• Applied Chemistry for Engineering
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• v.19 no.6
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• pp.609-616
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• 2008

The present study is concerned with a simple numerical method for estimating the hindered settling velocity of noncolloidal suspensions with bidisperse size distribution of particles. The method is based on an effective-medium theory which uses the conditional ensemble averages for describing the velocity fields or other physical quantities of interest in the suspension system with the particles randomly placed. The effective-medium theory originally developed by Acrivos and Chang[1] for monodisperse suspensions is modified for the bidisperse case. Using the radial distribution functions and stream functions the hindered settling velocity of the suspended particles is calculated numerically. The predictions by the present method are compared with the previous experimental results by Davis and Birdsell[2] and Cheung et al.[3]. It is shown that the estimations by the effective-medium model of the present study reasonably agree with the experimental results.

• Korea-Australia Rheology Journal
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• v.17 no.1
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• pp.27-32
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• 2005

We determine the effective viscosity of suspensions with bidisperse particle size distribution by modifying an effective-medium theory that was proposed by Acrivos and Chang (1987) for monodisperse suspensions. The modified theory uses a simple model that captures some important effects of multi-particle hydrodynamic interactions. The modifications are described in detail in the present study. Estimations of effective viscosity by the modified theory are compared with the results of prior work for monodisperse and bidisperse suspensions. It is shown that the estimations agree very well with experimental or other calculated results up to approximately 0.45 of normalized particle volume fraction which is the ratio of volume faction to the maximum volume fraction of particles for bidisperse suspensions.

• Journal of KIISE
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• v.43 no.10
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• pp.1085-1093
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• 2016

Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.