Output-only structural identification is developed by a refined Frequency Domain Decomposition（rFDD） approach, towards assessing current modal properties of heavy-damped buildings（in terms of identification challe...Output-only structural identification is developed by a refined Frequency Domain Decomposition（rFDD） approach, towards assessing current modal properties of heavy-damped buildings（in terms of identification challenge）, under strong ground motions. Structural responses from earthquake excitations are taken as input signals for the identification algorithm. A new dedicated computational procedure, based on coupled Chebyshev Type Ⅱ bandpass filters, is outlined for the effective estimation of natural frequencies, mode shapes and modal damping ratios. The identification technique is also coupled with a Gabor Wavelet Transform, resulting in an effective and self-contained time-frequency analysis framework. Simulated response signals generated by shear-type frames（with variable structural features） are used as a necessary validation condition. In this context use is made of a complete set of seismic records taken from the FEMA P695 database, i.e. all 44 ＂Far-Field＂（22 NS, 22 WE） earthquake signals. The modal estimates are statistically compared to their target values, proving the accuracy of the developed algorithm in providing prompt and accurate estimates of all current strong ground motion modal parameters. At this stage, such analysis tool may be employed for convenient application in the realm of Earthquake Engineering, towards potential Structural Health Monitoring and damage detection purposes.展开更多
Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.Th...Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.展开更多
This study explores the use of the Global Navigation Satellite System(GNSS)precise point positioning(PPP)technology to determine the natural vibration periods of towering structures through simulations and field testi...This study explores the use of the Global Navigation Satellite System(GNSS)precise point positioning(PPP)technology to determine the natural vibration periods of towering structures through simulations and field testing.During the simulation phase,a GNSS receiver captured vi-bration waveforms generated by a single-axis motion simulator based on preset signal parameters,analyzing how different satellite system configurations affect the efficiency of extracting vibration parameters.Subsequently,field tests were conducted on a high-rise steel singletube tower.The results indicate that in the simulation environment,no matter the PPP positioning data under single GPS or multisystem combination,the vibration frequency of singleaxis motion simulator can be accurately extracted after frequency do-main analysis,with multisystem setups providing more precise amplitude parameters.In the field test,the natural vibration periods of the main vibration modes of high-rise steel single-tube tower measured by PPP technology closely match the results of the first two modes derived from finite element analysis.The first mode period calculated by the em-pirical formula is approximately 6%higher than those determined through finite element analysis and PPP.This study demonstrates the potential of PPP for structural vibration analysis,offering significant benefits for assessing dynamic responses and monitoring the health of towering structures.展开更多
基金Public research funding from“Fondi di Ricerca d’Ateneo ex 60%” and a ministerial doctoral grantfunds at the ISA Doctoral School,University of Bergamo,Department of Engineering and Applied Sciences (Dalmine)
文摘Output-only structural identification is developed by a refined Frequency Domain Decomposition（rFDD） approach, towards assessing current modal properties of heavy-damped buildings（in terms of identification challenge）, under strong ground motions. Structural responses from earthquake excitations are taken as input signals for the identification algorithm. A new dedicated computational procedure, based on coupled Chebyshev Type Ⅱ bandpass filters, is outlined for the effective estimation of natural frequencies, mode shapes and modal damping ratios. The identification technique is also coupled with a Gabor Wavelet Transform, resulting in an effective and self-contained time-frequency analysis framework. Simulated response signals generated by shear-type frames（with variable structural features） are used as a necessary validation condition. In this context use is made of a complete set of seismic records taken from the FEMA P695 database, i.e. all 44 ＂Far-Field＂（22 NS, 22 WE） earthquake signals. The modal estimates are statistically compared to their target values, proving the accuracy of the developed algorithm in providing prompt and accurate estimates of all current strong ground motion modal parameters. At this stage, such analysis tool may be employed for convenient application in the realm of Earthquake Engineering, towards potential Structural Health Monitoring and damage detection purposes.
基金National Natural Science Foundation of China(61672032)National Key Research and Development Program of China(2016YFD0800904)+1 种基金Anhui Provincial Science and Technology Project(16030701091)The Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis&Application,Anhui University(AE2018009).
文摘Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.
基金The National Natural Science Foundation of China(No.41974214).
文摘This study explores the use of the Global Navigation Satellite System(GNSS)precise point positioning(PPP)technology to determine the natural vibration periods of towering structures through simulations and field testing.During the simulation phase,a GNSS receiver captured vi-bration waveforms generated by a single-axis motion simulator based on preset signal parameters,analyzing how different satellite system configurations affect the efficiency of extracting vibration parameters.Subsequently,field tests were conducted on a high-rise steel singletube tower.The results indicate that in the simulation environment,no matter the PPP positioning data under single GPS or multisystem combination,the vibration frequency of singleaxis motion simulator can be accurately extracted after frequency do-main analysis,with multisystem setups providing more precise amplitude parameters.In the field test,the natural vibration periods of the main vibration modes of high-rise steel single-tube tower measured by PPP technology closely match the results of the first two modes derived from finite element analysis.The first mode period calculated by the em-pirical formula is approximately 6%higher than those determined through finite element analysis and PPP.This study demonstrates the potential of PPP for structural vibration analysis,offering significant benefits for assessing dynamic responses and monitoring the health of towering structures.
