• Transactions of Materials Processing
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• v.13 no.1
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• pp.90-101
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• 2004

An inverse mosaic method has been proposed to generate an initial blank shape from the final product shape. Differently from the geometric mapping method, the method can handle triangular patches. However, the generated blank shape is strongly dependent on the order of determination of nodes. In order to compensate the dependency error smoothing technique has been also developed. Although the accuracy has been improved greatly compared with the geometrical mapping method, the method has limitation, due to the no incorporation of plasticity theory. Even though the accuracy of the radius vector method is already proved. the method requires initial guess to start the method. In order to compromise the limitation of the present method and the radius vector method, the method has been connected to the radius vector method. The efficiency of the present optimal blank design method has been verified with some chosen examples.

• Korean Journal of Remote Sensing
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• v.26 no.4
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• pp.373-385
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• 2010

A new method to mosaic reflectivity over the overlapped coverage of two radars was developed. The method mosaics the radar reflectivity with weights after adjustment of reflectivity differences on overlapped coverage of neighboring two radars. Their weights are inverse proportion to the difference between the height of an interpolated reflectivity and the level of CAPPI (Constant Altitude PPI). The performance of this method was compared to different mosaic methods (Mosaics by maximum value, averaged value, nearest value and distance weighted value) using the reflectivity fields of a typhoon event observed by two radar. New method was better than any other methods either as a continuity and as a bias analysis of reflectivity at the boundaries in overlapped coverage by two radars.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.483-488
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• 2015

This paper presents a local binary pattern(LBP) for effectively classifying textures, which is based on the multidimensional intensity difference between the adjacent pixels in the block image. The intensity difference by considering the a extent of 4 directional changes(verticality, horizontality, diagonality, inverse diagonality) in brightness between the adjacent pixels is applied to reduce the computation load as a results of decreasing the levels of histogram for classifying textures of image. And the binary patterns that is represented by the relevant intensities within a block image, is also used to effectively classify the textures by accurately reflecting the local attributes. The proposed method has been applied to classify 24 block images from USC Texture Mosaic #2 of 128*128 pixels gray image. The block images are different in size and texture. The experimental results show that the proposed method has a speedy classification and makes a free size block images classify possible. In particular, the proposed method gives better results than the conventional LBP by increasing the range of histogram level reduction as the block size becomes larger.