Browsing by Author "Liang, KY"

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Ring embedding in faulty pancake graphs
(2003-06-15)
Hung, CN
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Hsu, HC
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Liang, KY
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Hsu, LH
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資訊工程學系
In this paper, we consider the fault hamiltonicity and the fault hamiltonian connectivity of the pancake graph P-n. Assume that F subset of or equal to V(P-n) boolean OR E(P-n). For n greater than or equal to 4, we prove that P-n - F is hamiltonian if F less than or equal to (n - 3) and P-n - F is hamiltonian connected if F less than or equal to (n - 4). Moreover, all the bounds are optimal. (C) 2003 Elsevier Science B.V. All rights reserved.
Temperature and thermal deformation analysis on scrolls of scroll compressor
(2005-08-01)
Lin, CC
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Chang, YC
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Liang, KY
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Hung, CH
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機械工程學系
The thermal deformation of scrolls significantly affects the hermetic property between scrolls, and thus affects the efficiency of the scroll compressor. An accurate temperature distribution on scrolls must be obtained before the thermal deformation of scrolls can be analyzed. The temperature inside real scrolls in the steady state was experimentally measured. Then, reasonable thermal boundary conditions were applied to perform a heat transfer analysis to determine the temperature distribution on scrolls. Then, the distribution of the thermal deformation on scrolls was analyzed. Finally, fixed and orbiting scrolls were engaged with each other and analyses were performed to determine the stress and deformation distributions at the orbital angle that the maximum compression ratio happens. (c) 2004 Elsevier Ltd. All rights reserved.
Two-stage Welsh's trimmed mean for the simultaneous equations model
(2001-12-01)
Chen, LA
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Liang, KY
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Liu, CC
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統計學研究所
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Institute of Statistics
This paper discusses the large sample theory of the two-stage Welsh's trimmed mean for the limited information simultaneous equations model. Besides having asymptotic normality, this trimmed mean, as the two-stage least squares estimator, is a generalized least squares estimator. It also acts as a robust Aitken estimator for the simultaneous equations model. Examples illustrate real data analysis and large sample inferences based on this trimmed mean.