Methodology for Extraction of Tunnel Cross-Sections Using Dense Point Cloud Data

Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minutes as an innovation technique,which provides promising applications in tunnel deformation monitoring.Here,an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed.First,the tunnel orientation is determined using principal component analysis(PCA)in the Euclidean plane.Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m.Next,a z-score method is introduced to detect and remove the outlies.Because the tunnel cross-section’s standard shape is round,the circle fitting is implemented using the least-squares method.Afterward,the convergence analysis is made at the angles of 0°,30°and 150°.The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner.The results indicate that the proposed methodology achieves an overall accuracy of 1.34 mm,which is also in agreement with the measurements acquired by a total station instrument.The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring,which can also be extended to other engineering applications.

Shanghai TMRT discovered a new dense molecular cloud rich in long carbon-chain molecules

With the support by the National Natural Science Foundation of China and the Chinese Academy of Sciences,the research team led by Prof.Shen Zhiqiang(沈志强)at the Division of Radio Astronomy Science and Technology,Shanghai Astronomical Observatory,Chinese Academy of Sciences,discovered a new dense molecular cloud rich in long carbon-chain molecules,which was published in Astrophysical

Interfering single photons retreived from collective atomic excitations in two dense cold-atom clouds

We report the Hong–Ou–Mandel（HOM） interference, with visibility of 91%, produced from two independent single photons retrieved from collective atomic excitations in two separate cold-atom clouds with high optical depths of 90. The high visibility of the HOM dip is ascribed to the pure single photon in the Fock state that was generated from a dense-cold-atom cloud pumping by a short pulse. The visibility is always the same regardless of the time response of the single-photon detectors. This result experimentally shows that the single photons retrieved are in a separable temporal state with their idler photons.

Cicada(Magicicada)Tree Damage Detection Based on UAV Spectral and 3D Data

The periodical cicadas appear in regions of the United States in intervals of 13 or 17 years. During these intervals, deciduous trees are often impacted by the small cuts and eggs they lay in the outer branches which soon die off. Because this is such an infrequent occurrence and it is?so difficult to assess the damage across large forested areas, there is little information about the extent of this impact. The use of remote sensing techniques has been proven to be useful in forest health management to monitor large areas. In addition, the use of Unmanned Aerial Vehicles (UAVs) has become a valuable tool for analysis. In this study,?we evaluated the impact of the periodical cicada occurrence on a mixed hardwood forest using UAV imagery.?The goal was to evaluate the potential of this technology as a tool for forest health monitoring. We classified the cicada impact using two Maximum Likelihood classifications, one using only the high resolution spectral derived from leaf-on imagery (MLC 1), and in the second we included the Canopy Height Model (CHM)—derived from?leaf-on Digital Surface Model (DSM) and leaf-off Digital Terrain Model (DTM)—information in the classification process (MLC 2). We evaluated the damage percentage in relation to the total forest area in 15 circular plots and observed a range from 1.03%?-22.23% for MLC 1, and 0.02%?-?10.99% for MLC 2. The accuracy of the classification was 0.35 and 0.86, for MLC 1 and MLC 2, based on the kappa index. The results allow us to highlight the importance of combining spectral and 3D information to evaluate forest health features. We believe this approach can be applied in many forest monitoring objectives in order to?detect disease or pest impacts.

Lessons Learned as President of the Institute for Systems Biology(2000–2018)

I stepped down as president of the Institute for Systems Biol- ogy （ISB） on Jan 1, 2018. As I think about my 17-year term as President, I am astounded at how much I have learned, not only about science but also about, among other things, what it takes to build a unique world-class institution.