Pattern recognition human

As with the pneumatic panels, the display capitalizes on human pattern recognition capabilities. Should a furnace not be operating efficiently or a failure occur, this can be quickly detected by observing deviations from the standard symmetrical shape. In practice, the extent of any such deviations will be proportionally equivalent to the actual process parameter deviation. 

Dixon, S.J., Xu, Y., Brereton, R.G., Soini, H.A., Novotny, M.V., Oberzaucher, E., Grammer, K. and Penn, DJ. (2007) Pattern recognition of gas chromatography mass spectrometry of human volatiles in sweat to distinguish the sex of subjects and determine potential markers. Chemometrics Intell. Lab. Systems. In press. 

Sommerville, B.A., McCormick, J.P. and Broom D.M. (1994) Analysis of human sweat volatiles -an example of pattern-recognition in the analysis and interpretation of gas chromatograms. Pestic. Sci. 41, 365-368. 

Quality Assurance Applications of Pattern Recognition to Human Monitoring Data... 

Data have been collected since 1970 on the prevalence and levels of various chemicals in human adipose (fat) tissue. These data are stored on a mainframe computer and have undergone routine quality assurance/quality control checks using univariate statistical methods. Upon completion of the development of a new analysis file, multivariate statistical techniques are applied to the data. The purpose of this analysis is to determine the utility of pattern recognition techniques in assessing the quality of the data and its ability to assist in their interpretation. 

For example, a single estimate for total PCB s has been historically collected in the NHATS program. Current advances in chemical analysis protocols now allow for the determination of isomer specific resolution of PCB s. Given the 209 PCB s that are now possible to detect, an adequate evaluation of the data without the use of pattern recognition techniques seems impossible. From a QA/QC perspective, these methods can facilitate the detection of outliers and aid in the interpretation of human chemical residue data. The application of statistical analysis must keep abreast with these advances made in chemisty. To handle the complexity and quantity of such data, the use of more sophisticated statistical analyses is needed. 

Work is continuing on the application of pattern recognition to the human monitoring data base to assist in the identification and interpretation of potential underlying structures associated with this data base. 

Pattern recognition methods have been used for the description of air pollution in the industrialized region at the estuary of the river Rhine near Rotterdam. A selection of about eight chemical and physical-meteorological features offers a possibility for a description that accounts for out 70% of the information that is ccmprised in these features with two parameters only. Prediction of noxious air situations scmetimes succeeds for a period of at most four hours in advance. Seme-times, hewever, no prediction can be made. Investigations pertaining to the correlation between air conpo-sition and complaints on bad smell by inhabitants of the area show that, apart frem physical and chemical descriptors, other features are also involved that depend on human perception and bdiaviour. 

Because < this well-developed ability to perceive shapes, chemists often use picturesaa present their data. For example, in spectroscopy, a spectrum is plotted as arontinuous curve rather than represented in tabular form. Tlie human eye can perceive the presence or absence of peaks, and interpretations are made accardingly. Using the computer to perceive" these sliapes or to enhance recogrftion abilities is the goal of pattern recognition. 

Data, computer versus human view, 56-58 Data sets, see Example data sets DCLS, seeDirea da ical least squares (DCLS) Decision trees defining the problem, 9-11 muiiivaiiate calibration, 186-188 pattern recognition. 62-64 Definitions. 5-7 Degrees of freedom PLS F-test. 304 SLMCA F-test. 152-153 Dendrogram construaion of. 65-71 definition of 65... 

Hierarchical cluster analysis (HCA) is an unsupervised technique that examines the inteipoint distances between all of the samples and represents that information in the form of a twcKlimensional plot called a dendrogram. These dendrograms present the data from high-dimensional row spaces in a form that facilitates the use of human pattern-recognition abilities. 

There is only one liew of the data which is presented—the dendrogram. There is no interactive wa of manipulating the dendrogram to allow the user to explore the data using human pattern-recognition capabilities. 

CHERNOFF [1973] created an unusual graphical representation of multivariate data. Fie made the assumption that the human pattern recognition ability is best trained with human faces. Faces can be described with parameters like face width, ear level, half-face height, eccentric upper face, eccentric lower face, nose length, mouth centering, etc. 

Lasch, P. and Naumann, D. (1998) FT-IR microspectroscopic imaging of human carcinoma in thin sections based on pattern recognition techniques. Cell. Mol. Biol. 44(1), 189-202. 

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