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Prof. Dr. Florian Wahl

  • Data Analytics
  • Machine Learning
  • Pattern Recognition
  • Embedded Systems
  • Ubiquitous Computing

Professor


consulting time

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Sortierung:
Zeitschriftenartikel

  • Florian Wahl
  • Matthias Breslein
  • Benedikt Elser

On-demand forklift hailing system for Intralogistics 4.0

In: Procedia Computer Science vol. 200 pg. 878-886.

  • (2022)

DOI: 10.1016/j.procs.2022.01.285

The shift to I4.0 is happening. While large companies have a range of solutions to implement that change, small and medium-sized enterprises (SME) fall short on solutions tailored for their specific needs. To support SMEs in their transformation toward I4.0, we propose a lightweight system to hail forklifts in a production facility of a medium-sized enterprise. Existing shop floor workflows are implemented within the system and allow machine operators to hail forklift drivers using an embedded or a web-based client. Forklift drivers receive driving instructions on their smartphones. Shift managers can monitor intralogistic activities on a dashboard. Management can extract relevant production and forklift KPIs from the system. In a two-week evaluation phase, we installed our system in a production facility for injection moulded plastic parts. We equipped 12 machines and two forklifts and registered a total of 690 jobs. We found half of the jobs were picked up in 4:05 min and 80% of all jobs were completed in less than 40:02 min.
  • TC Grafenau
  • DIGITAL
Vortrag

  • Florian Wahl

Wieviel Personal brauche ich morgen? Nachfrageprognose in der Stückgutlogistik . Posterpräsentation

In: 6. Tag der Forschung

Technische Hochschule Deggendorf Deggendorf

  • 10.04.2019 (2019)
  • Angewandte Informatik
  • Angewandte Wirtschaftswissenschaften
  • TC Grafenau
  • DIGITAL
  • NACHHALTIG
Vortrag

  • Michael Fernandes
  • Florian Wahl

Tomatenkrimi - Der Foodscanner als Ermittler . Keynote

In: Tag der offenen Türe des Technologie Campus Grafenau

Grafenau

  • 12.07.2019 (2019)
  • TC Grafenau
  • Angewandte Informatik
  • NACHHALTIG
Vortrag

  • Florian Wahl

Data Science with Python

Hochschulgruppe Deggendorf der Gesellschaft für Informatik Deggendorf

  • 27.06.2019 (2019)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Florian Wahl

Produktion 4.0 in KMUs - Datenerhebung und Datenanalyse

Technische Hochschule Deggendorf Deggendorf

  • 05.06.2019 (2019)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Florian Wahl

Data Science with Python

In: Meetup-Reihe League of Geeks

Passau

  • 17.10.2019 (2019)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Florian Wahl

Building Industry 4.0 logistics applications with MicroPython and ESP32 MCUs

In: Konferenz EuroPython 2019

Basel, Schweiz

  • 11.07.2019 (2019)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Florian Wahl

Produktion 4.0 in KMUs - Datenerhebung und Datenanalyse

XING Nutzergruppe FRG Schönberg

  • 06.06.2019 (2019)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Florian Wahl

Wieviel Personal brauche ich morgen? . Best Presentation Award

In: 6. Tag der Forschung

Technische Hochschule Deggendorf Deggendorf

  • 10.04.2019 (2019)
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • Angewandte Informatik
  • DIGITAL
  • NACHHALTIG
Zeitschriftenartikel

  • Florian Wahl
  • O. Amft

Data and Expert Models for Sleep Timing and Chronotype Estimation from Smartphone Context Data and Simulations

In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Association for Computing Machinery, NY, USA) vol. 2

  • (2018)

DOI: 10.1145/3264949

We present a sleep timing estimation approach that combines data-driven estimators with an expert model and uses smartphone context data. Our data-driven methodology comprises a classifier trained on features from smartphone sensors. Another classifier uses time as input. Expert knowledge is incorporated via the human circadian and homeostatic two process model. We investigate the two process model as output filter on classifier results and as fusion method to combine sensor and time classifiers. We analyse sleep timing estimation performance, in data from a two-week free-living study of 13 participants and sensor data simulations of arbitrary sleep schedules, amounting to 98280 nights. Five intuitive sleep parameters were derived to control the simulation. Moreover, we investigate model personalisation, by retraining classifiers based on participant feedback. The joint data and expert model yields an average relative estimation error of -2±62 min for sleep onset and -5±70 min for wake (absolute errors 40±48 min and 42±57 min, mean median absolute deviation 22 min and 15 min), which significantly outperforms data-driven methods. Moreover, the data and expert models combination remains robust under varying sleep schedules. Personalising data models with user feedback from the last two days showed the largest performance gain of 57% for sleep onset and 59% for wake up. Our power-efficient smartphone app makes convenient everyday sleep monitoring finally realistic.
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL