In this paper, an expert system (ES) approach is adopted to develop a computer based intelligent technique for interpretation of minor impulse faults in distribution transformers.
For those in biomedical imaging, the goal of past research has been to develop image understanding techniques for the interpretation of x-ray, magnetic resonance imaging (MRI), or other radiological data.
These studies employed advanced statistical techniques for data interpretation including β-expectation tolerance intervals (BETI) and β-content γ-confidence tolerance intervals.
The agent roles integrate statistical techniques to select relevant variations and visualization techniques for the interpretation of the final results and to extract relevant information from different sources of information by applying a CBR system.
The fact that proteins are composed of a finite set of amino acids has allowed for the development of elaborate mathematical techniques for the interpretation of their fragmentation patterns.
Visualization techniques for the interpretation of matrix data can, however, also help to find matrix properties which allow reducing the storage size, such as partial symmetry or the occurrence of many near-zero transition probabilities.
Zingmond [ 45] has applied a semantic encoding tool to these reports to recognize abnormalities that should receive follow-up, and Friedman has studied techniques for encoding interpretations found in these reports [ 46- 50].
Chemometric techniques commonly utilized for interpretation and analysis of NIR spectroscopic applications have previously been critically reviewed [ 15].
Beiki and Pedersen (2011) developed a data window constrained two-dimensional (2D) inversion technique for the interpretation of gravity gradient tensor data using dike/sheet and contact models.
Sharma and Biswas (2013) introduced a very fast simulated-annealing (VFSA) global optimization technique for the interpretation of a SP anomaly measured over a 2D inclined sheet-type structure.
