Seamless Integration of Machine Learning Contents in Mechatronics Curricula

Frochte, Jörg; Lemmen, M.; Schmidt, Marco

doi:10.1109/rem.2018.8421794

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…In addition, this article focuses on examples of how Andruino-R2 can be applied in the areas of intelligent control, machine vision and machine learning, maintaining a didactic approach, to illustrate its versatility and simplicity. These techniques are increasingly relevant and some authors demand that they be incorporated into the ordinary curriculum of VET and engineering students [21]. However, in many cases, only simulation environments are considered or commercial educational robots are used with a higher cost [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

An Android and Arduino Based Low-Cost Educational Robot with Applied Intelligent Control and Machine Learning

López-Rodríguez

Cuesta

2020

Applied Sciences

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Applied Science requires testbeds to carry out experiments and validate in practice the results of the application of the methods. This article presents a low-cost (35–40 euros) educational mobile robot, based on Android and Arduino, integrated with Robot Operating System (ROS), together with its application for learning and teaching in the domain of intelligent automatic control, computer vision and Machine Learning. Specifically, the practical application to visual path tracking integrated with a Fuzzy Collision Risk system, that avoids collision with obstacles ahead, is shown. Likewise, a Wi-Fi positioning system is presented, which allows identifying in which room the robot is located, based on self-collected data and Machine Learning.

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Section: Introductionmentioning

confidence: 99%

An Android and Arduino Based Low-Cost Educational Robot with Applied Intelligent Control and Machine Learning

López-Rodríguez

Cuesta

2020

Applied Sciences

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“…We are building on prior work by others using active learning [6][7][8][9][10][11], PjBL [12][13][14][15][16][17], worked examples [18][19][20], Jupyter notebooks [21,22], agile software development methods [23][24][25], as well as existing IoT course materials [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. However, the existing mechatronics course materials with IoT tend to target Electrical Engineering (EE) and Computer Science (CS) students and the creation of underlying IoT technologies, especially low-level software.…”

Section: Introductionmentioning

confidence: 99%

Board 322: Integrating Internet of Things into Mechatronics to Prepare Mechanical Engineering Students for Industry 4.0

Gurocak,

Zhao,

Lesseig

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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Vancouver. She received her Ph.D. from Department of Computer Science at the University of Saskatchewan in 2012. She previously received an M.Sc. from the same university, and a B.Sc. from Department of Computer Science, Nanjing University.Dr. Zhao's research interests lie in the general areas of parallel and distributed systems, big data computing, cloud computing, and machine learning. Dr. Zhao is a member of IEEE, ACM, ASEE, and IEEE Women in Engineering, and has been actively contributing to the professional community. She served as the general chair for the 15th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2022) and the 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT2022). She also served as the local arrangement chair for IEEE CLUSTER 2021. She was the guest editor for Special Issue on Integration of Cloud, IoT and Big Data Analytics, Software: Practice and Experience (Wiley Press). In addition, she has served on the technical program committee for a number of conferences, and as reviewer for various journals.

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