Output data

Another benefit in using this kind of automatic classifier is that the output data gives an indication of the classification reliability. This information could be used to inform the operator which classifications are less reliable. 

Result of reconstruction is a 3D matrix of output data assigned with the values of the local density inside elementary volumes. The ways of obtaining the 3D matrix of output data can be various. They are determined by the structure of tomographic system and chosen way of collected data processing. 

Output data can be printed or exported to a spreadsheet. The rendering quality is very good. Structures can be rendered and labeled in several different ways. Molecular structures can be saved in several different formats or as image files. The presentation mode allows molecular structures to be combined with text. 

Numeric-to-numeric transformations are used as empirical mathematical models where the adaptive characteristics of neural networks learn to map between numeric sets of input-output data. In these modehng apphcations, neural networks are used as an alternative to traditional data regression schemes based on regression of plant data. Backpropagation networks have been widely used for this purpose. 

Those based on strictly empirical descriptions Mathematical models based on physical and chemical laws (e.g., mass and energy balances, thermodynamics, chemical reaction kinefics) are frequently employed in optimization apphcations. These models are conceptually attractive because a gener model for any system size can be developed before the system is constructed. On the other hand, an empirical model can be devised that simply correlates input-output data without any physiochemical analysis of the process. For... 

Providing input/output data is available, a neural network may be used to model the dynamies of an unknown plant. There is no eonstraint as to whether the plant is linear or nonlinear, providing that the training data eovers the whole envelope of plant operation. 

The training file eonsisted of input data of the form Time elapsed t kT), Rudder angle 6(kT), Engine speed n(kT) with eorresponding output data Forward veloeity u kT), Lateral veloeity v(kT), Yaw-rate r kT). 

So, for example, with the ship model shown in Figure 10.26, the inverse model eould be trained with time, forward veloeity, lateral veloeity and yaw-rate as input data and rudder angle and engine speed as output data. 

Model Execution uses efficient algorithms to access input data, to perform numerical simulations, to generate appropriate output data using a batch process. 

Normalize both the input and the target output data to fit the transfer function range. This implies that the data have to be scaled to fit between the minimum and maximum values of the selected transfer function. 

Similarly, the network predicted data must be unsealed for error estimation with the experimental output data. The unsealing was performed using a simple linear transformation to each data point. 

The first step in applying FEA is the construction of a model that breaks a component into simple standardized shapes or (usual term) elements located in space by a common coordinate grid system. The coordinate points of the element corners, or nodes, are the locations in the model where output data are provided. In some cases, special elements can also be used that provide additional nodes along their length or sides. Nodal stiffness properties are identified, arranged into matrices, and loaded into a computer where they are processed with certain applied loads and boundary conditions to calculate displacements and strains imposed by the loads (Appendix A PLASTICS DESIGN TOOLBOX). 

C. Output Data Reflect Essentially the dnjdt Values. 375... 

In the first one, the desorption rates and the corresponding desorbed amounts at a set of particular temperatures are extracted from the output data. These pairs of values are then substituted into the Arrhenius equation, and from their temperature dependence its parameters are estimated. This is the most general treatment, for which a more empirical knowledge of the time-temperature dependence is sufficient, and which in principle does not presume a constancy of the parameters in the Arrhenius equation. It requires, however, a graphical or numerical integration of experimental data and in some cases their differentiation as well, which inherently brings about some loss of information and accuracy, The reliability of the temperature estimate throughout the whole experiment with this... 

The minimum number of postulates of the model of a desorption process with no explicit analytical expression of the heating schedule are required if the primary output data are treated according to Eqs. (10) and (12), viz. by numerical or graphical derivations and integrations of the recorded pressure data. After an adaptation of the analyzer, these operations can be performed by means of electrical circuits. The known temperature-time relationship (either in the form of an analytical function or established... 

In principle, only the expressions for the correct desorption order should give a straight line at higher temperatures. In practice, however, the experimental scatter, possible inaccuracy in corrections of the output data, inherent departures from the simple model considered (mainly the dependence of Ea on 0), together with a rather strong correlation which can be shown to exist between the functions In [(1 /nB) — (l/nB0) ] and ln[ln(na0) — ln(n ) ], can seriously impair the plot and make the estimate of the desorption order rather dubious. Statistical methods should be helpful in this case, but to our knowledge they have not been employed so far. 

