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Taipei, Taiwan

Huafan University is an institute of higher education founded by members of the Buddhist community in Shiding District, New Taipei, Taiwan. The university consists of 4 colleges, 12 departments, 12 graduate institutes, and 5 research centers. Wikipedia.


This study investigates the variations in the critical conditions for debris-flow occurrence before and after the Chi-Chi earthquake in the Chen-Yu-Lan watershed, Taiwan. Topographical and rainfall parameters such as the gully gradient, drainage area, rainfall intensity, cumulative rainfall, and rainfall duration in the Chen-Yu-Lan watershed were used to analyze the conditions of debris-flow occurrence over the past 25 years. A recovery equation was proposed on the basis of rainfall parameters and used to determine the variations in the critical line of rainfall that trigger debris flow after the earthquake and to evaluate the recovery period required for the rainfall threshold of debris-flow occurrence after the earthquake to return to that before the earthquake in the watershed. The critical line for the runoff parameter versus gully gradient in the watershed was also presented. © 2011 Springer-Verlag. Source


Lee C.-Y.,National Ilan University | Lee Z.-J.,Huafan University
Applied Soft Computing Journal | Year: 2012

Unbalanced data that are minority classes with few samples presented in many fields. The mean of unbalanced data is difficult to formalize so that traditional algorithms are limited in solving unbalanced data. In this paper, a novel algorithm based on analysis of variance (ANOVA), fuzzy C-means (FCM) and bacterial foraging optimization (BFO) is proposed to classify unbalanced data. ANOVA can measure the difference between the means of two or more groups in which the observed variance is partitioned into components due to various explanatory variables. FCM is a method of fuzzy clustering algorithm that allows one piece of data to belong to two or more clusters. Natural selection tends to eliminate animals with poor foraging strategies and favors the propagation of genes of those animals that have successful foraging strategies. BFO can model the mechanism of natural selection and solve many application problems. The proposed algorithm combines the advantages of ANOVA, FCM and BFO. ANOVA has the ability to select beneficial feature subsets. FCM has the ability to identify data into clusters with certain membership degrees, and BFO has the fast ability to converge to global optima. In this paper, microarray data of ovarian cancer and zoo dataset are used to test the performance for the proposed algorithm. The performance of the proposed algorithm is supported by simulation results. From simulation results, the classification accuracy of the proposed algorithm outperforms other existing approaches. © 2012 Elsevier B.V. Source


Chuang C.-C.,National Ilan University | Lee Z.-J.,Huafan University
Applied Soft Computing Journal | Year: 2011

In this study, a hybrid robust support vector machine for regression is proposed to deal with training data sets with outliers. The proposed approach consists of two stages of strategies. The first stage is for data preprocessing and a support vector machine for regression is used to filter out outliers in the training data set. Since the outliers in the training data set are removed, the concept of robust statistic is not needed for reducing the outliers' effects in the later stage. Then, the training data set except for outliers, called as the reduced training data set, is directly used in training the non-robust least squares support vector machines for regression (LS-SVMR) or the non-robust support vector regression networks (SVRNs) in the second stage. Consequently, the learning mechanism of the proposed approach is much easier than that of the robust support vector regression networks (RSVRNs) approach and of the weighted LS-SVMR approach. Based on the simulation results, the performance of the proposed approach with non-robust LS-SVMR is superior to the weighted LS-SVMR approach when the outliers exist. Moreover, the performance of the proposed approach with non-robust SVRNs is also superior to the RSVRNs approach. © 2010 Elsevier B.V. All rights reserved. Source


Patent
Huafan University | Date: 2015-05-11

The present invention provides a portable sensing and operational device, which uses a sensing module to receive the sensing signal transmitted by the sensor and produce a sensing datum correspondingly. Then an operational circuit operates a first matrix and a second matrix according to the sensing datum. The first matrix corresponds to a plurality of maximum values; the second matrix corresponds to a plurality of minimum values. The operational circuit operates to generate at least a component by decomposing the first matrix and the second matrix. The component is provided to an output circuit for outputting the component to an electronic device.


Patent
Huafan University | Date: 2011-10-20

An operation circuit and an operation method thereof are revealed. The operation circuit includes an extreme value processing unit, a curve processing module, and a component unit. The extreme value processing unit receives and processes a plurality of input data to get maximum values and minimum values. The curve processing module constructs a first matrix and a second matrix according to the maximum and minimum values and then decomposes the first matrix and the second matrix into first submatrices and second submatrices respectively. According to these submatrices, the curve processing module gets at least one mean value function corresponding to the maximum and the minimum values. The computation of a single matrix is reduced by matrix decomposition and operations of the operation circuit. Compared with conventional Gauss matrix manipulations that run by computer systems, the present invention can be applied to simpler circuits by simplifying matrix operation processes.

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