Data-processing problems

Chapter 2 offers a unifying exposure to the relevant concepts of estimability and redundancy, in particular their importance in the decomposition of the general data processing problem. 

Be practical and collect only what you need. If you carefully determine which data to collect, you minimize the data-processing problem but run the risk of missing useful data. This is like spending an hour to pack a small suitcase but forgetting your comb. 

Sun, J. G., 1985, Data Processing Problems for Radioactive Particle Tracking Measurement, M. S. Thesis, Univ. of Illinois, Urbana-Champaign. 

Perone and Eagleston have described an on-line computer program simulating the process of gas chromatography. This process involves a wide variety of data-processing problems. Data-processing problems in this program are, for example ... 

Actually, a classification of problems is needed for further clarification. On the one hand, thdfe are data-processing problems, where the specification is almost tantamount to an actual program. On the other hand, there are algorithmic problems, where the specification is at best some kind of a naive (inefficient) program. The focus in this... 

Table 1.1 The nonlinearity of some data processing problems.

It is Vapnik and his coworkers who found the trick of using kernel function as a tool to convert nonlinear data processing problems into linearly solvable problems in about 25 years later after Aizermann s work about the potential function method. 

Electrical engineers apply electrical and mechanical principles to design and construct electronic computers. They perform research in computer circuitry, develop new methods of data processing and storage, and seek new ways to apply computers to various types of data-processing problems. 

The first of them to determine the LMA quantitatively and the second - the LF qualitatively Of course, limit of sensitivity of the LF channel depends on the rope type and on its state very close because the LF are detected by signal pulses exceeding over a noise level. The level is less for new ropes (especially for the locked coil ropes) than for multi-strand ropes used (especially for the ropes corroded). Even if a skilled and experienced operator interprets a record, this cannot exclude possible errors completely because of the evaluation subjectivity. Moreover it takes a lot of time for the interpretation. Some of flaw detector producers understand the problem and are intended to develop new instruments using data processing by a computer [6]. 

The evolutionary process of a genetic algorithm is accomplished by genetic operators which translate the evolutionary concepts of selection, recombination or crossover, and mutation into data processing to solve an optimization problem dynamically. Possible solutions to the problem are coded as so-called artificial chromosomes, which are changed and adapted throughout the optimization process until an optimrun solution is obtained. 

The major problem in this procedure is not in obtaining an accurate value for the solute diffusivity from peak width measurements (which is relatively straightforward) but in identifying the best relationship between diffusivity and molecular weight to employ in the subsequent data processing. 

Sections 2.5.1 to 2.5.7 are not an exhaustive list of equipment, process, maintenance, and data processing factors that influence failure rate, but they are an attempt to make the reader aware of the problems. The following example illustrates some of the points made ... 

Available data processing is a special starting procedure in the further comparison of three methods in line with established priorities by having recourse to the model problem (35) that we have set up on a square grid /ij = = h in the square G with the unit sides (/j = /2 = 1). Plain... 

General considerations of data availability lead immediately to the recognition that detection systems are more likely to be designed as comprehensive numeric-symbolic interpreters as illustrated in Fig. 3. State description systems may be configured as shown in either Fig. 3 or Fig. 4. Fault classification systems are most likely to require the symbolic-symbolic mapping to compensate for limited data as shown in Fig. 4. Many practical data interpretation problems involve all three kinds of interpreters. In all situations, there is a clear need for interpretation systems to adapt to and evolve with changing process conditions and ever-increasing experience. 

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. 

Consider again a batch polymerization process where the process is characterized by the sequential execution of a number of steps that take place in the two reactors. These are steps such as initial reactor charge, titration, reaction initiation, polymerization, and transfer. Because much of the critical product quality information is available only at the end of a batch cycle, the data interpretation system has been designed for diagnosis at the end of a cycle. At the end of a particular run, the data are analyzed and the identification of any problems is translated into corrective actions that are implemented for the next cycle. The interpretations of interest include root causes having to do with process problems (e.g., contamination or transfer problems), equipment malfunctions (e.g., valve problems or instrument failures), and step execution problems (e.g., titration too fast or too much catalyst added). The output dimension of the process is large with more than 300 possible root causes. Additional detail on the diagnostic system can be found in Sravana (1994). 

Manually processing each chromatographic peak is not only time and labor intensive but difficult to reproduce. To overcome these problems and to provide a consistent data format that was independent of retention time, a number of data-processing subroutines were automated to produce a single representative cellular protein spectrum. 

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