Deep Learning Industrial Challenge (DLIC 2017)
Here is the link to the official module description:
Modul: Case Study: Modelling, Simulation, Control and Optimization (MSCO) (English)
Kick-off Meeting: 25.10.2017, 11 a.m., room 1518
Course OverviewAllgemeine Informationen, die auch im Vorlesungsverzeichnis (Stundenplan) zu finden sind.
|Studiengang||AITM Automation and IT Master|
|Fach||Case Study: Deep Learning Industrial Challenge (DLIC 2017)|
|Raum||1518 Steinmüllerallee 6|
Nearly all industrial processes and machines are controlled and monitored using sensor data. With the actual trend of Industry 4.0 this reliance on sensor data is even increasing. Recording the sensor data is only half of the story, industry needs specialists to actually process and apply algorithms on this data in order to derive meaningful information. In this project, you will work with sensor data from our industrial partners (see below) to identify events of quality problems in the sensor data.
You have to apply preprocessing methods, select a suitable machine learning (e.g., deep learning, but any other method is feasible) event detection / quality control algorithm and implement your solution.
This case study is organized as a challenge.
During the first part of the study, you will learn about a) typical test cases from our industrial partners and b) standard tools from machine learning. In the second part of DLIC 2017 you have to apply this knowledge. The final goal is to develop an event detector to accurately predict changes in a time series of drinking water composition data. Further information about the current challenge, which is also part of one of the leading conferences in computational intelligence (GECCO 2017), can be found here: http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2017/
b. What do you learn?
State-of- the-art machine learning approaches (deep learning)
Dealing with practical engineering problems from industry
Documentation of scientific projects
Working in teams
Scientific working and writing
Basic programming skills
Interest in complex real-world problems
Knowledge of data mining, process control, statistics as introduced in
IMProvT research project
OWOS research project
KOARCH research project
T. Bartz-Beielstein, S. Moritz, S. Chandrasekaran, SPOTSeven Lab Team
Flexible start. Case study can be prepared during the summer months.