Measuring HTS Output Data Quality and Validated Hits... 

The output of a stab detonator is a detonation. Unfortunately, available output data are more nonspecific than input data so that firm, quant choices of output are difficult to make. Consequently, comprehensive testing is usually required (Refs 6 12)... 

The extensive data from these experiments were processed by the CDC-3600 computer at this laboratory. The curves and histograms were machine plotted from the output data of the computer. 

When the manufacturer has provided RS-232 output, data can be read directly through a serial port. 

An attempt has been made to make the input requirements convenient and self-explanatory. The output data are simply printed out at the terminal. No special output programming such as graphics have been supplied since the output requirements will vary strongly with the specific application. The program will run on any IBM-PC or compatible clone using BASICA (note for those users unfamiliar with BASICA, real constants are written with a this does not indicate a factorial expression). 

In POLYM the output data of KINREL are used with compositional information to calculate the number and mass average molecular masses (Rn and Rm, respectively) and number and end-group average functionalities (fp and fg> respectively) in the pre-gel region in all stages. In addition, the network characteristics such as sol fraction, mj, and the number of elastically active network chains per monomer (5), Ng, are calculated in the post-gel regime of stage 3. 

I/O data-based prediction model can be obtained in one step from collected past input and output data. However, thiCTe stiU exists a problem to be resolved. This prediction model does not require any stochastic observer to calculate the predicted output over one prediction horiajn. This feature can provide simplicity for control designer but in the pr ence of significant process or measurement noise, it can bring about too noise sensitive controller, i.e., file control input is also suppose to oscillate due to the noise of measursd output... 

As mentioned above, the backbone of the controller is the identified LTI part of Wiener model and the inverse of static nonlinear part just plays the role of converting the original output and reference of process to their linear counterpart. By doing so, the designed controller will try to make the linear counterpart of output follow that of reference. What should be advanced is, therefore, to obtain the linear input/output data-based prediction model, which is obtained by subspace identification. Let us consider the following state space model that can describe a general linear time invariant system ... 

Parameter estimation is one of the steps involved in the formulation and validation of a mathematical model that describes a process of interest. Parameter estimation refers to the process of obtaining values of the parameters from the matching of the model-based calculated values to the set of measurements (data). This is the classic parameter estimation or model fitting problem and it should be distinguished from the identification problem. The latter involves the development of a model from input/output data only. This case arises when there is no a priori information about the form of the model i.e. it is a black box. 

Allows for the definition of data sets and variables as well as the all-important observation boundary where the XML engine outputs data set observations. 

The resulting freqs data set contains the BY variable trt, a, b, and the cell frequency count and percentage percent variables. Row and column percentages can be added to the output data set by specifying the OUTPCT option. If you also want the totals row and column that you see in your PROC FREQ listing output, you can use ODS to export that to a data set called freqs ... 

In this case dsetname is the name of your output data set, and variables is a variable name list of one or more statistics in the following table. 

PROC MEANS, PROC SUMMARY, and PROC TABULATE are other SAS procedures that you can use to get descriptive statistics and place them into output data sets. However, those procedures do not offer any descriptive statistical variables that you cannot get from PROC FREQ or PROC UNIVARIATE. 

The output data set pvalue includes a variable called P PCHI that contains the Pearson chi-square p-value you need. 

The output data set pvalue contains numerous Wilcoxon test statistics. Assuming that you want the two-sided normal approximation test p-value, the variable in the pvalue data set that you want is called P2 WIL. ... 

The OUTSTAT= output data set pvalue contains the p-value in the PROB variable. If you have multiple predictor variables, you need to use the PROC GLM ODS data set OverallANOVA to get the overall model p-value from the ProbF variable. These output data sets contain other variables, such as the degrees of freedom, sum of squares, mean square, and F statistic, if you need them for an ANOVA table presentation. 

Check the SAS procedure syntax to see if there is an output data set that will provide you with the values you need. The output data sets from the SAS procedures are usually friendlier to use than the ODS OUTPUT data sets. 

---