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Chien Y.-H.,National Taichung University of Science and Technology
IEEE Transactions on Reliability | Year: 2012

This paper presents the effects of a renewing free-replacement warranty (RFRW) on the age-replacement policy in a discrete time process. Consider a product that should be operating over an indefinitely long operation cycle n(n=1,2,⋯); under the discrete age-replacement policy, a product is replaced at cycle N(N=1,2,⋯) after its installation, or at failure, whichever occurs first. The cost models from the customer's perspective are developed for both warranted, and non-warranted products. The corresponding optimal replacement age N* (i.e., the optimal number of operation cycles for a preventive replacement) is derived such that the long-run expected cost rate is minimized. Under the assumption of the discrete time increasing failure rate, the existence and uniqueness of the optimal N* are shown, and the impacts of a RFRW on the optimal replacement policies are investigated analytically. The optimal N* for a warranted product is closer to the end of the warranty period than for a non-warranted product. Finally, a numerical example is demonstrated for the optimal policy illustration and verification. The observations from the technical analysis and numerical results provide valuable information for a buyer (user) to adjust the optimal age-replacement policy if a product is operating in discrete time under a RFRW. © 2012 IEEE. Source

Lin H.-Y.,National Taichung University of Science and Technology
Decision Support Systems | Year: 2012

Classification problems have become more complex and intricate in modern applications in the face of continuous data explosion. In addition to great quantities of features and large numbers of instances, modern classification applications are continuously developed with multiple classes (objectives). The ever-increasing growth in data quantity and computation complexity has largely deteriorated the performance and accuracy of classification models. In order to deal with such situations, multivariate statistical analyses are adopted in this paper. Multivariate statistical analyses have two advantages. First, they can explore the relationships between variables and find the most characterizing features of the observed data. Second, they can solve problems which are stalled by high dimensionality. In this paper, the first advantage is applied to the selection of relevant features and the second is employed to generate the multivariate classifier. Experimental results show that our model can significantly improve classification training time by combining a compact subset of relevant features without the loss of accuracy in multi-class classification problems. In addition, the discrimination degree of our classifier outperforms other conventional classifiers. © 2012 Elsevier B.V. All rights reserved. Source

Lin H.-Y.,National Taichung University of Science and Technology
Knowledge-Based Systems | Year: 2013

Feature selection is an essential problem for pattern classification systems. This paper studies how to provide systems with the most characterizing features for ordinal multi-class classification task. The integration of cluster analyses and variability analyses advances a novel feature selection scheme with efficiency. The Huang-index method using fuzzy c-means is employed to enhance cluster validity and optimizes a consistent number of clusters among the features. A new entropy-based feature evaluation method is formulated for the authentication of relevant features. Then, multivariate statistical analyses are utilized to solve the redundancy between relevant features. Experimental results show that our new feature selection scheme sifts successfully a compact subset of characterizing features for classification problems with multiple classes. © 2012 Elsevier B.V. All rights reserved. Source

Chiang H.-S.,National Taichung University of Science and Technology
Online Information Review | Year: 2013

Purpose - Although increasing numbers of users have begun to use social networking sites (SNSs), the user growth of a few SNSs continues to decrease. Therefore identifying factors that influence users' intention to adopt and continuously use a particular SNS is a critical issue. To explore the factors, this study aims to apply the theories of reasoned action, uses and gratifications, and innovation diffusion to explain why people continue to join SNSs. Design/methodology/approach - The study participants were members of Facebook in Taiwan. An online questionnaire was used to conduct empirical research, and the data of 348 respondents were analysed using the partial least squares regression approach. Findings - It was found that the reasons why people continuously use SNSs vary with different innovation diffusion stages. In particular attitude toward SNSs had the strongest direct effect on continuous intention while the impact of social norms was not significant in the different innovation diffusion stages. Practical implications - From a practical perspective, insights provided by the study can help SNS developers understand user motivation and thus design more effective marketing strategies. Originality/value - The proposed model provides an improved understanding of the needs of different SNS users, and testing verified the effects of the factors related to gratification and innovation diffusion. © Emerald Group Publishing Limited. Source

Chen C.-C.,Tunghai University | Huang T.-C.,National Taichung University of Science and Technology
Computers and Education | Year: 2012

Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the digital world. Although learning outside of the traditional classroom is an innovative teaching approach, the two main problems are the lack of proper learning strategies and the capacity to acquire knowledge on subjects effectively. To manage these problems, this study proposes a context-aware ubiquitous learning system (CAULS) based on radio-frequency identification (RFID), wireless network, embedded handheld device, and database technologies to detect and examine real-world learning behaviors of students. A case study of an aboriginal education course was conducted in classrooms and at the Atayal u-Museum in Taiwan. Participants included elementary school teachers and students. We also designed and used a questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT) theory to measure the willingness for adoption or usage of the proposed system. The experimental results demonstrated that this innovative approach can enhance their learning intention. Furthermore, the results of a posttest survey revealed that most students' testing scores improved significantly, further indicating the effectiveness of the CAULS. © 2012 Elsevier Ltd. All rights reserved. Source

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