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Taipei, Taiwan

Aletheia University is a private university in Tamsui, New Taipei City and Madou, Tainan in Taiwan. It was founded by George Leslie Mackay as Oxford College. It has close links to the Presbyterian Church in Taiwan, and is one of the oldest institutions of higher education in Taiwan. Wikipedia.

Chiang W.-Y.,Aletheia University
Expert Systems with Applications | Year: 2011

This paper proposes a new procedure and an improved model to mine association rules of customer values. The market of online shopping industry in Taiwan is the research area. Research method adopts Ward's method to partition online shopping market into three markets. Customer values are refined from an improved RFMDR model (based on RFM/RFMD model). Supervised Apriori algorithm is employed with customer values to create association rules. These effective rules are suggested to apply on a customized marketing function of a CRM system for enhancing their customer values to be higher grades. © 2010 Elsevier Ltd. All rights reserved.

Yang M.-C.,Aletheia University
Information Sciences | Year: 2010

The use of edge-disjoint spanning trees or independent spanning trees in a network for data broadcasting has the benefits of increased of bandwidth and fault-tolerance. In this paper, we propose an algorithm which constructs n edge-disjoint spanning trees in the n-dimensional twisted cube, denoted by TQn. Because the n-dimensional twisted cube is n-regular, the result is optimal with respect to the number of edge-disjoint spanning trees constructed. Furthermore, we also show that n2 of the n edge-disjoint spanning trees constructed are independent spanning trees. This algorithm runs in time O(N log N) and can be parallelized to run in time O(log N) where N is the number of nodes in TQn. © 2010 Elsevier Inc. All rights reserved.

Huang C.-J.,Aletheia University
International Journal of Digital Content Technology and its Applications | Year: 2011

At the moment, Support Vector Machine (SVM) has been widely used in the study of stock investment related topics. Stock investment can be further divided into three strategies such as: buy, sell and hold. Using data concerning China Steel Corporation, this article adopts genetic algorithm for the search of the best SVM parameter and the selection of the best SVM prediction variable, then it will be compared with Logistic Regression for the classification prediction capability of stock investment. From the classification prediction result and the result of AUC of the models presented in this article, it can be seen that the SVM after adjustment of input variables and parameters will have classification prediction capability relatively superior to that of the other three models.

Chen C.-J.,Aletheia University
International Journal of Machine Learning and Cybernetics | Year: 2012

With the trend toward taller and more flexible building structures, the use of vibration control devices, passive as well as active, as means of structural protection against strong wind and earthquakes have received significant attention in recent years. A mass-damper shaking table system has been considered as means for vibration suppression to external excitation and disturbances. No explicitly system identification of the plant dynamics, no membership function and thus no fuzzification-defuzzification operation are required. For effective control performance, a neural classifier controller with genetic algorithm is developed. Compared with the conventional neural network and fuzzy controller, the neural classifier controller using genetic algorithm has been presented with the effectiveness of the vibration suppression control. Experimental results show that the neural classifier controller remains effective for building structure vibration suppression under free vibration and forced vibration excitation. © 2011 Springer-Verlag.

Yang M.-C.,Aletheia University
Information Sciences | Year: 2013

Diagnosability is a critical metric for determining the reliability of a multiprocessor system. In 2005, Lai et al. proposed a new measure for the fault diagnosis of a system, i.e., conditional diagnosability, in which it is assumed that at least one of the neighbors of an arbitrary node in the system is not faulty. In this paper, we obtain a sufficient condition for a class of networks, called Matching Composition Networks (MCNs), which are conditionally t-diagnosable under the MM* model. Then, we apply the sufficient condition to show the conditional diagnosability of bijective connection (BC) networks. Finally, we show that the sufficient condition can be applied to networks other than BC networks. © 2013 Elsevier Inc. All rights reserved.

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