Demonstration Application Method For Improved Motion Nusantara Dance Class Sdn 104 Pekanbaru

The background of this research is the lack of knowledge of teachers in learning the material master , no master of dance movement archipelago other areas and children do not know about other areas of the archipelago dance movement . This research was conducted with the aim to improve the ability of the archipelago dance with Learning Method Demonstration . Research findings show an increase in value - average ability to dance dance dance nusnatara especially tor - tor with preliminary data was 41.9 , an increase in the first cycle of 50.19 ( enough ) of the results of the initial data . In the assessment results kekampuan archipelago dance ( dance tor - tor ) second cycle value - average students increased to 71.2 ( capable ) . In the assessment of dance ability tor - tor the third cycle value - average students increased to 83.1 ( very poor). Activity teacher at the first meeting of the first cycle of 60 % with enough categories , increased in the second meeting rose to 70 % in both categories . At the first meeting of the second cycle increased by 80 % with good category , then at the second meeting rose to 85 % in both categories . At the first meeting of the third cycle increased to 90 % , increased in the second meeting be 95 % with a very good category . Average - average student activity in the first cycle of 50 % with the first peretmuan enough categories , increased in the second peretemuan 55 % with sufficient category . At the first meeting of the second cycle to 60 % with sufficient category , then at the second meeting of the second cycle increased to 70 % in both categories . At the first meeting of the third cycle of 90 % increase in the second meeting to 95 % . This shows that the application of the method can improve the ability of dance demonstration archipelago fifth grade students of SDN 104 Pekanbaru . Thus the hypothesis peneletian proven

Learning by observation through system identification

In our previous works, we present a new method to program mobile robots —“code identification by demonstration”— based on algorithmically transferring human behaviours to robot control code using transparent mathematical functions. Our approach has three stages: i) first extracting the trajectory of the desired behaviour by observing the human, ii) making the robot follow the human trajectory blindly to log the robot’s own perception perceived along that trajectory, and finally iii) linking the robot’s perception to the desired behaviour to obtain a generalised, sensor-based model. So far we used an external, camera based motion tracking system to log the trajectory of the human demonstrator during his initial demonstration of the desired motion. Because such tracking systems are complicated to set up and expensive, we propose an alternative method to obtain trajectory information, using the robot’s own sensor perception. In this method, we train a mathematical polynomial using the NARMAX system identification methodology which maps the position of the “red jacket” worn by the demonstrator in the image captured by the robot’s camera, to the relative position of the demonstrator in the real world according to the robot. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve door traversal behaviour

Bringing global gyrokinetic turbulence simulations to the transport timescale using a multiscale approach

The vast separation dividing the characteristic times of energy confinement and turbulence in the core of toroidal plasmas makes first-principles prediction on long timescales extremely challenging. Here we report the demonstration of a multiple-timescale method that enables coupling global gyrokinetic simulations with a transport solver to calculate the evolution of the self-consistent temperature profile. This method, which exhibits resiliency to the intrinsic fluctuations arising in turbulence simulations, holds potential for integrating nonlocal gyrokinetic turbulence simulations into predictive, whole-device models.Comment: 7 pages, 3 figure