SHI MIN

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engshim@nus.edu.sg


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    A machine learning approach for the duration of biomedical literature
    (2003) Shi, M.; Edwin, D.S.; Menon, R.; Shen, L.; Lim, J.Y.K.; Loh, H.T.; Keerthi, S.S.; Ong, C.J.; MECHANICAL ENGINEERING
    In the field of the biomedical sciences there exists a vast repository of information located within large quantities of research papers. Very often, researchers need to spend considerable amounts of time reading through entire papers before being able to determine whether or not they should be curated (archived). In this paper, we present an automated text classification system for the classification of biomedical papers. This classification is based on whether there is experimental evidence for the expression of molecular gene products for specified genes within a given paper. The system performs pre-processing and data cleaning, followed by feature extraction from the raw text. It subsequently classifies the paper using the extracted features with a Naïve Bayes Classifier. Our approach has made it possible to classify (and curate) biomedical papers automatically, thus potentially saving considerable time and resources. The system proved to be highly accurate, and won honourable mention in the KDD Cup 2002 task 1. © Springer-Verlag Berlin Heidelberg 2003.
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    Triangular mesh generation employing a boundary expansion technique
    (2006-08) Shi, M.; Zhang, Y.F.; Loh, H.T.; Bradley, C.; Wong, Y.S.; MECHANICAL ENGINEERING
    This paper presents a triangulation method for modelling very large sets of cloud data. The three-dimensional (3D) data sets are produced by a machine vision system and/or coordinate measuring machine (CMM). The algorithm is suitable for processing the data collected from objects composed of free form surface patches especially with interior holes. This is accomplished from the 3D data sets in two steps. Firstly, the original cloud data is reduced into a simplified data set employing a data reduction technique (voxel binning method), in which the error between the cloud data and the meshed surface is used to control the data reduction. Secondly, the triangulation process starts with a randomly selected seed triangle. The triangular mesh extends outward by continuously linking suitable external points to it along the boundary edges of the meshed area. A complex free form surface with interior holes can be triangulated in one computing session without manually dividing it into several simple patches. The error-based data reduction parameters are extracted from the cloud data set, by a series of local surface patches, and the required spatial error between the final triangulation and the cloud data. Experimental results are given to illustrate the efficacy of the technique for rapidly constructing a geometric model from 3D digitised cloud data.