Interpretation Performance

The simulator models the FCCU, generating output from 110 sensors every 20 seconds. In all, 13 different malfunction situations were simulated and are available for analysis. There are two scenarios for each malfunction, slow and fast ramp. Table II provides a list and brief description of each malfunction. A typical training scenario for any fast ramp malfunction simulation had the landmarks listed in Table III. Similarly, a typical training scenario for any slow ramp malfunction simulation is shown in Table IV. For both the fast and slow ramp scenarios, there was data corresponding to 10 min of steady-state behavior prior to onset of the faulty situations. 

With reference to Figs. 24 and 25, Fig. 31 illustrates a pattern representa- 

As shown, the data patterns 1 and 2 are classified as Normal with high certainty, as they lie within the boundaries of the normal class. However, 3 and 4 are classified as normal with medium certainty, as they lie outside the normal region, but their similarities are closest to the normal cluster. Similarly, the data pattern represented by 5 is classified as fault2 with medium certainty and the data patterns represented by 6, 7, and 8 are classified as fault2, and so on. If the data are collected every 20 seconds as in the case study, the dynamic interpretation is tabulated as shown in Table V, with the labels in italics representing the correct class and appropriate certainty. An x means there was not an interpretation with this certainty. 

To illustrate that increasing output dimensionality can lead to performance problems for a numeric-symbolic interpreter, the ART interpreter was trained with various subgroupings of the 13 malfunction scenarios. With 13 malfunction scenarios and one normal scenario, the maximum output dimension is 14. 

The subgroups were determined based on a hierarchical decomposition strategy for a FCCU (Ramesh et al, 1992). The FCCU was decomposed into four separate units feed.system, catalyst.system, reactor/regenera-tor.system, and separation.system as shown in Fig. 32. Each of these units was further divided into more detailed functional, structural, or behavioral 

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In addition to a large input dimension, complex processes also typically have a large output dimension (i.e., many possible plant behaviors). Although only a few events occur at any given time, identification of those few events requires consideration of a large subset of all possible events. Without careful management of both input and output dimensionality, interpretation performance deteriorates rapidly with scale and complexity. 

By definition, the exemplar patterns used by these algorithms must be representative of the various pattern classes. Performance is tied directly to the choice and distribution of these exemplar patterns. In light of the high dimensionality of the process data interpretation problem, these approaches leave in question how reasonable it is to accurately partition a space such as R6+ (six-dimensional representation space) using a finite set of pattern exemplars. This degradation of interpretation performance as the number of possible labels (classes) increases is an issue of output dimensionality. 

FIGURE 2.1 The relationship among data, information, knowledge, and action is described by the knowledge pyramid. The transition from data to information is performed by calculation, analysis, and interpretation performed by computer software, whereas in the transition from information to knowledge, human interpretation, experience, and intuition play a major role. The final step of reasoning leads to actions to be taken. Amount, context, and patterns can be considered as theoretical factors in contrast to that, real data have to be described by concepts of certainty, probability, and fuzziness. 

Miglioretti DL, Smith-Bindman R, Abraham L, et al (2007) Radiologist characteristics associated with interpretive performance of diagnostic mammography. J Natl Cancer Inst 99 1854-1863... 

Section 20.4 explains and discusses the concept acceptance sampling, its practices and interpretations. Performing assays on pharmaceutical preparations in a sample is not sufficient warranty of quality, as valid conclusions with respect to the population cannot be drawn without knowledge of the sampling method. Such conclusion would be acceptance or rejection of the batch. 

When the interpreter computes (or evaluates, in Lisp jargon) a list, it always assumes that the first element of the list is the name of a function and the rest of the elements are the arguments that the function needs for processing. In the above example, the interpreter performs the addition of two numbers the name of the function is the symbol + and the two arguments are the numbers 330 and 336. 

One approach to a mathematically well defined performance measure is to interpret the amplitude values of a processed signal as realizations of a stochastic variable x which can take a discrete number of values with probabilities P , n = 1,2,..., N. Briefly motivated in the introduction, then an interesting quality measure is the entropy H x) of the amplitude distribu-... 

In the experiments, the probabilities were estimated from the processed signal by means of a histogram. It is well known that the entropy is large for nearly uniform distributions and small for distributions with few peaks. Thus it is an interesting candidate as a performance measure when the goal is to process a signal to become more easily interpreted. 

Repeatability. This refers to two aspects of inspection similarity between objects that are inspected and possibility of maintaining constant inspection conditions (settings) for all the inspections performed. Obviously, interpretation of data in repeatable conditions is significantly simplified. Usually, inspection during or after manufacturing process will be repeatable. Another example of repeatable inspection is inspection of heat exchangers in power nuclear plants, inspection of aircrafts as these are well standardised. However, a large part of the NDT inspection done is not repeatable. 

