The four-track walking mining vehicle can better cope with the complex terrain of cobalt-rich crusts on the seabed.To explore the influence of different parameters on the obstacle-crossing ability of mining vehicles,t...The four-track walking mining vehicle can better cope with the complex terrain of cobalt-rich crusts on the seabed.To explore the influence of different parameters on the obstacle-crossing ability of mining vehicles,this paper took a certain type of mine vehicle as an example and establish a mechanical model of the mine vehicle.Through this model,the vehicle's traction coefficient variation could be analyzed during the obstacle-crossing process.It also reflected the relationship between the obstacle-crossing ability and the required traction coefficient.Many parameters were used for this analysis including the radius of the guide wheel radius,ground clearance of the driving wheel,the dip angle of the approaching angular and the position of centroid.The result showed that the ability to cross the obstacles requires adhesion coefficient as support.When the ratio between obstacle height and ground clearance of the guide wheel was greater than 0.7,the required adhesion coefficient increased sharply.The ability to cross obstacles will decrease,if the radius of the guide wheel increases,the height of the driving wheel increases or the dip angle of the approaching angular increases.It was most beneficial to cross the obstacle when-the ratio of the distance between the center of mass and the front driving wheel to the wheelbase is between 0.450.48.The results of this paper could provide reference for structural parameter design and performance research for mining vehicles.展开更多
Ce and C-S codoped mesoporous TiO_(2)nanocomposites were synthesized via a sol-gel method integrated with an evaporation-induced self-assembly approach.The basic physicochemical characteristics of the synthetic sample...Ce and C-S codoped mesoporous TiO_(2)nanocomposites were synthesized via a sol-gel method integrated with an evaporation-induced self-assembly approach.The basic physicochemical characteristics of the synthetic samples were analyzed via a series of characterization techniques.The results reveal that C-S and Ce codoping on mesoporous TiO_(2)enhances the photocatalytic activity owing to the synergistic effect caused by narrowing the band gap,enhancing adsorption,trapping and transferring the excited e^(-)/h^(+)pairs and suppressing the recombination of e^(-)/h^(+)pairs.Furthermore,the obtained C,S-TiO_(2)/CeO_(2)materials exhibit large specific surface areas and numerous pores which not only effectively improve the adsorption-enrichment capability,but also supply multi-dimensional mass and electron transfer channels.The photodegradation efficiency of RhB by C,S-TiO_(2)/CeO_(2)within 40 min is nearly 100%,and its degradation efficiency is 6.63 times that of undoped TiO_(2).Recycling experiments show that mesoporous C,S-TiO_(2)/CeO_(2)shows excellent recoverability and stability.Furthermore,by trapping experiments,·O_(2)e^(-)/h^(+)and·OH are the predominant active species and a possible reaction mechanism is proposed.展开更多
Classification methods for binary(yes/no)tasks often produce a continuously valued score.Machine learning practitioners must perform model selection,calibration,discretization,performance assessment,tuning,and fairnes...Classification methods for binary(yes/no)tasks often produce a continuously valued score.Machine learning practitioners must perform model selection,calibration,discretization,performance assessment,tuning,and fairness assessment.Such tasks involve examining classifier results,typically using summary statistics and manual examination of details.In this paper,we provide an interactive visualization approach to support such continuously-valued classifier examination tasks.Our approach addresses the three phases of these tasks:calibration,operating point selection,and examination.We enhance standard views and introduce task-specific views so that they can be integrated into a multi-view coordination(MVC)system.We build on an existing comparison-based approach,extending it to continuous classifiers by treating the continuous values as trinary(positive,unsure,negative)even if the classifier will not ultimately use the 3-way classification.We provide use cases that demonstrate how our approach enables machine learning practitioners to accomplish key tasks.展开更多
基金Supported by National Ocean Key Special Funds in 12th Five-Year Plan of China (Grant No.DY125-11-T-01)National Natural Science Foundation of China (Grant No.52074294)。
文摘The four-track walking mining vehicle can better cope with the complex terrain of cobalt-rich crusts on the seabed.To explore the influence of different parameters on the obstacle-crossing ability of mining vehicles,this paper took a certain type of mine vehicle as an example and establish a mechanical model of the mine vehicle.Through this model,the vehicle's traction coefficient variation could be analyzed during the obstacle-crossing process.It also reflected the relationship between the obstacle-crossing ability and the required traction coefficient.Many parameters were used for this analysis including the radius of the guide wheel radius,ground clearance of the driving wheel,the dip angle of the approaching angular and the position of centroid.The result showed that the ability to cross the obstacles requires adhesion coefficient as support.When the ratio between obstacle height and ground clearance of the guide wheel was greater than 0.7,the required adhesion coefficient increased sharply.The ability to cross obstacles will decrease,if the radius of the guide wheel increases,the height of the driving wheel increases or the dip angle of the approaching angular increases.It was most beneficial to cross the obstacle when-the ratio of the distance between the center of mass and the front driving wheel to the wheelbase is between 0.450.48.The results of this paper could provide reference for structural parameter design and performance research for mining vehicles.
基金Project supported by the National Natural Science Foundation of China(41831285,51974261)Doctoral Research Initiation Project(YBZ202127)from Xichang University。
文摘Ce and C-S codoped mesoporous TiO_(2)nanocomposites were synthesized via a sol-gel method integrated with an evaporation-induced self-assembly approach.The basic physicochemical characteristics of the synthetic samples were analyzed via a series of characterization techniques.The results reveal that C-S and Ce codoping on mesoporous TiO_(2)enhances the photocatalytic activity owing to the synergistic effect caused by narrowing the band gap,enhancing adsorption,trapping and transferring the excited e^(-)/h^(+)pairs and suppressing the recombination of e^(-)/h^(+)pairs.Furthermore,the obtained C,S-TiO_(2)/CeO_(2)materials exhibit large specific surface areas and numerous pores which not only effectively improve the adsorption-enrichment capability,but also supply multi-dimensional mass and electron transfer channels.The photodegradation efficiency of RhB by C,S-TiO_(2)/CeO_(2)within 40 min is nearly 100%,and its degradation efficiency is 6.63 times that of undoped TiO_(2).Recycling experiments show that mesoporous C,S-TiO_(2)/CeO_(2)shows excellent recoverability and stability.Furthermore,by trapping experiments,·O_(2)e^(-)/h^(+)and·OH are the predominant active species and a possible reaction mechanism is proposed.
基金This research was supported in part by National Science Foundation of the USA awards 1841349 and 2007436.
文摘Classification methods for binary(yes/no)tasks often produce a continuously valued score.Machine learning practitioners must perform model selection,calibration,discretization,performance assessment,tuning,and fairness assessment.Such tasks involve examining classifier results,typically using summary statistics and manual examination of details.In this paper,we provide an interactive visualization approach to support such continuously-valued classifier examination tasks.Our approach addresses the three phases of these tasks:calibration,operating point selection,and examination.We enhance standard views and introduce task-specific views so that they can be integrated into a multi-view coordination(MVC)system.We build on an existing comparison-based approach,extending it to continuous classifiers by treating the continuous values as trinary(positive,unsure,negative)even if the classifier will not ultimately use the 3-way classification.We provide use cases that demonstrate how our approach enables machine learning practitioners to accomplish key tasks.
