Description and contextualization of the subject

This course is about applying computer science to the industry, paying special attention to data science, the Cloud, and programming. The main goal is to develop informatics systems to control and/or monitor industrial systems. In our case, we will learn some basics on programming and the Cloud to be able to apply these ideas to Smartgrid systems.

Learning outcomes of the subject

By the end of this course, the students should be able to:



-To program basic applications in MATLAB.

-To program basic notebooks in Python.

-To launch virtual instances in AWS.

-To program basic VIs with LabVIEW.

-To create an IoT-like system with myRIO.

Ordinary call: orientations and renunciation

The subject is presential, so the student has to attend to the lessons to pass the subject. Anyway, given the current issues with COVID-19, there will be means to make possible to pass the course without presential activity. Indeed, many of the exercises we will do, be it presentially or remotely, are based on online platform for this purpose.



In the event that the sanitary conditions prevent the realization of a teaching activity and / or face-to-face evaluation, a non-face-to-face modality will be activated of which the students will be informed promptly.



The learning methodology is based on hands-on, self-paced learning of interconnected techniques. The student must tick some boxes to pass the course, and the rest of the exercises will determine the final mark of the assessment.



The assessment items that are mandatory are the following:

1. MATLAB: the student must complete the MATLAB Onramp course

2. Python: the student must complete the Python basic course in Kaggle

3. AWS: the student must complete the aws.training basic course

4. LabVIEW: the student must pass the learn LabVIEW test



These four items are requirements to pass the course, that is, no student will pass the course without ticking all of these boxes. Obviously, they are fairly easy and fun, so there is no reason to skip them. If any student struggles to do any of the mandatory tasks, he or she should ask the teacher for an alternative way to assess that part of the course.



Each mandatory item has two exercises associated to it: one guided exercise the student will have to do in an online platform, and another one that will be more open. The open exercise should be an example application for Smartgrids or distributed generation. Each exercise will have a weight between 5% and 15% of the final mark of the subject. The range of the sum of the marks for all the exercises should be between 30% and 70% of the final mark.



Finally, the student will have to design a system using one or many of the techniques learned during the course. If possible, the student should try to implement part of the system. This final work will get a mark between 20% and 40% of the course, including the presentation that the student will have to give in the last session of the course, and the short report that would have to be submitted in a week's time after the last session.



Students who want to avoid the continuous assessment method or to do a no-show should follow current regulations. Do not hesitate to contact the teacher of the course, if you have any question about the assessment.

Extraordinary call: orientations and renunciation

The considerations of the regular call apply to the extraordinary call too. The only exception is that the presentation would be arranged directly with the teacher in the extraordinary call date range.

Temary

Introduction to Information Technology in the Industry

MATLAB basics

Data Science with Python and Jupyter notebooks

Losing one's fear to the Cloud (AWS)

Graphical programming fundamentals (LabVIEW) and IoT targets (myRIO)

Bibliography

Compulsory materials

Documents available in eGela: https://eGela.ehu.eus/

Basic bibliography

Documents available in eGela: http://eGela.ehu.eus/

In-depth bibliography

- Ogata K. Problemas de Ingeniería de Control utilizando MATLAB, Prentice-Hall Iberia, 1999.







- Lutz M. Learning Python: Powerful Object-Oriented Programming. Ed. O'Reilly Media, 2013.



- Usmani Z. Kaggle for Beginners: with Kernel Code. Ed. Gufhtugu, 2017.







- NI myRIO Project Essentials Guide. Ed Doering. Ed. National Technology and Science Press.



- Introduction to data acquisition with LabView. Robert King. McGraw-Hill, New York, 2013.



- LabVIEW Data Acquisition Basics Manual. National Instruments. 1998.



- LabVIEW programming, data acquisition and analysis. J.Y. Beyon. Ed. Prentice Hall.



- Hands-on exercise manual for LabVIEW programming, data acquisition and analysis. J.Y. Beyon. Ed. Prentice Hall.







- Real-Time Systems. Jane W.S. Liu. Prentice Hall, 2000.



- Demystifying embedded systems middleware. Tammy Noergaard. Elsevier/Newnes, Oxford : 2011.

Journals

Computers & Industrial Engineering



ISSN: 0360-8352



http://www.journals.elsevier.com/computers-and-industrial-engineering







IEEE Transactions on Industrial Informatics



ISSN 1551-3203



http://tii.ieee-ies.org/







IEEE Transactions on Smart Grid



ISSN: 1949-3053



http://www.ieee-pes.org/ieee-transactions-on-smart-grid

Links

https://informatics.industriainformatika.pw/







http://www.ni.com/en-us/innovations/energy/smart-grid.html







https://es.mathworks.com/academia/tah-portal/universidad-del-pais-vasco-31427936.html







https://www.kaggle.com/