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OUYANG, Boya (author), LI, Yuhai (author), SONG, Yu (author), WU, Feishu (author), YU, Huizi (author), WANG, Yongzhe (author), BAUCHY, Mathieu (author), SANT, Gaurav (author)
Despite previous efforts to relate concrete proportioning and strength, a robust knowledgebased model for accurate concrete strength predictions is still lacking. As an alternative to physical or chemical-based models, machine learning (ML) methods offer a new solution to this problem. Although ML can handle the complex, non-linear, non-additive...
conference paper 2021
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Poddighe, R. (author), Roos, N. (author)
In this paper, two alternative methods to the Inverse Kinematics problem are compared to traditional methods regarding computation time, accuracy, and convergence rate. The test domain is the arm of the NAO humanoid robot. The results show that FABRIK, a heuristic iterative approximation algorithm outperforms the two traditional methods, which...
conference paper 2013