• Journal of Information Technology Applications and Management
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• v.29 no.6
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• pp.135-144
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• 2022

In this study, we transformed 4680 type lithium-ion batteries to 3-dimensional CAD models and present a methodology to detect defects using Radon inverse transformation. Transparency was applied to the model to make it look like a CT image when viewed from the front. One normal and three defect models were created and analyzed. The models were saved as image files while rotating at a certain angle. Then, we used the Radon inverse transformation to reconstruct the original 3D geometry from the image files. Finally, we successfully found defects in the defect models for three cases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.123-131
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• 2012

As the price of traditional fuels soar, the alternatives are becoming more viable. And manufacturers are promoting the growing viability of electric and biofuel-powered vehicles through longer warranties. Now, these longer green environment (emission)warranties, sometimes called extended warranties or "super warranties," have been adapted. The main result of this paper is to present a new method to approximate a bivariate warranty function by using Radial Basis Function Network with application of Radon Transform and its inverse which is used to reduce the dimension of the warranty space. This method consist of the following stages: First, by using the Radon Transform, the bivariate warranty function can be reduced to one dimensional function. Second, each of the one dimensional functions is approximated by using neural network technique into neural sub-networks. Third, these neural sub-networks are combined together to form the final approximation neural network. Four, by using the inverse of radon transform to this final approximation neural network we get the approximation to the given function. Also, we apply the above method to some green warranty data of automotive vehicle company.