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Prof. Dr. Florian Wahl

Data Analytics
Machine Learning
Pattern Recognition
Embedded Systems
Ubiquitous Computing

Centre for Applied Research

Technology Campus Grafenau

Professor

A 011

0991/3615-8257

consulting time

by appointment via Mrs. Verena Windorfer-Bogner.

publications

Sortierung:

Vortrag

Florian Wahl

Unlocking the Potential of Artificial Intelligence in Care

In: 30th IRIXYS Workshop

Institut Nationale des Sciences Appliqués (INSA) Lyon Lyon, France

10.05.2023 (2023)

Angewandte Informatik
DIGITAL
GESUND

Beitrag in Sammelwerk/Tagungsband

Domenic Sommer
Sebastian Wilhelm
Diane Ahrens
Florian Wahl

Implementing an Intersectoral Telemedicine Network in Rural Areas: Evaluation from the Point of View of Telemedicine Users

pg. 15-27.

(2023)

TC Grafenau
DIGITAL
GESUND

Vortrag

Florian Wahl

Das Unterstützungspotential von künstlicher Intelligenz in der Pflege

In: 7. Ambient Medicine® Forum

Hochschule Kempten Kempten

24.03.2023 (2023)

Angewandte Informatik
DIGITAL
GESUND

Zeitschriftenartikel

Roman-David Kulko
Alexander Pletl
H. Mempel
Florian Wahl
Benedikt Elser

OpenVNT: An Open Platform for VIS-NIR Technology

In: Sensors vol. 23 pg. 3151.

(2023)

DOI: 10.3390/s23063151

Abstract anzeigen

Spectrometers measure diffuse reflectance and create a “molecular fingerprint” of the material under investigation. Ruggedized, small scale devices for “in-field” use cases exist. Such devices might for example be used by companies in the food supply chain for inward inspection of goods. However, their application for the industrial Internet of Things workflows or scientific research is limited due to their proprietary nature. We propose an open platform for visible and near-infrared technology (OpenVNT), an open platform for capturing, transmitting, and analysing spectral measurements. It is built for use in the field, as it is battery-powered and transmits data wireless. To achieve high accuracy, the OpenVNT instrument contains two spectrometers covering a wavelength range of 400–1700 nm. We conducted a study on white grapes to compare the performance of the OpenVNT instrument against the Felix Instruments F750, an established commercial instrument. Using a refractometer as ground truth, we built and validated models to estimate the Brix value. As a quality measure, we used coefficient of determination of the cross-validation (R2CV) between the instrument estimation and ground truth. With 0.94 for the OpenVNT and 0.97 for the F750, a comparable R2CV was achieved for both instruments. OpenVNT matches the performance of commercially available instruments at one tenth of the price. We provide an open bill of materials, building instructions, firmware, and analysis software to enable research and industrial IOT solutions without the limitations of walled garden platforms.

TC Grafenau
DIGITAL

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

Abstract anzeigen

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

Das Unterstützungspotential von KI in der Pflege

In: INDIGO KI Campus Ostbayern: Interdisziplinäre Perspektiven auf KI

Universität Passau Passau

26.06.2022 (2022)

Angewandte Informatik
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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 Wirtschaftswissenschaften
Angewandte Informatik
TC Grafenau
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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

KI Arbeitskreis Juni 2019 Deggendorf

05.06.2019 (2019)

Angewandte Informatik
TC Grafenau
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Vortrag

Florian Wahl

Data Science with Python

In: Meetup-Reihe League of Geeks

Passau

17.10.2019 (2019)

TC Grafenau
Angewandte Informatik
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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
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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)

Angewandte Wirtschaftswissenschaften
Angewandte Informatik
TC Grafenau
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

Abstract anzeigen

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.

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