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Jhongli, Taiwan

Huang R.-Y.,National Central University | Hsu W.-T.,National CentralUniversity
Civil Engineering and Environmental Systems | Year: 2011

To keep pace with global trends in sustainable development, the construction industry worldwide also needs to examine their practices for sustainability. The purpose of this study is to establish a proper indexing system for assessing the performance of a nation in terms of sustainable construction. In this study, a framework for the assessment of state-level sustainable construction is established. The framework consists of five layers, from bottom to top: the indicator; the indicator category; the core cluster; the theme; and the overall performance. The max-min fuzzy Delphi method is employed to identify the proper items of each layer. In addition, the fuzzy analytic hierarchy process is applied to determine the weight of items in each layer. With the developed framework, Sustainable Construction Index of a nation can then be computed to assess its progress in sustainable construction. The result can help a nation to pinpoint areas needing improvement. © 2011 Taylor & Francis. Source

Wang J.-C.,National CentralUniversity | Lin C.-H.,National CentralUniversity | Siahaan E.,National CentralUniversity | Chen B.-W.,National Cheng Kung University | Chuang H.-L.,National CentralUniversity
IEEE Transactions on Industrial Informatics | Year: 2014

In this paper, we present the problem of mixed sound event verification in a wireless sensor network for home automation systems. In home automation systems, the sound recognized by the system becomes the basis for performing certain tasks. However, if a target source is mixed with another sound due to simultaneous occurrence, the system would generate poor recognition results, subsequently leading to inappropriate responses. To handle such problems, this study proposes a framework, which consists of sound separation and sound verification techniques based on a wireless sensor network (WSN), to realize sound-triggered automation. In the sound separation phase, we present a convolutive blind source separation system with source number estimation using time-frequency clustering. An accurate mixing matrix can be estimated by the proposed phase compensation technique and used for reconstructing the separated sound sources. In the verification phase, Mel frequency cepstral coefficients and Fisher scores that are derived from the wavelet packet decomposition of signals are used as features for support vector machines. Finally, a sound of interest can be selected for triggering automated services according to the verification result. The experimental results demonstrate the robustness and feasibility of the proposed system for mixed sound verification in WSN-based home environments. © 2005-2012 IEEE. Source

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