The combination of spark discharge and laser-induced breakdown spectroscopy (LIBS) is called spark discharge assisted LIBS.It works under laser-plasma triggered spark discharge mode,and shows its ability to enhance sp...The combination of spark discharge and laser-induced breakdown spectroscopy (LIBS) is called spark discharge assisted LIBS.It works under laser-plasma triggered spark discharge mode,and shows its ability to enhance spectral emission intensity.This work uses a femtosecond laser as the light souuce,since femtosecond laser has many advantages in laser-induced plasma compared with nanosecond laser,meanwhile,the study on femtosecond LIBS with spark discharge is rare.Time-resolved spectroscopy of spark discharge assisted femtosecond LIBS was investigated under different discharge voltages and laser energies.The results showed that the spectral intensity was significantly enhanced by using spark discharge compared with LIBS alone.And,the spectral emission intensity using spark discharge assisted LIBS increased with the increase in the laser energy.In addition,at low laser energy,there was an obvious delay on the discharge time compared with high laser energy,and the discharge time with positive voltage was different from that with negative voltage.展开更多
Self-supervised depth estimation approaches present excellent results that are comparable to those of the fully supervised approaches,by employing view synthesis between the target and reference images in the training...Self-supervised depth estimation approaches present excellent results that are comparable to those of the fully supervised approaches,by employing view synthesis between the target and reference images in the training data.ResNet,which serves as a backbone network,has some structural deficiencies when applied to downstream fields,because its original purpose was to cope with classification problems.The low-texture area also deteriorates the performance.To address these problems,we propose a set of improvements that lead to superior predictions.First,we boost the information flow in the network and improve the ability to learn spatial structures by improving the network structures.Second,we use a binary mask to remove the pixels in low-texture areas between the target and reference images to more accurately reconstruct the image.Finally,we input the target and reference images randomly to expand the dataset and pre-train it on ImageNet,so that the model obtains a favorable general feature representation.We demonstrate state-of-the-art performance on an Eigen split of the KITTI driving dataset using stereo pairs.展开更多
基金National Natural Science Foundation of China (Nos. 11674128, and 11674124)Jilin Province Scientific and Technological Development Program, China (No. 20170101063JC).
文摘The combination of spark discharge and laser-induced breakdown spectroscopy (LIBS) is called spark discharge assisted LIBS.It works under laser-plasma triggered spark discharge mode,and shows its ability to enhance spectral emission intensity.This work uses a femtosecond laser as the light souuce,since femtosecond laser has many advantages in laser-induced plasma compared with nanosecond laser,meanwhile,the study on femtosecond LIBS with spark discharge is rare.Time-resolved spectroscopy of spark discharge assisted femtosecond LIBS was investigated under different discharge voltages and laser energies.The results showed that the spectral intensity was significantly enhanced by using spark discharge compared with LIBS alone.And,the spectral emission intensity using spark discharge assisted LIBS increased with the increase in the laser energy.In addition,at low laser energy,there was an obvious delay on the discharge time compared with high laser energy,and the discharge time with positive voltage was different from that with negative voltage.
基金Project supported by the Key R&D Program of Guangdong Province,China(No.2019B01015000)the National Natural Science Foundation of China(No.61902201)。
文摘Self-supervised depth estimation approaches present excellent results that are comparable to those of the fully supervised approaches,by employing view synthesis between the target and reference images in the training data.ResNet,which serves as a backbone network,has some structural deficiencies when applied to downstream fields,because its original purpose was to cope with classification problems.The low-texture area also deteriorates the performance.To address these problems,we propose a set of improvements that lead to superior predictions.First,we boost the information flow in the network and improve the ability to learn spatial structures by improving the network structures.Second,we use a binary mask to remove the pixels in low-texture areas between the target and reference images to more accurately reconstruct the image.Finally,we input the target and reference images randomly to expand the dataset and pre-train it on ImageNet,so that the model obtains a favorable general feature representation.We demonstrate state-of-the-art performance on an Eigen split of the KITTI driving dataset using stereo pairs.
