
Marcus Venzke

I am in the Institute of Telematics since 1997, having the position of the senior engineer (Oberingenieur) since 2004. Find my private homepage at www.MarcusVenzke.de.
Teaching
- Informatics for Process Engineers
- Intelligente Stromnetze: Kommunikation, Regelung, Sicherheit und Privatsphäre
Projects
- CyEntEE - Cyber Physical Energy Systems – Sustainability, Resilience and Economics
- WinOSens - Ultra-low-power and ultra-low-performance asset localization to increase efficiency and transparency in industrial logistics processes with the help of machine learning in embedded sensor systems
Publications
Kai Hoth, Tom Steffen, Béla Wiegel, Amine Youssfi, Davood Babazadeh, Marcus Venzke, Christian Becker, Kathrin Fischer and Volker Turau. Holistic Simulation Approach for Optimal Operation of Smart Integrated Energy Systems under Consideration of Resilience, Economics and Sustainability. Infrastructures, 6(11), October 2021.
@Article{MDPI_CyEntEE_Simulation_Smart_Energy_System_2021,
author = {Kai Hoth and Tom Steffen and Béla Wiegel and Amine Youssfi and Davood Babazadeh and Marcus Venzke and Christian Becker and Kathrin Fischer and Volker Turau},
title = {Holistic Simulation Approach for Optimal Operation of Smart Integrated Energy Systems under Consideration of Resilience, Economics and Sustainability},
pages = ,
journal = {Infrastructures},
volume = {6},
number = {11},
publisher = {MDPI},
month = oct,
year = 2021,
}
Abstract:
The intermittent energy supply from distributed resources and the coupling of different energy and application sectors play an important role for future energy systems. Novel operational concepts require the use of widespread and reliable Information and Communication Technology (ICT). This paper presents the approach of a research project that focuses on the development of an innovative operational concept for a Smart Integrated Energy System (SIES), which consists of a physical architecture, ICT and energy management strategies. The cellular approach provides the architecture of the physical system in combination with Transactive Control (TC) as the system’s energy management framework. Independent dynamic models for each component, the physical and digital system, operational management and market are suggested and combined in a newly introduced co-simulation platform to create a holistic model of the integrated energy system. To verify the effectiveness of the operational concept, energy system scenarios are derived and evaluation criteria are suggested which can be employed to evaluate the future system operations.
Marcus Venzke, Daniel Klisch, Philipp Kubik, Asad Ali, Jesper Dell Missier and Volker Turau. Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems. Technical Report Report arXiv:2012.08403, arXiv.org e-Print Archive - Computing Research Repository (CoRR), Cornell University, December 2020.
@TechReport{Telematik_Venzke_ANNsES,
author = {Marcus Venzke and Daniel Klisch and Philipp Kubik and Asad Ali and Jesper Dell Missier and Volker Turau},
title = {Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems},
number = {Report arXiv:2012.08403},
institution = {arXiv.org e-Print Archive - Computing Research Repository (CoRR)},
address = {Cornell University},
month = dec,
year = 2020,
}
Abstract:
In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost microcontrollers with a few kilobytes of memory to semantically enrich data captured by sensors. The focus is on classifying temporal data series with a high level of reliability. Design and implementation of ANNs are analyzed considering Feed Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs). We validate the developed ANNs in a case study of optical hand gesture recognition on an 8-bit microcontroller. The best reliability was found for an FFNN with two layers and 1493 parameters requiring an execution time of 36 ms. We propose a workflow to develop ANNs for embedded devices.
Uwe Bartels, Marcus Venzke, Maurice Girod, Maciej Mühleisen and Christoph Petersen. Method for transmitting prioritized data and a transmitter. Technical Report United States Patent, No. 10,230,655 B2, Airbus Operations GmbH, March 2019.
@TechReport{Patent_Darsy_Airbus,
author = {Uwe Bartels and Marcus Venzke and Maurice Girod and Maciej M{\"u}hleisen and Christoph Petersen},
title = {Method for transmitting prioritized data and a transmitter},
number = {United States Patent, No. 10,230,655 B2},
institution = {Airbus Operations GmbH},
address = {},
month = mar,
year = 2019,
}
Abstract:
Described is a method for transmitting continuously created data items from an aircraft to a receiver. The data items are of a plurality of data types and each have a different priority. For each data type a live LIFO buffer and a main LIFO buffer are provided. In a regular operation mode continuously created data items are continuously stored in the main buffers. In a transmission operation mode continuously created data items are continuously stored in the live buffers, consecutive data packets are transmitted and for each data packet the data is selected from the buffers, wherein data items stored in live buffers are transmitted before data items stored in main buffers and data items of higher priorities are transmitted before data items of lower priorities. Further, a transmitter and an aircraft are described and claimed.
The complete list of publications is available separately.
Supervised Theses
Ongoing Theses
- Feature Engineering for Sensor Based Location Awareness using Decision Trees
- Generation of Synthetic Training Data for Sensor-Based Location Awareness
Completed Theses
- Sensor Based Location Awareness with LSTM-Networks
- Sensor Based Location Awareness with Decision Trees
- Co-Simulation of an FMU in OMNeT++
- Hand gesture recognition with decision trees
- Training rekurrenter neuronaler Netze als zwei Feedforward-Netze
- A case study of gesture control with artificial neural networks for a coffee machine
- Neuroevolution using the example of the optical gesture recognition
- Zuverlässige Gestenerkennung mit künstlichen neuronalen Netzen
- Optical gesture recognition using artificial neural networks for small embedded systems
- Model for Realistic Samples of Waterbeds for Simulation in Demand Response Studies
- Neuronale Netze zur Lastprognose von Wasserbetten mit Demand-Response
- Heuristic Day-Ahead Real-Time-Pricing for Demand Response with Waterbeds
- Heuristic Optimization of Day-Ahead Price Functions based on Load Forecasts for Demand Response with Waterbeds
- Load forecasting and load control of waterbeds for demand response
- Comparison of Demand Response Approaches for Waterbeds Using Simulation
- Development of a module for consumer-side power network analysis
- A Demand-Response Algorithm Based on Linear Programming
- Simulation of controlling domestic water heaters for demand response
- Analysing human running styles with an embedded system containing accelerometers
- Architektur zur Steuerung von heterogenen Smart-Home-Umgebungen durch Smartphones
- Model of energy production of an electromagnetic energy harvester
- An embedded system with accelerometer for analysing human running styles
- Schneller Hotspotwechsel bei IEEE 802.11b/g-Funknetzen
- Dienstqualität des WLANs auf dem Flughafen Hamburg in Abhängigkeit von Wetter, Ort und anderen Einflüssen