Data analysis input mapping

The objective of data analysis (or feature extraction) is to transform numeric inputs in such a way as to reject irrelevant information that can confuse the information of interest and to accentuate information that supports the feature mapping. This usually is accomplished by some form of numeric-numeric transformation in which the numeric input data are transformed into a set of numeric features. The numeric-numeric transformation makes use of a process model to map between the input and the output. 

Input Analysis addresses input mapping approaches that transform input data without knowledge of or interest in output variables. 

As discussed and illustrated in the introduction, data analysis can be conveniently viewed in terms of two categories of numeric-numeric manipulation, input and input-output, both of which transform numeric data into more valuable forms of numeric data. Input manipulations map from input data without knowledge of the output variables, generally to transform the input data to a more convenient representation that has unnecessary information removed while retaining the essential information. As presented in Section IV, input-output manipulations relate input variables to numeric output variables for the purpose of predictive modeling and may include an implicit or explicit input transformation step for reducing input dimensionality. When applied to data interpretation, the primary emphasis of input and input-output manipulation is on feature extraction, driving extracted features from the process data toward useful numeric information on plant behaviors. 

The overall objective of the system is to map from three types of numeric input process data into, generally, one to three root causes out of the possible 300. The data available include numeric information from sensors, product-specific numeric information such as molecular weight and area under peak from gel permeation chromatography (GPC) analysis of the product, and additional information from the GPC in the form of variances in expected shapes of traces. The plant also uses univariate statistical methods for data analysis of numeric product information. 

VANTED, short for visualization and analysis of networks with related experimental data, uses networks produced by the software tool itself or derived from the KEGG database. It allows representation of transcript, enzyme, and/or metabolite data on the networks (e.g., for time-series data). A standardized Excel sheet serves as input for the application. It offers advanced data analysis methods, such as correlation analysis or selforganizing maps (30,31). 

FIGURE 4.16 Different tasks solved by ANNs for the data analysis of a mnltidimensional object. Classification performs the assignment of inpnt objects X to predefined classes y. Modeling creates a functional relationship between the input objects and other multidimensional data. Mapping allows for reducing the input objects to a usually two-dimensional plane. Association allows assigning input objects to other multidimensional data on the basis of their relationships. 

In addition, the GPC trace, an example of which is shown in Fig. 42, reflects the composition signature of a given product and reflects the spectrum of molecular chains that are present. Analysis of the area, height, and location of each peak provides valuable quantitative information that is used as input to a CUSUM analysis. Numeric input data from the GPC is mapped into high, normal, and low, based on variance from established normal operating experience. Both the sensor and GPC interpretations are accomplished by individual numeric-symbolic interpreters using limit checking for each individual measurement. 

A more detailed overview of the main components of the GEOS-DAS system the forecast model, the input data (total ozone observations from Total Ozone Mapping Spectrometer /TOMS/ and vertical ozone profiles from the Solar Backscatter Ultra Violet instrument /SBUV/, the analysis scheme and its implementation could be easy found in the paper of Riishojgard [19]. 

Lee et al. (2006) use both PCA and CA to determine metal associations arising from anthropogenic input to the soils of Hong Kong. The analysis identifies Cu, Pb and Zn as the main elements responsible for pollution in the area. The author uses the data to calculate and map the distribution of a soil pollution index (SPI), which is indicative of the traffic impact on the urban territory (Fig. 8.8). 

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