A method based on multiple images captured under different light sources at different incident angles was developed to recognize the coal density range in this study.The innovation is that two new images were construc...A method based on multiple images captured under different light sources at different incident angles was developed to recognize the coal density range in this study.The innovation is that two new images were constructed based on images captured under four single light sources.Reconstruction image 1 was constructed by fusing greyscale versions of the original images into one image,and Reconstruction image2 was constructed based on the differences between the images captured under the different light sources.Subsequently,the four original images and two reconstructed images were input into the convolutional neural network AlexNet to recognize the density range in three cases:-1.5(clean coal) and+1.5 g/cm^(3)(non-clean coal);-1.8(non-gangue) and+1.8 g/cm^(3)(gangue);-1.5(clean coal),1.5-1.8(middlings),and+1.8 g/cm^(3)(gangue).The results show the following:(1) The reconstructed images,especially Reconstruction image 2,can effectively improve the recognition accuracy for the coal density range compared with images captured under single light source.(2) The recognition accuracies for gangue and non-gangue,clean coal and non-clean coal,and clean coal,middlings,and gangue reached88.44%,86.72% and 77.08%,respectively.(3) The recognition accuracy increases as the density moves further away from the boundary density.展开更多
In recent years, semiconductor quantum dots （QDs） have been widely used as photon sources in quantum optics due to their special properties, such as high quantum effi- ciency, narrow and tunable emission spectrum, e...In recent years, semiconductor quantum dots （QDs） have been widely used as photon sources in quantum optics due to their special properties, such as high quantum effi- ciency, narrow and tunable emission spectrum, easy manipulation, and so on. The spontaneous emission of QDs also depends on the surrounding environment.展开更多
文摘A method based on multiple images captured under different light sources at different incident angles was developed to recognize the coal density range in this study.The innovation is that two new images were constructed based on images captured under four single light sources.Reconstruction image 1 was constructed by fusing greyscale versions of the original images into one image,and Reconstruction image2 was constructed based on the differences between the images captured under the different light sources.Subsequently,the four original images and two reconstructed images were input into the convolutional neural network AlexNet to recognize the density range in three cases:-1.5(clean coal) and+1.5 g/cm^(3)(non-clean coal);-1.8(non-gangue) and+1.8 g/cm^(3)(gangue);-1.5(clean coal),1.5-1.8(middlings),and+1.8 g/cm^(3)(gangue).The results show the following:(1) The reconstructed images,especially Reconstruction image 2,can effectively improve the recognition accuracy for the coal density range compared with images captured under single light source.(2) The recognition accuracies for gangue and non-gangue,clean coal and non-clean coal,and clean coal,middlings,and gangue reached88.44%,86.72% and 77.08%,respectively.(3) The recognition accuracy increases as the density moves further away from the boundary density.
基金supported by the National Natural Science Foundation of China(Nos.11374289 and 61590932)the National Key R&D Program(No.2016YFA0301700)+2 种基金the Innovation Funds from the Chinese Academy of Sciences(No.60921091)the Fundamental Research Funds for the Central Universitiesthe Open Fund of the State Key Laboratory on Integrated Optoelectronics(No.IOSKL2015KF12)
文摘In recent years, semiconductor quantum dots （QDs） have been widely used as photon sources in quantum optics due to their special properties, such as high quantum effi- ciency, narrow and tunable emission spectrum, easy manipulation, and so on. The spontaneous emission of QDs also depends on the surrounding environment.
