Issues - Data Encoding

The purpose of the following four chapters is to build a foundation of understanding of basic neural network principles. Subsequent chapters will address issues specific to choosing the neural network design (architecture) for particular applications and the preparation of data (data encoding) for use by neural networks. 

Three reports have been issued containing IPRDS failure data. Information on pumps, valves, and major components in NPP electrical distribution systems has been encoded and analyzed. All three reports provide introductions to the IPRDS, explain failure data collections, discuss the type of failure data in the data base, and summarize the findings. They all contain comprehensive breakdowns of failure rates by failure modes with the results compared with WASH-1400 and the corresponding LER summaries. Statistical tables and plant-specific data are found in the appendixes. Because the data base was developed from only four nuclear power stations, caution should be used for other than generic application. 

Major problems facing an investigator who wants to prepare data for analysis or neural network modeling concern what input data features are to be used and how the information will be encoded before presentation to the model. Another issue to be faced concerns discovery of biological rules and features from the data, after analysis or modeling-e.g., what do the results mean Interpretation of weights after training, for example, is a particularly difficult problem. 

A barcode basically is a machine-readable visual representation of information printed on the surface of objects. There are several different kinds of barcodes, for example, barcodes which store data in the widths and spacing of printed parallel lines, and those that store data within the patterns of dots, or concentric circles, or even hidden within images. This encoded data on the barcodes is read by barcode readers, which update the backend ERP, SCM, or WMS systems. However there are some inherent issues with using a barcode, for instance, barcodes become ineffective in rain, fog, snow, dirt and grime, and so forth (Tecstra, n.d.). Since barcodes rely on optical sensors, any minor change on the barcode print can make it difficult to read. This can be commonly seen at point of sale (POS) in the supermarkets, where the POS operator scans the barcode several times because it is either wet or not aligned properly. 

Section 15.4 provides a discussion of similarity measures, which depend on three factors (1) the representation used to encode the desired molecular and chemical information, (2) whether and how much information is weighted, and (3) the similarity function (sometimes called the similarity coefficient) that maps the set of ordered pairs of representations onto the unit interval of the real line. Each of these factors is discussed in separate subsections. Section 15.5 presents a discussion of a number of questions that address significant issues associated with MSA Does asymmetric similarity have a role to play Do two-dimensional (2D) similarity methods perform better than three-dimensional (3D) methods Do data fusion and consensus similarity methods exhibit improved results Are different similarity measures statistically independent How do we compare similarity methods Can similarity measures be validated S ection 15.6 provides a discussion of activity landscapes... 

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