A Machine Learning Platform for Multirotor Activity Training and Recognition

被引：1
|
作者：
De La Rosa, Matthew [1 ]
Chen, Yinong [1 ]
机构：
[1] Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85281 USA
来源：
2019 IEEE 14TH INTERNATIONAL SYMPOSIUM ON AUTONOMOUS DECENTRALIZED SYSTEM (ISADS) | 2019年
关键词：
Machine learning; training and recognition; Internet of Things; VIPLE; cloud computing; orchestration; education; classification; multirotor; INTERNET; THINGS;
D O I：
10.1109/isads45777.2019.9155812
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Machine learning is a new paradigm of problem solving. Teaching machine learning in schools and colleges to prepare the industry's needs becomes imminent, not only in computing majors, but also in all engineering disciplines. This paper develops a new, hands-on approach to teaching machine learning by training a linear classifier and applying that classifier to solve Multirotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. This work extends Arizona State University's Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development.
引用
收藏
页码：15 / 22
页数：8
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