the same ANN architecture can be applied in which the input and output parameters based on the H-Oil units are given in Table 1. There are twenty input nodes, ten from eaeh train (A and B). They cover the fresh and recycle liquid feed rates, feed API, makeup and recycle gas rates, hydrogen partial pressure, reactor pressure, exotherm, reactor average temperature and catalyst addition rate. The model also consists nine hidden and six output nodes. However, the number of nodes in each layer or the inclusion of which process parameters can be modified as occasion arises, such as changes in process requirement, model refinement or data availability. 

Each row corresponds to a step of the proof in the text. Only the most important input and output parameters of the algorithms are shown. The order from left to right is chronologic algorithms aligned with each other either interact, or are identical, or simulate each other. 

For the sake of simplicity, input and output parameters of tasks as well as data... 

The process view concept is illustrated by an example in Fig. 3.87. The top part of the figure shows a part of the Polyamide-6 process (used throughout the entire subsection) as it is seen from the perspective of the Chemical Company. In the middle part of Fig. 3.87, a process view definition named Re-actionSimulationTasks for this process is shown which contains two simulation tasks Simulate CSTR and Simulate PFR with their input and output parameters from the overall process, while the control flow between both tasks is not included in the process view. 

During verification of adequacy of the mathematical models and the real gas sensors, statistical approaches have usually been employed, since both input and output parameters of the sensors in some respect are incidental quantities. Sometimes in practical engineering tasks, the average or maximum deviation of some characteristics, calculated by the model from the corresponding experimental values, can be used as an adequacy criterion. However, such a criterion is implemented at the practical measurement of partial gas pressures in different environments, assuming that both informative and noninformative parameters of the sensor are constant and strictly follow the scope and conditions of the laboratory experiments. This is not always the case. Furthermore, the allowed deviation between calculated and measured characteristics (10-15%) is subjective estimation. 

Process performance qualification typically is conducted during the production of qualification lots using target set points for all process input parameters to demonstrate consistency and reproducibility of input and output parameters [32]. This process consistency is demonstrated by multiple full-scale batches, for example, three-to-five consecutive purification lots from three consecutive fermentation lots, during which yield and product concentration are monitored at each step [14]. A failed lot can occur without resetting the count if it is due to a known assignable cause that is not process-related (e.g., operator error or equipment failure) [33]. The cause of noncompliance must be determined and corrected, the noncompliant product disposition designated, and any corrective actions taken recorded [11]. At... 

These dimensionless groups are called pi terms, and the number of independent pi terms is equal to the number of variables involved minus the number of dimensions in which those variables may be measured (Murphy, 1950). In this example there were five variables and three dimensions, giving two pi terms. Because the formulation of pi terms in the example above was based on arbitrary choices, the pi terms are not unique. However, they are complete. The relationship between input and output parameters thus takes the form... 

In writing Equation 3.1, we have assumed that only the input and output parameters count. Indeed, when the volume under consideration is perfectly mixed, all phases inside this volume will have the same property as that at the output. This is the principle of a lumped parameter model (LPM). 

The configuration of a process chain is described in detail in Fig. 1. The so-called PEC model is classified in three levels and specifies basic terms used in the following (Denkena and Toenshoff 2011). The process constitutes the smallest and hence impartible unit of a process chain. Input and output parameters are part of a process. A manufacturing operation, for example, the generation of a drill hole with a tool. 

Figure 5.2 shows the interaction between the input and output parameters of rapid isobutylene polymerisation, occurring in the tubular turbulence reactor, demonstrating the possibility of automated reaction operation. 

The frequency response (although called transfer function) is a conmum function in signal analysis and control engineering when the dynamic behavior of a system must be analyzed. Therefore, the input and output parameters of the system will be compared as a function of frequency. For example, when the system is stimulated with a harmonic input signal of a certain frequency, the system will answer with the same frequency, but with attenuated amplitude and a shifted phase. Since the amplitude attenuation and the phase shift are both functiOTis of the stimulation frequency, it is common to plot them in Bode diagrams, where the amplirnde response and the phase response are displayed separately over the frequency. 

Sensitivity analysis of the influence of the variable input parameters to the reliability of the structures depends on the statistical independency between input and output parameters. 

Such a database enables a flexible overview and examination of the primary data available and guarantees the traceabihty of the Fhe PSA analyses. The basic structure of the Fhe PSA database as well as some important input and output parameters are depicted in Figure 2. The database is composed of two databases communicating via the field . The... 

The semantics of each input and output parameter are defined as an OWL concept formally specified in a given ontology, while preconditions and effects are represented as logical formulas that can be expressed in any appropriate logic (rule) language such as KIF, PDDL, SWRL or SPIN [35]. 

A digital signal processor (DSP) is used to monitor the input and output parameters of the APLC. The DSP then makes any needed adjustment in the amount of voltage compensation and current injection by modifying the switching characteristics of the insulated-gate bipolar transistors (IGBTs) in the APLC. 

Then, input and output parameters will be identified and classified. The methodology used to fit the model to sled tests will be presented, with the help of a sensitivity analysis and/or a rules system. We will explain how this approach enabled us to correlate the model for very different crash test configurations with the same basic data. Partial results of two simulations will be shown ... 

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