{"title":"基于刀具轨迹个人特性分类的平面精加工技能培训系统开发","authors":"M. Teranishi, S. Matsumoto, Hidetoshi Takeno","doi":"10.1109/IIAI-AAI.2018.00151","DOIUrl":null,"url":null,"abstract":"The paper proposes a new education software system for flat finishing skill with an iron file based on classification of personal peculiarities. The software measures learner's flat finishing motion by using a 3D stylus, and displays classified personal peculiarities effectively to correct learner's finishing motions. The system also extracts peculiarity feature pattern based on the trajectory of the iron file. A torus type Self-Organizing Map is used to classify such unknown number of classes of peculiarity patterns. To let the trainers make viewing entire students' peculiarities easily, the resulted feature maps of the torus type SOM are classified into appropriate peculiarity classes based on the cluster map values. Early developing results of the peculiarity computation classification parts with measured data of an expert and sixteen learners show effectiveness of the proposed system.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of Flat Finishing Skill Training System Based on Personal Peculiarity Classification of Tool Trajectory\",\"authors\":\"M. Teranishi, S. Matsumoto, Hidetoshi Takeno\",\"doi\":\"10.1109/IIAI-AAI.2018.00151\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper proposes a new education software system for flat finishing skill with an iron file based on classification of personal peculiarities. The software measures learner's flat finishing motion by using a 3D stylus, and displays classified personal peculiarities effectively to correct learner's finishing motions. The system also extracts peculiarity feature pattern based on the trajectory of the iron file. A torus type Self-Organizing Map is used to classify such unknown number of classes of peculiarity patterns. To let the trainers make viewing entire students' peculiarities easily, the resulted feature maps of the torus type SOM are classified into appropriate peculiarity classes based on the cluster map values. Early developing results of the peculiarity computation classification parts with measured data of an expert and sixteen learners show effectiveness of the proposed system.\",\"PeriodicalId\":309975,\"journal\":{\"name\":\"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IIAI-AAI.2018.00151\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIAI-AAI.2018.00151","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of Flat Finishing Skill Training System Based on Personal Peculiarity Classification of Tool Trajectory
The paper proposes a new education software system for flat finishing skill with an iron file based on classification of personal peculiarities. The software measures learner's flat finishing motion by using a 3D stylus, and displays classified personal peculiarities effectively to correct learner's finishing motions. The system also extracts peculiarity feature pattern based on the trajectory of the iron file. A torus type Self-Organizing Map is used to classify such unknown number of classes of peculiarity patterns. To let the trainers make viewing entire students' peculiarities easily, the resulted feature maps of the torus type SOM are classified into appropriate peculiarity classes based on the cluster map values. Early developing results of the peculiarity computation classification parts with measured data of an expert and sixteen learners show effectiveness of the proposed system.