Electrical Conductivity Based Flow Regime Recognition of Two-phase Flows in Horizontal pipeline

An experimental method of resolving flow regimes by utilizing the conductivity data measured by Electrical Resistance Tomography (ERT) is presented. The method applies Boolean logic and frequency analysis of the ERT signal in order to identify five typical flow regimes in horizontal pipe namely: bubble, plug, slug, stratified and annular. The relative conductivity signal obtained from the tomograms is converted to binary form in order to perform Boolean logical operation with the binary templates of typical flow patterns. The overall conductivity of the tomogram is used to extract frequency information of the flow. Flow pattern is identified by the statistical analysis of the combination of this information. The recognition method was evaluated using experimental data from horizontal pipeline for different flow conditions. The identification of the flow regimes from the method was verified using the conductivity images from ERT

Stochastic-Based Pattern Recognition Analysis

In this work we review the basic principles of stochastic logic and propose its application to probabilistic-based pattern-recognition analysis. The proposed technique is intrinsically a parallel comparison of input data to various pre-stored categories using Bayesian techniques. We design smart pulse-based stochastic-logic blocks to provide an efficient pattern recognition analysis. The proposed rchitecture is applied to a specific navigation problem. The resulting system is orders of magnitude faster than processor-based solutions

Word shape analysis for a hybrid recognition system

This paper describes two wholistic recognizers developed for use in a hybrid recognition system. The recognizers use information about the word shape. This information is strongly related to word zoning. One of the recognizers is explicitly limited by the accuracy of the zoning information extraction. The other recognizer is designed so as to avoid this limitation. The recognizers use very simple sets of features and fuzzy set based pattern matching techniques. This not only aims to increase their robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. Letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. Despite some remaining disambiguation problems, wholistic recognizers are found capable of outperforming the segmentation based recognizer. When working together in a hybrid system, the results are significantly higher than that of the individual recognizers. Recognition results are reported and compared