• Tunnel and Underground Space
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• v.11 no.1
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• pp.36-41
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• 2001

It is often said that the supply of fossil fuels in use for energy source will last only for 40 years. Futhermore, statistics shows that most of the fuels are imported from outside and that 30-40% of total cost for housing in Korea. One of those methods for reducing the energy cost for housing is to use underground space. Being used well, it may bring a considerable saving of energy since the underground space keeps its air cool in summer and warm in winter. To use underground space, we need to analyse its temperature first. For this purpose, what is generally used is the numerical analysis with the use of nodal system. That is, we can calculate a specific underground temperature with the matrix of thermal resistance after we make a nodal system. However, the existing numerical analysis programs need usually high cost and require a computer with large capacity. So they are seldom used in practice. Considering such problems, this study seeks to find a method for making the matrix of thermal resistance operatable on PC level.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1278-1290
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• 2008

This paper proposes a preprocessing method and a neural network based character recognizer to enhance the overall performance of the license plate recognition system. First, plate outlines are extracted by virtual line matching, and then the 4 vertexes are obtained by calculating intersecting points of extracted lines. By these vertexes, plate image is reconstructed as rectangle-shaped image by bilinear transform. Finally, the license plate is recognized by the neural network based classifier which had been trained using delta-bar-delta algorithm. Various license plate images were used in the experiments, and the proposed plate normalization enhanced the recognition performance up to 16 percent.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.5
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• pp.510-517
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• 2001

This paper describes an initial study for the application of eddy current pattern recognition approaches to more realistic flaw characterization in steam generator tubes. For this purpose, finite-element model-based theoretical eddy current testing (ECT) signals are simulated from 5 types of OD flaws with the variation in flaw size parameters and testing frequency. In addition, three kinds of software are developed for the convenience in the application of steps in pattern recognition approaches such as feature extraction feature selection and classification by probabilistic neural networks (PNNs). The cross point of the ECT signals simulated from flaws with non-symmetric cross-sections shows the deviation from the origin of the impedance plane. New features taking advantages of this phenomenon are added to complete the feature set with a total of 18 features. Then, classification with PNNs are performed based on this feature set. The PNN classifiers show high performance for the identification of symmetry in the cross-section of a flaw. However, they show very limited success in the interrogation of the sharpness of flaw tips.