Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection...Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.展开更多
A lower-part humanoid robot CHP-1 with 12 degree-of-freedom of motion has been developed for cooperative motion,such as pushing or lifting an object.The capability of the robot is mainly dependent on the performance o...A lower-part humanoid robot CHP-1 with 12 degree-of-freedom of motion has been developed for cooperative motion,such as pushing or lifting an object.The capability of the robot is mainly dependent on the performance of the motors,thus the motors need to be properly selected.For the purpose,the kinematics of the robot was analyzed,and a number of simulations for two kinds of cooperative motions were carried out.The torques required at each motor of the robot under external forces were obtained.Here,the external forces were also estimated through simulation and literature survey.On the basis of the torques found,the selection of motors was finally suggested,and the motors are to be installed to the humanoid robot.展开更多
To make the dynamic assembly reliability analysis more effective for complex machinery of multi-object multi-discipline(MOMD),distributed collaborative extremum response surface method(DCERSM)was proposed based on ext...To make the dynamic assembly reliability analysis more effective for complex machinery of multi-object multi-discipline(MOMD),distributed collaborative extremum response surface method(DCERSM)was proposed based on extremum response surface method(ERSM).Firstly,the basic theories of the ERSM and DCERSM were investigated,and the strengths of DCERSM were proved theoretically.Secondly,the mathematical model of the DCERSM was established based upon extremum response surface function(ERSF).Finally,this model was applied to the reliability analysis of blade-tip radial running clearance(BTRRC)of an aeroengine high pressure turbine(HPT)to verify its advantages.The results show that the DCERSM can not only reshape the possibility of the reliability analysis for the complex turbo machinery,but also greatly improve the computational speed,save the computational time and improve the computational efficiency while keeping the accuracy.Thus,the DCERSM is verified to be feasible and effective in the dynamic assembly reliability(DAR)analysis of complex machinery.Moreover,this method offers an useful insight for designing and optimizing the dynamic reliability of complex machinery.展开更多
基金Project(G2022165004L)supported by the High-end Foreign Expert Introduction Program,ChinaProject(2021XM3008)supported by the Special Foundation of Postdoctoral Support Program,Chongqing,China+1 种基金Project(2018-ZL-01)supported by the Sichuan Transportation Science and Technology Project,ChinaProject(HZ2021001)supported by the Chongqing Municipal Education Commission,China。
文摘Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.
基金Project supported by the Second Stage of Brain Korea 21 Projects
文摘A lower-part humanoid robot CHP-1 with 12 degree-of-freedom of motion has been developed for cooperative motion,such as pushing or lifting an object.The capability of the robot is mainly dependent on the performance of the motors,thus the motors need to be properly selected.For the purpose,the kinematics of the robot was analyzed,and a number of simulations for two kinds of cooperative motions were carried out.The torques required at each motor of the robot under external forces were obtained.Here,the external forces were also estimated through simulation and literature survey.On the basis of the torques found,the selection of motors was finally suggested,and the motors are to be installed to the humanoid robot.
基金Project(51175017)supported by the National Natural Science Foundation of ChinaProject(YWF-12-RBYJ-008)supported by the Innovation Foundation of Beihang University for PhD Graduates,ChinaProject(20111102110011)supported by the Research Fund for the Doctoral Program of Higher Education of China
文摘To make the dynamic assembly reliability analysis more effective for complex machinery of multi-object multi-discipline(MOMD),distributed collaborative extremum response surface method(DCERSM)was proposed based on extremum response surface method(ERSM).Firstly,the basic theories of the ERSM and DCERSM were investigated,and the strengths of DCERSM were proved theoretically.Secondly,the mathematical model of the DCERSM was established based upon extremum response surface function(ERSF).Finally,this model was applied to the reliability analysis of blade-tip radial running clearance(BTRRC)of an aeroengine high pressure turbine(HPT)to verify its advantages.The results show that the DCERSM can not only reshape the possibility of the reliability analysis for the complex turbo machinery,but also greatly improve the computational speed,save the computational time and improve the computational efficiency while keeping the accuracy.Thus,the DCERSM is verified to be feasible and effective in the dynamic assembly reliability(DAR)analysis of complex machinery.Moreover,this method offers an useful insight for designing and optimizing the dynamic reliability of complex machinery.
