• Journal of Energy Engineering
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• v.27 no.4
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• pp.1-12
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• 2018

The code and standards related to fuel cells were analyzed to derive the SOFC(Solid Oxide Fuel Cell) stack safety performance evaluation items and evaluation methode. Safety performance evluation of the SOFC stack was tested by quoting derived test items. The stack used in the test is an anode-supported type 2 Cell stack (Active surface area : 220cm) manufactured by MICO Inc, and SOFC stack safety performance evaluation system used for the test is self-manufactured. We conducted a leakage test, current voltage characteristic test, rated output test, and power response characteristics test. In the safety performance evaluation test, the stack showed no gas leakage, the maximum output and rated output was recorded to 65.6 W(1.41 V, 46.5 A, $422mA/cm^2$), 62.3 W(1.57 V, 40 A, $363mA/cm^2$). In the power response characteristics test verified that the output is kept stable within two seconds. At the maximum load (40 A) and the minimum load (8 A), the output was recorded 62 W and 16W in $750^{\circ}C$. This study will contribute to the universalization and to provide much safe environment of operating the solid oxide fuel cell system.

• 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.