The advantages of a flat-panel X-ray source(FPXS)make it a promising candidate for imaging applications.Accurate imaging-system modeling and projection simulation are critical for analyzing imaging performance and res...The advantages of a flat-panel X-ray source(FPXS)make it a promising candidate for imaging applications.Accurate imaging-system modeling and projection simulation are critical for analyzing imaging performance and resolving overlapping projection issues in FPXS.The conventional analytical ray-tracing approach is limited by the number of patterns and is not applicable to FPXS-projection calculations.However,the computation time of Monte Carlo(MC)simulation is independent of the size of the patterned arrays in FPXS.This study proposes two high-efficiency MC projection simulators for FPXS:a graphics processing unit(GPU)-based phase-space sampling MC(gPSMC)simulator and GPU-based fluence sampling MC(gFSMC)simulator.The two simulators comprise three components:imaging-system modeling,photon initialization,and physical-interaction simulations in the phantom.Imaging-system modeling was performed by modeling the FPXS,imaging geometry,and detector.The gPSMC simulator samples the initial photons from the phase space,whereas the gFSMC simulator performs photon initialization from the calculated energy spectrum and fluence map.The entire process of photon interaction with the geometry and arrival at the detector was simulated in parallel using multiple GPU kernels,and projections based on the two simulators were calculated.The accuracies of the two simulators were evaluated by comparing them with the conventional analytical ray-tracing approach and acquired projections,and the efficiencies were evaluated by comparing the computation time.The results of simulated and realistic experiments illustrate the accuracy and efficiency of the proposed gPSMC and gFSMC simulators in the projection calculation of various phantoms.展开更多
The global reduction in agricultural labor owing to rural depopulation and an aging workforce necessitates advancements in agricultural automation.This paper presents the design,optimization,and experimental validatio...The global reduction in agricultural labor owing to rural depopulation and an aging workforce necessitates advancements in agricultural automation.This paper presents the design,optimization,and experimental validation of an innovative automatic lettuce-harvesting robot tailored to address the challenges posed by variable lettuce sizes,irregular shapes,and complex harvesting environments.The robot integrates three primary functional modules:root-cutting,leaf-removal,and adaptive transfer mechanisms.Employing a six-bar linkage for leaf removal,the mechanism achieves superior adaptability,durability,and precision while minimizing design complexity.The transfer system,featuring dual-layer toothed belts and self-adjusting spring mechanisms,ensures the stability and efficiency of lettuce of varying sizes.The system’s performance was enhanced through systematic kinematic and dynamic modeling,followed by Monte Carlo-based parametric optimization.Experimental evaluation of the prototype validated the robot’s operational effectiveness,achieving a root-cutting success rate of 98%,a leaf-removal completion rate of over 97%,and the ability to complete the full process of handling 29 lettuces per minute in a laboratory setting.This study advances the field of agricultural robotics by offering scalable solutions for efficient lettuce harvesting and potential adaptation to other crops,laying the foundation for sustainable automation in precision agriculture.展开更多
文摘The advantages of a flat-panel X-ray source(FPXS)make it a promising candidate for imaging applications.Accurate imaging-system modeling and projection simulation are critical for analyzing imaging performance and resolving overlapping projection issues in FPXS.The conventional analytical ray-tracing approach is limited by the number of patterns and is not applicable to FPXS-projection calculations.However,the computation time of Monte Carlo(MC)simulation is independent of the size of the patterned arrays in FPXS.This study proposes two high-efficiency MC projection simulators for FPXS:a graphics processing unit(GPU)-based phase-space sampling MC(gPSMC)simulator and GPU-based fluence sampling MC(gFSMC)simulator.The two simulators comprise three components:imaging-system modeling,photon initialization,and physical-interaction simulations in the phantom.Imaging-system modeling was performed by modeling the FPXS,imaging geometry,and detector.The gPSMC simulator samples the initial photons from the phase space,whereas the gFSMC simulator performs photon initialization from the calculated energy spectrum and fluence map.The entire process of photon interaction with the geometry and arrival at the detector was simulated in parallel using multiple GPU kernels,and projections based on the two simulators were calculated.The accuracies of the two simulators were evaluated by comparing them with the conventional analytical ray-tracing approach and acquired projections,and the efficiencies were evaluated by comparing the computation time.The results of simulated and realistic experiments illustrate the accuracy and efficiency of the proposed gPSMC and gFSMC simulators in the projection calculation of various phantoms.
基金Supported by Beijing Natural Science Foundation(Grant No.QY24093).
文摘The global reduction in agricultural labor owing to rural depopulation and an aging workforce necessitates advancements in agricultural automation.This paper presents the design,optimization,and experimental validation of an innovative automatic lettuce-harvesting robot tailored to address the challenges posed by variable lettuce sizes,irregular shapes,and complex harvesting environments.The robot integrates three primary functional modules:root-cutting,leaf-removal,and adaptive transfer mechanisms.Employing a six-bar linkage for leaf removal,the mechanism achieves superior adaptability,durability,and precision while minimizing design complexity.The transfer system,featuring dual-layer toothed belts and self-adjusting spring mechanisms,ensures the stability and efficiency of lettuce of varying sizes.The system’s performance was enhanced through systematic kinematic and dynamic modeling,followed by Monte Carlo-based parametric optimization.Experimental evaluation of the prototype validated the robot’s operational effectiveness,achieving a root-cutting success rate of 98%,a leaf-removal completion rate of over 97%,and the ability to complete the full process of handling 29 lettuces per minute in a laboratory setting.This study advances the field of agricultural robotics by offering scalable solutions for efficient lettuce harvesting and potential adaptation to other crops,laying the foundation for sustainable automation in precision agriculture.