In service inspections of French nuclear Pressure Water Reactor (PWR) vessels are carried out automatically in complete immersion from the inside by means of ultrasonic focused probes working in the pulse echo mode. Concern has been expressed about the capabilities of performing non destructive evaluation of the Outer Surface Defects (OSD), i.e. defects located in the vicinity of the outer surface of the inspected components. OSD are insonified by both a "direct" field that passes through the inner surface (water/steel) of the component containing the defect and a "secondary" field reflected from the outer surface. Consequently, the Bscan images, containing the signatures of such defects, are complicated and their interpretation is a difficult task. 

Radiographic inspection is performed to reach a decision about the acceptability of the component or product being tested. Before any evaluation can be made, the interpreter must be certain that the images are satisfactory. In addition, the interpreter must have a solid understanding of the following in order to be successful with the interpretation of the radiographic film for welding quality. 

As a first step in the direction outlined here some manufacturers and BAM last year discussed the problems and the possible procedures of such a system of quality assurance. As a result of this meeting round robin tests for the harmonization of the measurements of film system parameters and a possible procedure of surveillance of the quality of film systems were proposed. Closely related to these the BAM offers to perform the classification of film systems. But as during the production of films variations of the properties of the different batches cannot be avoided, the results of measurements of films of a single batch will be restricted to this charge, while only the measurements and mean of several batches of a film type will give representative values of its properties. This fact is taken into account already in section 4 of the standard EN 584-1 which can be interpreted as a kind of continuous surveillance. In accordance with this standard a film system caimot be certified on the base of measurements of a single emulsion only. 

The aim of the work we present in this paper is to optimize the control parameters used in particles magnetic and interpret the obtained results. Experiments are performed on samples of welds or materials containing known defects. The realized and tested defects are grooves situated at different depths with variables dimensions. Other types of defects have been studied (inclusions, lack of penetration, etc.). 

In the field of radiation methods of control, development work was performed in order to create the X-ray detectors with a low content of silver. X-ray TV systems with improved performance for automatic interpretation of the X-ray TV images, portable radiometers and dosimeters, creation of portable equipment for radioscopy of the welded joints of pipelines, etc. 

The presence of surface conductance behind the slip plane alters the relationships between the various electrokinetic phenomena [83, 84] further complications arise in solvent mixtures [85]. Surface conductance can have a profound effect on the streaming current and electrophoretic mobility of polymer latices [86, 87]. In order to obtain an accurate interpretation of the electrostatic properties of a suspension, one must perform more than one type of electrokinetic experiment. One novel approach is to measure electrophoretic mobility and dielectric spectroscopy in a single instrument [88]. 

The present high cost of full CASSCF direct dynamics means that it is not possible to use such calculations to run large numbers of trajectories. As a result it cannot be used to build up experience of the types of effects to be found in dynamical studies of organic photochemistry, and in their interpretation. This problem can be remedied by performing calculations using the MMVB force field [63,64]. 

Woodruff and co-workers introduced the expert system PAIRS [67], a program that is able to analyze IR spectra in the same manner as a spectroscopist would. Chalmers and co-workers [68] used an approach for automated interpretation of Fourier Transform Raman spectra of complex polymers. Andreev and Argirov developed the expert system EXPIRS [69] for the interpretation of IR spectra. EXPIRS provides a hierarchical organization of the characteristic groups that are recognized by peak detection in discrete ames. Penchev et al. [70] recently introduced a computer system that performs searches in spectral libraries and systematic analysis of mixture spectra. It is able to classify IR spectra with the aid of linear discriminant analysis, artificial neural networks, and the method of fe-nearest neighbors. 

Neural networks have been applied to IR spectrum interpreting systems in many variations and applications. Anand [108] introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90%. Robb and Munk [109] used a linear neural network model for interpreting IR spectra for routine analysis purposes, with a similar performance. Ehrentreich et al. [110] used a counterpropagation network based on a strategy of Novic and Zupan [111] to model the correlation of structures and IR spectra. Penchev and co-workers [112] compared three types of spectral features derived from IR peak tables for their ability to be used in automatic classification of IR spectra. 

Th e ability to perform m oleciilar orbital (MO ) calculation s on m et-als is extremely useliil because molecular mechanics methods are gen erally unable to treat m etals. This is becau se m etals h ave a wide range of valences, oxidation states, spin multiplicities, and have 1111 usual bonding situations (e.g.. d%-p% back bonding). In addition. the 11 on direction al n at are o ( m etallic hon din g is less am en a-ble to a ball and spring interpretation. 

Measurements of pressure driven fluxes may also be performed on mixtures, and here care must be exercised in interpretation, since the fluxes of the two substances are not in proportion to their mole fractions in the mixture. Consequently the fluxes and must, in principle, be measured... 

Using Control Charts for Quality Assurance Control charts play an important role in a performance-based program of quality assurance because they provide an easily interpreted picture of the statistical state of an analytical system. Quality assessment samples such as blanks, standards, and spike recoveries can be monitored with property control charts. A precision control chart can be used to monitor duplicate samples. 

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