• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.1
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• pp.33-40
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• 2003

Radio frequency management system(RFMS) is being operated to facilitate national spectrum management and monitoring in Korea. To improve the engineering analysis function in RFMS, criteria for the automated selection of the propagation model adequate to the radio station service environment considered are proposed. Those criteria are derived from the specified parameters obtained through the analysis of related ITU-R propagation & diffraction loss models which are to be used in RFMS. Then, using criteria acquired, computer program is made to achieve the automated selection of the most appropriate propagation algorithm, among the ones provided in RFMS, to the environment in which the engineering analysis is required. Furthermore, an illustrative example is shown with the proposals fur increasing the efficiency of the engineering analysis in RFMS.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.281-287
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• 2003

This paper proposed an improvement scheme for effective usage of radio-frequency management system(RFMS), which has been operated to facilitate national spectrum management and monitoring in Korea. Based on the wave propagation models, interference analysis algorithms, and sharing criteria recommended by ITU-R, we derived criteria for the automated selection of the channel interference analysis algorithms and sharing conditions adequate to the environment to be analysed. Then using the obtained criteria, computer and program has been made and shown to select the most appropriate propagation models, interference analysis algorithms, and sharing criteria from the ones provided in RFMS, with the illustrative example.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.