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Table of Contents
Papers
Paulo Abreu, Manuel Romano Barbosa and António Mendes Lopes
Abstract
This paper presents the use of a virtual lab for teaching industrial robots programming to university students. The virtual lab, that replicates the existing physical lab, is built using an industrial simulation software package, RobotStudio™. The capabilities of this tool are explored in order to complement the introduction of theoretical concepts with practical programming experience. In addition to illustrate the use of different coordinate systems in a robotic cell, a description of the tool center point calibration and examples of evaluating different moving strategies to cover a plane surface, are also presented.
Li Wang, Zhi-kai Zhao and Na Xu
Abstract
3D models classification is a critical process of Building Information Modeling (BIM). A Deep Learning Approach is proposed to classify 3D models in BIM environment. The ray based feature extraction algorithm is used to extract features of 3D models and form features matrix. The Deep Belief Network constructed by Restricted Boltzmann Machines applies the features matrix and classifies the models adopting the effective training process. The process of training DBN is layer by layer. Experiments were taken on the public 3D model library of PSB model database. The results show that compared with several commonly used classification method, the proposed method of this paper has achieved good results in the 3D model classification for efficiently BIM.
Calls
13th International Conference on Remote Engineering and Virtual Instrumentation (REV2016)
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