We are excited to present the PI World Innovation Hackathon EMEA 2018 winners!

 

DEME kindly provided a sample of their data with sensors, jack-up vessels and soil models information. Participants were encouraged to create killer applications for DEME by leveraging the PI System infrastructure.

 

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The participants had 23 hours to create an app using any of the following technologies:

  • PI Server 2018
  • PI Web API 2018
  • PI Vision 2017 R2

 

Our judges evaluated each app based on their creativity, technical content, potential business impact, data analysis and insight and UI/UX. Although it is a tough challenge to create an app in 23 hours, 4 groups were able to finish their app and present to the judges!

 

Prizes:

1st place: Intel NUC Barebone (Core 3-7100U, 120 GB SSD, 8 GB RAM), one year free subscription to PI Developers Club, one time free registration at OSIsoft PI World over the next 1 year

2nd place: Bose SoundLink Around-Ear Wireless Headphones II (black), one year free subscription to PI Developers Club, 50% discount for registration at OSIsoft PI World over the next 1 year

3rd place: Raspberry Pi 3 Model B+ Retro Arcade Gaming Kit incl. 2 classic controllers and one year free subscription to PI Developers Club

 

 

Without further do, here are the winners!

 

1st place - AG Solution

 

The team members were: Sergio Hernandez, Juri Krivoruchko and Marc Torralba

 

 

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Team AG Solution has developed an application on top of PI AF SDK and PI Vision. They've developed a custom data reference that uses Stochastic Dual Coordinate classifier from ML.NET to detect the current state of vessel by the direction of changing the state.

 

The team used the following technologies:

  • PI AF SDK
  • ML.NET
  • PI Vision

 

Here are some screenshots presented by the AG Solution team!

 

 

 

 

 

 

 

 

2nd place - M.E.S.S

 

The team members were:  David Rodriguez, Leandro Hideo, Alexander Hosefelder and Alexander Dixon

 

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Team M.E.S.S developed an algorithm in R in order to detect sensors anomalies using Machine Learning.

 

The team used the following technologies:

  • R
  • PI Web API
  • PI Web API package for R

 

Here are some screenshots presented by M.E.S.S!

 

 

 

 

 

 

3rd place - Werusys Cologne

 

The team members were: Kai Weber, Ansgar Backhaus and Julian Weber

 

 

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Team Werusys Cologne developed an application to analyze wind mill installation case data based on hidden markov model.

 

The team used the following technologies:

  • PI Web API
  • Seeq

 

Here are some screenshots presented by the Werusys Cologne!