Hidetoshi Takeno

Personal Peculiarity Classification of Flat Finishing Skill Training by using Torus type Self-Organizing Maps

The paper proposes an unsupervised classification method for peculiarities of flat finishing motion with an iron file, measured by a 3D stylus. The classified personal peculiarities are used to correct learner’s finishing motions effectively for skill training. In the case of such skill training, the number of classes of peculiarity is unknown. A torus type Self-Organizing Maps is effectively used to classify such unknown number of classes of peculiarity patterns.

Peculiarity Classification of Flat Finishing Motion Based on Tool Trajectory by Using Self-organizing Maps

The paper proposes an unsupervised classification method for peculiarities of flat finishing motion with an iron file, measured by a 3D stylus. The proposed method extract personal peculiarities based on trajectory of an iron file. The classified peculiarities are used to correct learner’s finishing motions effectively for skill training. In the case of such skill training, the number of classes of peculiarity is unknown. A torus type Self-Organizing Maps is effectively used to classify such unknown number of classes of peculiarity patterns.

Examining efficient instructional methods for computer-aided brush coating skill training system in elementary and secondary education

In recent Japan, many elementary and secondary school children strongly feel an awareness of resistance toward manufacturing class. It is considered as one of the aversion causes to science and engineering. We suspect that the shortage of teacher’s manufacturing experience is related to this background. So, the authors previously have developed a brush coating skill training system with a haptic device to improve the ability. In the skill training, historically, the instruction method while reproducing the video of trainee’s action has been considered to be effective. Thus, our developed system includes a software system to visualize the brush coating motion. This software system can record trainee’s brush coating motion and visualize the data in virtual three-dimensional space, so the advisor and the trainee can share his/her skill level and the wrong motion such as extra shaking and tilt by reproducing his/her past training data on a computer screen. In this paper, to improve the learning effectiveness of our brush coating skill training system, some kinds of instructional methods are designed, and the difference of the instructional methods is analyzed based on the proposed criteria. This paper performed some experiments with 10 college students who have less experience of brush coating. From the analysis results, the most effective instruction method was clarified.

Personal Peculiarity Classification of Flat Finishing Motion for Skill Training by Using Expanding Self-Organizing Maps

The paper proposes an unsupervised classification method for peculiarities of flat finishing motion with an iron file, measured by a 3D stylus. The classified personal peculiarities are used to correct learner’s finishing motions effectively for skill training. In the case of such skill training, the number of classes of peculiarity is unknown. An expanding Self-Organizing Maps is effectively used to classify such unknown number of classes of peculiarity patterns.

Personal peculiarity classification of flat finishing tool motion by using self-organizing maps for skill training

The paper proposes a unsupervised classification method for personal peculiarities in flat finishing motion, which are measured by three dimensional stylus attached to an iron file. The method classifies personal peculiarity variations in order to correct bad finishing motions effectively for skill training. In the case of flat finishing motion, the number of classes of personal peculiarities is unknown. A Self-Organizing Maps is used to classify such unknown number of classes of personal peculiarities. Experimental results of the classification with measured data of an expert and two learners show availability of the proposed method.