• International Journal of CAD/CAM
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• v.4 no.1
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• pp.11-17
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• 2004

In this paper, an exponential bound of the distance between a Loop subdivision surface and its control mesh is derived based on the topological structure of the control mesh. The exponential bound is independent of the process of recursive subdivisions and can be evaluated without subdividing the control mesh actually. Using the exponential bound, we can predict the depth of recursion within a user-specified tolerance as well as the error bound after n steps of subdivision. The error-estimating approach can be used in many engineering applications such as surface/surface intersection, mesh generation, NC machining, surface rendering and the like.

• International Journal of CAD/CAM
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• v.4 no.1
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• pp.47-53
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• 2004

The subdivision surface is the limit of recursively refined polyhedral mesh. It is quite intuitive that the multi-resolution feature can be utilized to simplify generation of NC (Numerical Control) tool paths for rough machining. In this paper, a new method of parallel NC tool path generation for subdivision surfaces is presented. The basic idea of the method includes two steps: first, extending G-Buffer to a strip buffer (called S-Buffer) by dividing the working area into strips to generate NC tool paths for objects of large size; second, generating NC tool paths by parallel implementation of S-Buffer based on MPI (Message Passing Interface). Moreover, the recursion depth of the surface can be estimated for a user-specified error tolerance, so we substitute the polyhedral mesh for the limit surface during rough machining. Furthermore, we exploit the locality of S-Buffer and develop a dynamic division and load-balanced strategy to effectively parallelize S-Buffer.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.272-277
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• 2008

Symmerge is a stable minimum storage merging algorithm that needs $O(m{\log}{\frac{n}{m}})$ element comparisons, where in and n are the sizes of the input sequences with $m{\leq}n$. Hence, according to the lower bound for merging, the algorithm is asymptotically optimal regarding the number of comparisons. The Symmerge algorithm is based on the standard recursive technique of "divide and conquer". The objective of this paper is to consider the relationship between m and n for the degenerated case where the recursion depth reaches m-1.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.429-440
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• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.