Given the background of a transmission tower erected on a particular mining subsidence area,we used finite element modeling to analyze the anti-deformation performance of transmission towers under a number of differen...Given the background of a transmission tower erected on a particular mining subsidence area,we used finite element modeling to analyze the anti-deformation performance of transmission towers under a number of different load conditions,including horizontal foundation displacement,uneven vertical downward displacement,wind loads and icing conditions.The results show that the failure in stability of a single steel angle iron represents the limit of the tower given ground deformation.We calculated the corresponding limits of foundation displacements.The results indicate that compression displacement of the foundation is more dangerous than tension displacement.Under complex foundation displacement conditions,horizontal foundation displacement is a key factor leading to failure in the stability of towers.Under conditions of compression or tension displacement of the foundation,wind load becomes the key factor.Towers do not fail when foundation displacements are smaller than 1% (under tension) or 0.5% (under horizontal compression or single foundation subsidence) of the distance between two supports.展开更多
Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested o...Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested on Schaefer and Fox type simulated data in three simulated fisheries(declining, well-managed, and restoring fisheries) at two white noise levels. Monte Carlo simulation was conducted to verify the utility of moving averaging(MA), which was an important technique for reducing the effect of noise in data in these models. The relative estimation error(REE) of maximum sustainable yield(MSY) was used as an indicator for the analysis, and one-way ANOVA was applied to test the significance of the REE calculated at four levels of MA. Simulation results suggested that increasing the value of MA could significantly improve the performance of the surplus production model(low REE) in all cases when the white noise level was low(coefficient of variation(CV) = 0.02). However, when the white noise level increased(CV= 0.25), adding the value of MA could still significantly enhance the performance of most models. Our results indicated that the best model performance occurred frequently when MA was equal to 3; however, some exceptions were observed when MA was higher.展开更多
The Autoregressive Moving Average （ARMA） model for whispered speech is proposed. with normal speech, whispered speech has no fundamental frequency because of the glottis being semi-opened and turbulent flow being cr...The Autoregressive Moving Average （ARMA） model for whispered speech is proposed. with normal speech, whispered speech has no fundamental frequency because of the glottis being semi-opened and turbulent flow being created, and formant shifting exists in the lower frequency region due to the narrowing of the tract in the false vocal fold regions and weak acoustic coupling with the aubglottal system. Analysis shows that the effect of the subglottal system is to introduce additional pole-zero pairs into the vocal tract transfer function. Theoretically, the method based on an ARMA process is superior to that based on an AR process in the spectral analysis of the whispered speech. Two methods, the least squared modified Yule-Walker likelihood estimate （LSMY） algorithm and the Frequency-Domain Steiglitz-Mcbide （FDSM） algorithm, are applied to the ARMA mfldel for the whispered speech. The performance evaluation shows that the ARMA model is much more appropriate for representing the whispered speech than the AR model, and the FDSM algorithm provides a name acorate estimation of the whispered speech spectral envelope than the LSMY algorithm with higher conputational complexity.展开更多
基金National Natural Science Foundation of China(No.50004008)Xuzhou Power Supply Company and the Fundamental Research Funds for the Central Universities(No.2011QNB18) for their financial and technical support for this work
文摘Given the background of a transmission tower erected on a particular mining subsidence area,we used finite element modeling to analyze the anti-deformation performance of transmission towers under a number of different load conditions,including horizontal foundation displacement,uneven vertical downward displacement,wind loads and icing conditions.The results show that the failure in stability of a single steel angle iron represents the limit of the tower given ground deformation.We calculated the corresponding limits of foundation displacements.The results indicate that compression displacement of the foundation is more dangerous than tension displacement.Under complex foundation displacement conditions,horizontal foundation displacement is a key factor leading to failure in the stability of towers.Under conditions of compression or tension displacement of the foundation,wind load becomes the key factor.Towers do not fail when foundation displacements are smaller than 1% (under tension) or 0.5% (under horizontal compression or single foundation subsidence) of the distance between two supports.
基金supported by the special research fund of Ocean University of China (201022001)
文摘Surplus production models are the simplest analytical methods effective for fish stock assessment and fisheries management. In this paper, eight surplus production estimators(three estimation procedures) were tested on Schaefer and Fox type simulated data in three simulated fisheries(declining, well-managed, and restoring fisheries) at two white noise levels. Monte Carlo simulation was conducted to verify the utility of moving averaging(MA), which was an important technique for reducing the effect of noise in data in these models. The relative estimation error(REE) of maximum sustainable yield(MSY) was used as an indicator for the analysis, and one-way ANOVA was applied to test the significance of the REE calculated at four levels of MA. Simulation results suggested that increasing the value of MA could significantly improve the performance of the surplus production model(low REE) in all cases when the white noise level was low(coefficient of variation(CV) = 0.02). However, when the white noise level increased(CV= 0.25), adding the value of MA could still significantly enhance the performance of most models. Our results indicated that the best model performance occurred frequently when MA was equal to 3; however, some exceptions were observed when MA was higher.
基金supported by the Independent Innovation Foundation of Shandong University(No.2009JC004)the Natural Science Foundation of Shandong Province(No.Y2007G31)
文摘The Autoregressive Moving Average （ARMA） model for whispered speech is proposed. with normal speech, whispered speech has no fundamental frequency because of the glottis being semi-opened and turbulent flow being created, and formant shifting exists in the lower frequency region due to the narrowing of the tract in the false vocal fold regions and weak acoustic coupling with the aubglottal system. Analysis shows that the effect of the subglottal system is to introduce additional pole-zero pairs into the vocal tract transfer function. Theoretically, the method based on an ARMA process is superior to that based on an AR process in the spectral analysis of the whispered speech. Two methods, the least squared modified Yule-Walker likelihood estimate （LSMY） algorithm and the Frequency-Domain Steiglitz-Mcbide （FDSM） algorithm, are applied to the ARMA mfldel for the whispered speech. The performance evaluation shows that the ARMA model is much more appropriate for representing the whispered speech than the AR model, and the FDSM algorithm provides a name acorate estimation of the whispered speech spectral envelope than the LSMY algorithm with higher conputational complexity.
