Identification Results

the results of these calculations are presented and then, in Sect. 9.5.2, the lead screw friction and damping identification results are given. 

By fitting a straight line to these data points using the least squares technique, the overall damping, Co, and residual friction torque, Tfo, were estimated. These results together with other known system parameters are listed in Table 9.3. 

As described in the previous section, a straight line is fitted to the data points at each velocity setting, which gives variation of motor torque vs. applied axial force 

Overall DC motor and the gearbox internal friction, Tfo 5.61 X 10 Nmrad/s 

14 Sample measurement results. Variation of motor torque with applied axial load at constant speeds. Dots measurements, solid line fitted line to the data points 

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Fig. 1 illustrates the identification result, i.e., validation of identified model. The 4-level pseudo random signal is introduced to obtain the excited output signal which contains the sufficient information on process dynamics. With these exciting and excited data, L and Lu as well as state space model are oalcidated and on the basis of these matrices the modified output prediction model is constructed according to Eq. (8). To both mathematical model assum as plimt and identified model another 4-level pseudo random signal is introduced and then the corresponding outputs fiom both are compared as shown in Fig. 1. Based on the identified model, we design the controller and investigate its performance under the demand on changes in the set-points for the conversion and M . The sampling time, prediction and...

Figure 7.4. The significance level of an identification result can be determined once the distribution of scores for false identification results is known. Score distributions for true results can vary between experiments and are typically unknown, in contrast with the distribution of scores for false identification results, which can be derived by various methods (see text for details). A score that is in a region with little overlap with the distribution for false results yields a good significance level (the gray area is small).

J. Eriksson, B. T. Chait, and D. Fenyo. A Statistical Basis for Testing the Significance of Mass Spectrometric Protein Identification Results. Anal Chem., 12, no. 5 (2000) 999-1005. 

We have developed a simple method of nonisotopically labeling sample nucleic acids, which are then hybridized simultaneously to an array of unlabeled, immobilized probes. This "reversed hybridization" procedure thus provides identification results after a single hybridization reaction. 

The quality of the system identification results is strongly dependent on the manner in which the spectroscopic measurements are made. In this regard, the time-scale of the individual spectral measurements Tgpect is crucial. Many good resolution FTIR, Raman, UV-VIS, fluorescence and H, F,"P NMR spectra can be obtained in 100 s or less. Also, many VCD, ECD, and 2D NMR spectra can be obtained in 1000 s or less. 

In the new and rather unique approach to this problem, the identification of inks on questioned documents depends on the maintenance of an up-to-date standard ink library. The actual identification is made by comparing the characteristics, or points of identification, resulting from the analysis of the questioned ink with the corresponding results obtained from dried samples (on standard paper) of the known standard inks in the library. Clearly, the larger the number of characteristics... 

TABLE 1. The identification results of some experimental data for steel (12X18H10T) ... 

The parameters identification results for the layers of the membrane from stainless steel with TiN protective coat are contained in Tables 3, 4. The approximations of the experimental fluxes by model ones for some temperatures are shown in Figs. 4, 5. 

Biomarker identification, result validation, biochemical pathway visualization... 

Lucas SV. 1984. GC/MS (gas chromatography/mass spectrophotometry) Analysis of organics in drinking water concentrates and advanced waste treatment concentrates, Vol. 2 Computer-printed tabulations of compound identification results for large-volume concentrations. Battelle Laboratories, Columbus, OH for U.S. Environmental Protection Agency, Office of Research and Development, Health Effects Research Laboratories, RTP, NC. Report No. EPA -600/1-84-020A. 397. 

Table 9 Identification results by the RE method Hinf I) on PCR product obtained by BCFWl and BCRWl...

AACompIdent, several amino acid compositions are available, and a sequence tag of up to six amino acids can be used to increase confidence in the identification results. 

From 2-DE gel spots, protein identification can be performed using various methods, as described in Section 4.4. All of these approaches imply a serial process, starting from a single-step or multiple-step chemical processing of the entire proteins and followed by the measurement and the interpretation of the produced protein attributes, then by specific queries in protein sequence database. The outputs of such searches are interpreted and used, first, to annotate the 2-DE image with the identification results and, second, to focus on the biological relevance of the expression level of the interesting protein spots. 

This example demonstrates that even for a deterministic system without any external loading, the response appears to be uncertain. In the real world, there are many types of unmodeled behavior/dynamics of complex physical phenomena (e.g., chaotic systems) and one possible approach is to treat them as random variables or random processes. Then, statistical moments are used to represent the overall behavior. This type of error is regarded hereafter as a modeling error. Another main source of uncertainty is due to the finite amount of information carried by the data. Due to the finite amount of the measurement, and hence the finite amount of information, identification results can be determined up to finite precision so uncertainty gets into the picture. Finally, due to the finite precision of data acquisition, measurement error is induced, including electrical noise and quantization error. 

In science and engineering problems, there are various uncertain parameters necessary to be determined for modeling and other purposes. The Bayes theorem offers the possibility for inferencing uncertain models/systems from their measurements. There are two levels of system identification. The first is parametric identiflcation, in which a class of mathematical models for a particular physical phenomenon or system is given with unknown parameters to be identified. The second level deals with the selection of a suitable class of mathematical models for parametric identification. This is significantly more difficult but more important than the first level since parametric identification results will be by no means meaningful if one fails to obtain a suitable class of models. However, due to the difficulty of this problem, it is usually determined by user s judgement. Chapters 2-5 focus on parametric identification and Chapter 6 addresses the problem of model class selection. 

Once the conditional optimal values for b and are obtained, the value of the goodness-of-flt function can be computed by Equation (2.121) whereas the normalizing constant in the posterior PDF does not affect the parametric identification results. By maximizing the goodness-of-fit function with respect to n, the updated model parameters can be obtained. Therefore, the closed-form solution of the conditional optimal parameters reduces the dimension of the original optimization problem from - - - - 1 to N only. 

Table 2.2 Identification result of the quadratic function coefficients...

To let the identification results solely depend on the contribution from the data, a non-informative prior is taken, i.e., p 9 C) is constant and it is absorbed into the normalizing constant. Table 3.1 refers to the identification results using a single set of displacement measurements V. It shows the optimal values = [S2, f, h calculated standard deviations... 

Table 3.1 Identification results for one set of data and frequency range (0, 1.2 Si] ...

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