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A Coordinate-Free Approach to the Design of Generalized Griffis-Duffy Platforms
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作者 Chengwei Shen Xu Pei +1 位作者 Lubin Hang Jingjun Yu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第3期95-103,共9页
Architectural singularity belongs to the Type II singularity,in which a parallel manipulator(PM)gains one or more degrees of freedom and becomes uncontrollable.PMs remaining permanently in a singularity are beneficial... Architectural singularity belongs to the Type II singularity,in which a parallel manipulator(PM)gains one or more degrees of freedom and becomes uncontrollable.PMs remaining permanently in a singularity are beneficial for linearto-rotary motion conversion.Griffis-Duffy(GD)platform is a mobile structure admitting a Bricard motion.In this paper,we present a coordinate-free approach to the design of generalized GD platforms,which consists in determining the shape and attachment of both the moving platform and the fixed base.The generalized GD platform is treated as a combination of six coaxial single-loop mechanisms under the same constraints.Owing to the inversion,hidden in the geometric structure of these single-loop mechanisms,the mapping from a line to a circle establishes the geometric transformation between the fixed base and the moving platform based on the center of inversion,and describes the shape and attachment of the generalized GD platform.Moreover,the center of inversion not only identifies the location of rotation axis,but also affects the shape of the platform mechanism.A graphical construction of generalized GD platforms using inversion,proposed in the paper,provides geometrically feasible solutions of the manipulator design for the requirement of the location of rotation axis. 展开更多
关键词 Generalized Griffis-Duffy platforms SELF-MOTION INVERSION coordinate-free determination Manipulator design
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The Coordinate-Free Prediction in Finite Populations with Correlated Observations
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作者 Silvia N. Elian 《Open Journal of Statistics》 2017年第2期182-193,共12页
In this paper, we got the best linear unbiased predictor of any linear function of the elements of a finite population under coordinate-free models. The optimal predictor of these quantities was obtained in an earlier... In this paper, we got the best linear unbiased predictor of any linear function of the elements of a finite population under coordinate-free models. The optimal predictor of these quantities was obtained in an earlier work considering models with a known diagonal covariance matrix. We extended this result assuming any known covariance matrix. It is shown that in the particular case of the coordinatized models, this general predictor coincides with the optimal predictor of the total population under a regression super population model with correlated observations. 展开更多
关键词 coordinate-free Models Best Linear UNBIASED PREDICTOR COVARIANCE Matrix ORTHOGONAL PROJECTION
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A REVIEW AND PROSPECT OF READABLE MACHINE PROOFS FOR GEOMETRY THEOREMS 被引量:3
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作者 Jianguo JIANG Jingzhong ZHANG 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2012年第4期802-820,共19页
After half a century research, the mechanical theorem proving in geometries has become an active research topic in the automated reasoning field. This review involves three approaches on automated generating readable ... After half a century research, the mechanical theorem proving in geometries has become an active research topic in the automated reasoning field. This review involves three approaches on automated generating readable machine proofs for geometry theorems which include search methods, coordinate-free methods, and formal logic methods. Some critical issues about these approaches are also discussed. Furthermore, the authors propose three further research directions for the readable machine proofs for geometry theorems, including geometry inequalities, intelligent geometry softwares and machine learning. 展开更多
关键词 Automated geometry reasoning coordinate-free method formal logic method geometric inequality intelligent geometry software machine learning mechanical theorem proving readable machine proof search method.
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