Uncertainty widely exists in engineering structures,making it necessary to update the finite element(FE)modeling with uncertainty.An updating approach for the FE model of structural dynamics with interval uncertain pa...Uncertainty widely exists in engineering structures,making it necessary to update the finite element(FE)modeling with uncertainty.An updating approach for the FE model of structural dynamics with interval uncertain parameters is proposed in this work.Firstly,the correlations between the updating parameters and the output qualities of interest are uncorrelated by the principal component analysis(PCA),and the massive samples of major principal components are obtained using the Monte Carlo simulation.Then,the massive samples of updating parameters and output qualities are obtained by reversing transformation in the PCA,and the new samples of updating parameters are re-chosen by combining the massive samples with the experimental intervals of the qualities of interest.Next,the 95%CI of the updating parameters is estimated by the nonparametrie kernel density estimation approach,which are regarded as the intervals of updating parameters.Lastly,the proposed approach is validated in simple rectangular plates and the transport mirror system.The updating results evidently demonstrate the feasibility and reliability of the proposed approach.展开更多
The development of artificial intelligence(AI)technologies creates a great chance for the iteration of railway monitoring.This paper proposes a comprehensive method for railway utility pole detection.The framework of ...The development of artificial intelligence(AI)technologies creates a great chance for the iteration of railway monitoring.This paper proposes a comprehensive method for railway utility pole detection.The framework of this paper on railway systems consists of two parts:point cloud preprocessing and railway utility pole detection.Thismethod overcomes the challenges of dynamic environment adaptability,reliance on lighting conditions,sensitivity to weather and environmental conditions,and visual occlusion issues present in 2D images and videos,which utilize mobile LiDAR(Laser Radar)acquisition devices to obtain point cloud data.Due to factors such as acquisition equipment and environmental conditions,there is a significant amount of noise interference in the point cloud data,affecting subsequent detection tasks.We designed a Dual-Region Adaptive Point Cloud Preprocessing method,which divides the railway point cloud data into track and non-track regions.The track region undergoes projection dimensionality reduction,with the projected results being unique and subsequently subjected to 2D density clustering,greatly reducing data computation volume.The non-track region undergoes PCA-based dimensionality reduction and clustering operations to achieve preprocessing of large-scale point cloud scenes.Finally,the preprocessed results are used for training,achieving higher accuracy in utility pole detection and data communication.Experimental results show that our proposed preprocessing method not only improves efficiency but also enhances detection accuracy.展开更多
Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide.Using land use,socioeconomic,and natural hazards data,we conducted an assessment of the ecologic...Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide.Using land use,socioeconomic,and natural hazards data,we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005,2010,and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations.We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index(EVI),and constructed the EVI using a principal component analysis-based entropy method.A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability.The results show that:(1)for each of the study years,prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan,whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability;(2)Sichuan’s ecological vulnerability increased significantly(p=0.011)during2005–2010;(3)anthropogenic activities were the main factors driving variations in ecological vulnerability.These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.展开更多
基金the Science Challenge Project(Grant No.TZ2018007)the National Key Research and Development Program of China(Grant No.2016YFB0201005)the National Natural Science Foundation of China(Grant No.11472256).
文摘Uncertainty widely exists in engineering structures,making it necessary to update the finite element(FE)modeling with uncertainty.An updating approach for the FE model of structural dynamics with interval uncertain parameters is proposed in this work.Firstly,the correlations between the updating parameters and the output qualities of interest are uncorrelated by the principal component analysis(PCA),and the massive samples of major principal components are obtained using the Monte Carlo simulation.Then,the massive samples of updating parameters and output qualities are obtained by reversing transformation in the PCA,and the new samples of updating parameters are re-chosen by combining the massive samples with the experimental intervals of the qualities of interest.Next,the 95%CI of the updating parameters is estimated by the nonparametrie kernel density estimation approach,which are regarded as the intervals of updating parameters.Lastly,the proposed approach is validated in simple rectangular plates and the transport mirror system.The updating results evidently demonstrate the feasibility and reliability of the proposed approach.
文摘The development of artificial intelligence(AI)technologies creates a great chance for the iteration of railway monitoring.This paper proposes a comprehensive method for railway utility pole detection.The framework of this paper on railway systems consists of two parts:point cloud preprocessing and railway utility pole detection.Thismethod overcomes the challenges of dynamic environment adaptability,reliance on lighting conditions,sensitivity to weather and environmental conditions,and visual occlusion issues present in 2D images and videos,which utilize mobile LiDAR(Laser Radar)acquisition devices to obtain point cloud data.Due to factors such as acquisition equipment and environmental conditions,there is a significant amount of noise interference in the point cloud data,affecting subsequent detection tasks.We designed a Dual-Region Adaptive Point Cloud Preprocessing method,which divides the railway point cloud data into track and non-track regions.The track region undergoes projection dimensionality reduction,with the projected results being unique and subsequently subjected to 2D density clustering,greatly reducing data computation volume.The non-track region undergoes PCA-based dimensionality reduction and clustering operations to achieve preprocessing of large-scale point cloud scenes.Finally,the preprocessed results are used for training,achieving higher accuracy in utility pole detection and data communication.Experimental results show that our proposed preprocessing method not only improves efficiency but also enhances detection accuracy.
基金sponsored by the National Key Research Program of China(2016YFA0602403)the National Science Foundation(41621061)the International Center for Collaborative Research on Disaster Risk Reduction(ICCR-DRR)
文摘Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide.Using land use,socioeconomic,and natural hazards data,we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005,2010,and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations.We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index(EVI),and constructed the EVI using a principal component analysis-based entropy method.A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability.The results show that:(1)for each of the study years,prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan,whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability;(2)Sichuan’s ecological vulnerability increased significantly(p=0.011)during2005–2010;(3)anthropogenic activities were the main factors driving variations in ecological vulnerability.These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.
