Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the envir...Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.展开更多
A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,...A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.展开更多
With the purpose of on-line structural health monitoring,a transducer network was embedded into compos- ite structure to minimize the influence of surroundings.The intrinsic dispersion characteristic of Lamb wave make...With the purpose of on-line structural health monitoring,a transducer network was embedded into compos- ite structure to minimize the influence of surroundings.The intrinsic dispersion characteristic of Lamb wave makes the wavelet transform an effective signal processing method for guided waves.To get high precision in feature extrac- tion,an information entropy-based optimal mother wavelet selection approach was proposed,which was used to choose the most appropriate basis function for particular Lamb wave analysis.By using the embedded sensor network and extracting time-of-flight,delamination in the composite laminate was identified and located.The results demon- strate the effectiveness of the proposed methods.展开更多
Subsurface defects were fluorescently tagged with nanoscale quantum dots and scanned layer by layer using confocal fluorescence microscopy to obtain images at various depths. Subsurface damage depths of fused silica o...Subsurface defects were fluorescently tagged with nanoscale quantum dots and scanned layer by layer using confocal fluorescence microscopy to obtain images at various depths. Subsurface damage depths of fused silica optics were characterized quantitatively by changes in the fluorescence intensity of feature points. The fluorescence intensity vs scan depth revealed that the maximum fluorescence intensity decreases sharply when the scan depth exceeds a critical value. The subsurface damage depth could be determined by the actual embedded depth of the quantum dots. Taper polishing and magnetorheological finishing were performed under the same conditions to verify the effectiveness of the nondestructive fluorescence method. The results indicated that the quantum dots effectively tagged subsurface defects of fused-silica optics, and that the nondestructive detection method could effectively evaluate subsurface damage depths.展开更多
文摘Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.
文摘A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.
基金Supported by Natural Science Foundation of China(NSFC No.10702041)NSFC Joint Research Fund for Overseas Chinese Young Scholars(10528206)+1 种基金Key International S&T Cooperation Project of China Ministry of Science and Technnlogy(2005DFA00110)Australian Research Council(Discovery Project).
文摘With the purpose of on-line structural health monitoring,a transducer network was embedded into compos- ite structure to minimize the influence of surroundings.The intrinsic dispersion characteristic of Lamb wave makes the wavelet transform an effective signal processing method for guided waves.To get high precision in feature extrac- tion,an information entropy-based optimal mother wavelet selection approach was proposed,which was used to choose the most appropriate basis function for particular Lamb wave analysis.By using the embedded sensor network and extracting time-of-flight,delamination in the composite laminate was identified and located.The results demon- strate the effectiveness of the proposed methods.
基金Project(JCKY2016212A506-0503) supported by the Science Challenge Project of ChinaProject(51475106) supported by the National Natural Science Foundation of China
文摘Subsurface defects were fluorescently tagged with nanoscale quantum dots and scanned layer by layer using confocal fluorescence microscopy to obtain images at various depths. Subsurface damage depths of fused silica optics were characterized quantitatively by changes in the fluorescence intensity of feature points. The fluorescence intensity vs scan depth revealed that the maximum fluorescence intensity decreases sharply when the scan depth exceeds a critical value. The subsurface damage depth could be determined by the actual embedded depth of the quantum dots. Taper polishing and magnetorheological finishing were performed under the same conditions to verify the effectiveness of the nondestructive fluorescence method. The results indicated that the quantum dots effectively tagged subsurface defects of fused-silica optics, and that the nondestructive detection method could effectively evaluate subsurface damage depths.
