Three-dimensional (3D) morphology of microparts has an important influence on performance of microassembly system that mainly assembles microparts in millimetre and micron scale. Because 3D morphology of microparts cannot be accurately obtained by conventional microscopic vision system, a depth estimation method of surface of micropart in microassembly space based on microscopic vision tomographic scanning (MVTS) images is proposed in this paper. The proposed method uses the positions of pixels with the largest focus values in MVTS image to construct the isodepth contours of surface of micropart and obtains the depth values of micropart's surface at the positions of MVTS by assigning depth values to corresponding isodepth contours. The MVTS images are obtained by MVTS and pixels with the largest focus values in MVTS image are obtained by focus measurement of MVTS images of micropart in microassembly space. On these bases, 3D spatial interpolation method is applied to map depth value of space between adjacent isodepth contours and to obtain depth values of all surface of micropart. Simulation experiments are carried out to verify the proposed method by generating simulated MVTS image array from two simulation objects, and the influence parameters of the proposed method are analysed. In established experimental setup of microassembly that can realise MVTS, experimental verification for the proposed depth estimation method are carried out by using cone cavity and end jaws of microgripper. 3D morphologies of depth maps of cone cavity and end jaws of microgripper are registered with their respective CAD models using iterative nearest point registration algorithm to quantify accuracy of depth estimation. The research results show that 3D morphology of micropart can be obtained by the proposed method and has better accuracy than those by conventional shape from focus method. This method provides a new way to obtain the morphology of microparts and lays a foundation for improving the accuracy and efficiency of gripping, alignment and approaching microparts in microassembly systems.
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Am J Sports Med
January 2025
Department of Orthopaedics, Isala Hospital, Zwolle, The Netherlands.
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J Biomed Opt
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The Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States.
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View Article and Find Full Text PDFNatl Sci Rev
February 2025
SinoProbe Laboratory, Key Laboratory of Continental Dynamics of Ministry of Natural Resources, Institute of Geology, Chinese Academy of Geological Sciences, Beijing 100037, China.
The onset age and depth of the central Tibet strike-slip faults are two still unresolved fundamental issues with regard to the Cenozoic tectonic evolution of central Tibet. Here we present a comprehensive dataset of geochronological, geochemical and structural data on recently discovered en-echelon dykes representing the incipient development of strike-slip faulting from the Lunpola basin in central Tibet. Our results provide evidence for mantle-derived, bimodal magmatism linked to lithospheric-scale strike-slip faulting at 35-32 Ma, and demonstrate that the central Tibet strike-slip faults are at least 20 Ma older than previously estimated (15-8 Ma).
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January 2025
Maebashi-Institute of Technology, Systems Life Engineering, Gunma, 371-0816 Japan. Electronic address:
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View Article and Find Full Text PDFEnviron Res
January 2025
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM machine-learning model covering the contiguous US from 2003 through 2023. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures.
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