• Journal of KIISE:Computer Systems and Theory
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• v.27 no.7
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• pp.619-628
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• 2000

Abstract Perspective views provide a powerful depth cue and thus aid the interpretation of complicated images. The main drawback of current perspective volume rendering is the long execution time. In this paper, we present an efficient perspective volume rendering algorithm based on coherency between rays. Two sets of templates are built for the rays cast from horizontal and vertical scanlines in the intermediate image which is parallel to one of volume faces. Each sample along a ray is calculated by interpolating neighboring voxels with the pre-computed weights in the templates. We also solve the problem of uneven sampling rate due to perspective ray divergence by building more templates for the regions far away from a viewpoint. Since our algorithm operates in object-order, it can avoid redundant access to each voxel and exploit spatial data coherency by using run-length encoded volume. Experimental results show that the use of templates and the object-order processing with run-length encoded volume provide speedups, compared to the other approaches. Additionally, the image quality of our algorithm improves by solving uneven sampling rate due to perspective ray di vergence.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.907-920
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• 2015

Charts based on geometric distribution are effective to monitor the proportion of nonconforming items in high-quality processes where the in-control proportion nonconforming is low. The implementation of this chart is often based on the assumption that in-control proportion nonconforming is known or accurately estimated. However, accurate parameter estimation is very difficult and may require a larger sample size than that available in practice for high-quality process where the proportion of nonconforming items is very small. An inaccurate estimate of the parameter can result in estimated control limits that cause unreliability in the monitoring process. The maximum likelihood estimator (MLE) is often used to estimate in-control proportion nonconforming. In this paper, we recommend a Bayes estimator for the in-control proportion nonconforming to incorporate practitioner knowledge and avoid estimation issues when no nonconforming items are observed in the Phase I sample. The effects of parameter estimation on the geometric chart and the geometric CUSUM chart are considered when the MLE and the Bayes estimator are used. The results show that chart performance with estimated control limits based on the Bayes estimator is generally better than that based on the MLE.