Local site conditions play an important role in the effective application of strong motion recordings.In the China National Strong Motion Observation Network System(NSMONS),some of the stations do not provide boreho...Local site conditions play an important role in the effective application of strong motion recordings.In the China National Strong Motion Observation Network System(NSMONS),some of the stations do not provide borehole information,and correspondingly,do not assign the site classes yet.In this paper,site classification methodologies for free-field strong motion stations are reviewed and the limitations and uncertainties of the horizontal-to-vertical spectral ratio(HVSR) methods are discussed.Then,a new method for site classification based on the entropy weight theory is proposed.The proposed method avoids the head or tail joggle phenomenon by providing the objective and subjective weights.The method was applied to aftershock recordings from the 2008 Wenchuan earthquake,and 54 free-field NSMONS stations were selected for site classification and the mean HVSRs were calculated.The results show that the improved HVSR method proposed in this paper has a higher success rate and could be adopted in NSMONS.展开更多
Based on the text orientation classification, a new measurement approach to semantic orientation of words was proposed. According to the integrated and detailed definition of words in HowNet, seed sets including the w...Based on the text orientation classification, a new measurement approach to semantic orientation of words was proposed. According to the integrated and detailed definition of words in HowNet, seed sets including the words with intense orientations were built up. The orientation similarity between the seed words and the given word was then calculated using the sentiment weight priority to recognize the semantic orientation of common words. Finally, the words' semantic orientation and the context were combined to recognize the given words' orientation. The experiments show that the measurement approach achieves better results for common words' orientation classification and contributes particularly to the text orientation classification of large granularities.展开更多
基金National Key Technology R&D Program Under Grant No.2009BAK55B05Nonprofit Industry Research Project of CEA Under Grant No.201108003Science Foundation of Institute of Engineering Mechanics,CEA Under Grant No.2010C01
文摘Local site conditions play an important role in the effective application of strong motion recordings.In the China National Strong Motion Observation Network System(NSMONS),some of the stations do not provide borehole information,and correspondingly,do not assign the site classes yet.In this paper,site classification methodologies for free-field strong motion stations are reviewed and the limitations and uncertainties of the horizontal-to-vertical spectral ratio(HVSR) methods are discussed.Then,a new method for site classification based on the entropy weight theory is proposed.The proposed method avoids the head or tail joggle phenomenon by providing the objective and subjective weights.The method was applied to aftershock recordings from the 2008 Wenchuan earthquake,and 54 free-field NSMONS stations were selected for site classification and the mean HVSRs were calculated.The results show that the improved HVSR method proposed in this paper has a higher success rate and could be adopted in NSMONS.
基金supported by the National Natural Science Foundation of China (50375010).
文摘Based on the text orientation classification, a new measurement approach to semantic orientation of words was proposed. According to the integrated and detailed definition of words in HowNet, seed sets including the words with intense orientations were built up. The orientation similarity between the seed words and the given word was then calculated using the sentiment weight priority to recognize the semantic orientation of common words. Finally, the words' semantic orientation and the context were combined to recognize the given words' orientation. The experiments show that the measurement approach achieves better results for common words' orientation classification and contributes particularly to the text orientation classification of large granularities.