Browsing by Author "Chang, KC"

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Body force effect on consolidation of porous elastic media due to pumping
(2006-01-01)
Tsai, TL
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Chang, KC
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Huang, LH
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防災與水環境研究中心
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Disaster Prevention and Water Environment Research Center
In this study, the linear poro-elasticity theory is applied to examine the body force effect on consolidation of porous media due to pumping. The steady-state solutions of displacement and incremental effective stress for a stratum of clay sandwiched between sandy strata are analytically given. The effect of body force could be represented by the body force number that depends on Lame's constants, porosity, and the thickness of porous media. The consolidation of clay is significantly related not only to the body force number but also to the ratio of water table depression in upper and lower sandy strata due to pumping. The neglect of body force will severely underestimate the displacement and incremental effective stress when porous media are soft or thick, or both. This might lead to potential flaws in engineering practice, such as the calculation of soil settlement.
Machine learning by imitating human learning
(1996-05-01)
Chang, KC
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Hong, TP
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Tseng, SS
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資訊工程學系
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Department of Computer Science
Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances and rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments.
Sampling time effect on 2,4-Toluene diisocyanate concentration by using different samplers
(2006)
Tsai, CJ
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Lin, HC
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Lin, KY
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Shih, TS
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Chang, KC
In order to understand the sampling time effect on the 2,4-TDI (Toluene diisocyanate) concentrations, laboratory and field tests were conducted in this study. An ADS (annular denuder sampler) and two OFFCs (open face filter holders) with different filters were tested for 1 to 120 minutes in the laboratory using 2,4-TDI gas. In the field study, the standard sampling method, the dual filter the triple filter and annular denuder systems were used at two workplaces to study the change of 2,4-TDI concentrations with sampling time from 15 to 60 minutes. The test results in both laboratory and field studies show that the sampling time influences the sampled TDI concentration considerably which may be due to reaction of TDI with water vapor and polyo in the sampling process. It is evident that as sampling time increases the TDI concentration decreases very significantly.