This article extends a signal-based approach formerly proposed by the authors, which utilizes the fractal dimension of time frequency feature （FDTFF） of displacements, for earthquake damage detection of moment resis...This article extends a signal-based approach formerly proposed by the authors, which utilizes the fractal dimension of time frequency feature （FDTFF） of displacements, for earthquake damage detection of moment resist frame （MRF）, and validates the approach with shaking table tests. The time frequency feature （TFF） of the relative displacement at measured story is defined as the real part of the coefficients of the analytical wavelet transform. The fractal dimension （FD） is to quantify the TFF within the fundamental frequency band using box counting method. It is verified that the FDTFFs at all stories of the linear MRF are identical with the help of static condensation method and modal superposition principle, while the FDTFFs at the stories with localized nonlinearities due to damage will be different from those at the stories without nonlinearities using the reverse-path methodology. By comparing the FDTFFs of displacements at measured stories in a structure, the damage-induced nonlinearity of the structure under strong ground motion can be detected and localized. Finally shaking table experiments on a 1：8 scale sixteen-story three-bay steel MRF with added frictional dampers, which generate local nonlinearities, are conducted to validate the approach.展开更多
This study develops a signal-based trading strategy for the SPDR S&P 500 ETF Trust(SPY)using a multiple linear regression framework to analyze interrelationships between SPY and global equity indices across U.S.,E...This study develops a signal-based trading strategy for the SPDR S&P 500 ETF Trust(SPY)using a multiple linear regression framework to analyze interrelationships between SPY and global equity indices across U.S.,European,Asian,and Australian markets.By synthesizing historical pricing data from these major benchmarks,the model generates systematic trading signals through predicted price trajectories.In controlled training scenarios,the strategy achieved superior risk-adjusted returns compared to passive buy-and-hold approaches,demonstrating the value of cross-market signal integration.While the framework shows promise for algorithmic trading systems,the study acknowledges limitations in generalizing historical patterns to evolving market conditions.The findings highlight opportunities to enhance predictive accuracy through machine learning architectures capable of processing nonlinear market dynamics.These insights advance quantitative trading research by establishing methodologies for cross-market signal synthesis and proposing pathways to develop adaptive models for volatile capital markets。展开更多
基金National Natural Science Foundation under Grant No.51161120359Ministry of Education under Grant No.20112302110050Special Fund for Earthquake Scientific Research in the Public Interest under Grant No.201308003
文摘This article extends a signal-based approach formerly proposed by the authors, which utilizes the fractal dimension of time frequency feature （FDTFF） of displacements, for earthquake damage detection of moment resist frame （MRF）, and validates the approach with shaking table tests. The time frequency feature （TFF） of the relative displacement at measured story is defined as the real part of the coefficients of the analytical wavelet transform. The fractal dimension （FD） is to quantify the TFF within the fundamental frequency band using box counting method. It is verified that the FDTFFs at all stories of the linear MRF are identical with the help of static condensation method and modal superposition principle, while the FDTFFs at the stories with localized nonlinearities due to damage will be different from those at the stories without nonlinearities using the reverse-path methodology. By comparing the FDTFFs of displacements at measured stories in a structure, the damage-induced nonlinearity of the structure under strong ground motion can be detected and localized. Finally shaking table experiments on a 1：8 scale sixteen-story three-bay steel MRF with added frictional dampers, which generate local nonlinearities, are conducted to validate the approach.
文摘This study develops a signal-based trading strategy for the SPDR S&P 500 ETF Trust(SPY)using a multiple linear regression framework to analyze interrelationships between SPY and global equity indices across U.S.,European,Asian,and Australian markets.By synthesizing historical pricing data from these major benchmarks,the model generates systematic trading signals through predicted price trajectories.In controlled training scenarios,the strategy achieved superior risk-adjusted returns compared to passive buy-and-hold approaches,demonstrating the value of cross-market signal integration.While the framework shows promise for algorithmic trading systems,the study acknowledges limitations in generalizing historical patterns to evolving market conditions.The findings highlight opportunities to enhance predictive accuracy through machine learning architectures capable of processing nonlinear market dynamics.These insights advance quantitative trading research by establishing methodologies for cross-market signal synthesis and proposing pathways to develop adaptive models for volatile capital markets。
