通过加速器质谱 C 法测年和不平衡铀系热电离质谱(TIMS)法测年结果的对比, 结合已发表的 1414C 法测年结果和对应的日历年代, 研究了特定的洞穴次生碳酸盐沉积中“死碳”混入比例及其对 C ...通过加速器质谱 C 法测年和不平衡铀系热电离质谱(TIMS)法测年结果的对比, 结合已发表的 1414C 法测年结果和对应的日历年代, 研究了特定的洞穴次生碳酸盐沉积中“死碳”混入比例及其对 C 14法测年的影响. 结果表明, 洞穴次生碳酸盐中“死碳”混入比例变化较大; “死碳”混入比例的不确定使得洞穴次生碳酸盐的 C 法测年结果具有较大的误差. 在对经过老灰岩地层形成的洞穴石笋样品进行 C 14 14或 AMS14C 法定年时, 要充分考虑“死碳”对年龄可靠性的影响.展开更多
Research has been continually growing toward the development of image-based structural health monitoring tools that can leverage deep learning models to automate damage detection in civil infrastructure.However,these ...Research has been continually growing toward the development of image-based structural health monitoring tools that can leverage deep learning models to automate damage detection in civil infrastructure.However,these tools are typically based on RGB images,which work well under ideal lighting conditions,but often have degrading performance in poor and low-light scenes.On the other hand,thermal images,while lacking in crispness of details,do not show the same degradation of performance in changing lighting conditions.The potential to enhance automated damage detection by fusing RGB and thermal images together within a deep learning network has yet to be explored.In this paper,RGB and thermal images are fused in a ResNET-based semantic segmentation model for vision-based inspections.A convolutional neural network is then employed to automatically identify damage defects in concrete.The model uses a thermal and RGB encoder to combine the features detected from both spectrums to improve its performance of the model,and a single decoder to predict the classes.The results suggest that this RGB-thermal fusion network outperforms the RGB-only network in the detection of cracks using the Intersection Over Union(IOU)performance metric.The RGB-thermal fusion model not only detected damage at a higher performance rate,but it also performed much better in differentiating the types of damage.展开更多
文摘Research has been continually growing toward the development of image-based structural health monitoring tools that can leverage deep learning models to automate damage detection in civil infrastructure.However,these tools are typically based on RGB images,which work well under ideal lighting conditions,but often have degrading performance in poor and low-light scenes.On the other hand,thermal images,while lacking in crispness of details,do not show the same degradation of performance in changing lighting conditions.The potential to enhance automated damage detection by fusing RGB and thermal images together within a deep learning network has yet to be explored.In this paper,RGB and thermal images are fused in a ResNET-based semantic segmentation model for vision-based inspections.A convolutional neural network is then employed to automatically identify damage defects in concrete.The model uses a thermal and RGB encoder to combine the features detected from both spectrums to improve its performance of the model,and a single decoder to predict the classes.The results suggest that this RGB-thermal fusion network outperforms the RGB-only network in the detection of cracks using the Intersection Over Union(IOU)performance metric.The RGB-thermal fusion model not only detected damage at a higher performance rate,but it also performed much better in differentiating the types of damage.