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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.techfore.2010.03.013 Byline: Hsiao-Chun Wu (a), Hung-Yi Chen (b), Kung-Yen Lee (c) Keywords: Core technology capability; Semiconductor industry; Patent analysis; Patent grouping Abstract: Patent information provides an objective and public source to understand core technologies of companies. In this paper, a method is proposed for identifying core technology capabilities for a company in the semiconductor industry. The method is built on the patent similarity and K-means clustering algorithm. The indirect relations among the patents in the complex industry are considered in the method. In addition, the age of the patent is taken into account to avoid identifying aged core patents. The method is demonstrated by exploring the core technology capabilities that support the platform technology portfolio of the Taiwan Semiconductor Manufacturing Company (TSMC). Author Affiliation: (a) Department of Business Administration, Chaoyang University of Technology, Wufong Township Taichung County, Taiwan, ROC (b) Department of Information Management, Chaoyang University of Technology, Wufong Township Taichung County, Taiwan, ROC (c) Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei, Taiwan, ROC Article History: Received 27 July 2009; Revised 24 March 2010; Accepted 25 March 2010

Abstract: Multiscale entropy (MSE) is a prevalent algorithm used to measure the complexity of a time series. Because the coarse-graining procedure reduces the length of a time series, the conventional MSE algorithm applied to a short-term time series may yield an imprecise estimation of entropy or induce undefined entropy. To overcome this obstacle, the modified multiscale entropy (MMSE) was developed. The coarse-graining procedure was replaced with a moving-average procedure and a time delay was incorporated for constructing template vectors in calculating sample entropy. For conducting short-term time series analysis, this study shows that the MMSE algorithm is more reliable than the conventional MSE. [Copyright &y& Elsevier]

Abstract: Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series that has been applied in many fields successfully. However, MSE may yield an inaccurate estimation of entropy or induce undefined entropy because the coarse-graining procedure reduces the length of a time series considerably at large scales. Composite multiscale entropy (CMSE) was recently proposed to improve the accuracy of MSE, but it does not resolve undefined entropy. Here we propose a refined composite multiscale entropy (RCMSE) to improve CMSE. For short time series analyses, we demonstrate that RCMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy. [Copyright &y& Elsevier]