2021

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• Benjamin Völker, Marc Pfeifer, Florian Wolling, Philipp M. Scholl, Bernd Becker
  Introducing MILM - A Hybrid Minimal-Intrusive Load Monitoring Approach
  2021 The Twelfth ACM International Conference on Future Energy Systems (e-Energy ’21) ACM
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  Abstract

  The shift towards an advanced electricity metering infrastructure has gained traction because of several smart meter roll-outs. This accelerated research in Non-Intrusive Load Monitoring techniques. These techniques highly benefit from the temporal resolution improvements achieved by smart meters. Nevertheless, industrial adoption is low, not least because the achieved disaggregation performance is rather poor for unsupervised approaches. This work sketches a way to utilize intrusive sensors in combination with a standard NILM system to enhance training and maximize overall system's performance while minimizing the number of required intrusive sensors.
• Marc Pfeifer, Benjamin Völker, Sebastian Böttcher, Sven Köhler, Philipp M Scholl
  Teaching Embedded Systems by Constructing an Escape Room
  2021 In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE ’21), Virtual Event, New York, USA ACM
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  Abstract

  Embedded systems form the basis of many of today's consumer products and industrial applications - and they are increasingly connected. To teach this topic, we created a course with the overarching goal of designing and constructing an automated escape room. This provided the motivation for the students to learn the engineering and soft skills required for building networked embedded systems. The game was open for faculty members and friends of the students after course completion. By splitting the building process into multiple tasks, such as individual puzzles, the presented concept encourages inter- and intra-group work, including conceptualizing, designing and developing reliable, connected embedded systems. In this paper we first present the motivation, context, and pedagogical approach of the course. We then describe the course structure and conclude with experiences from constructing an escape room as a group.

2020

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• Benjamin Völker, Marc Pfeifer, Philipp M Scholl, Bernd Becker
  A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring
  2020 Energies
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  Abstract

  In order to reduce the electricity consumption in our homes, a first step is to make the user aware of it. Raising such awareness, however, demands to pinpoint users of specific appliances that unnecessarily consume electricity. A retrofittable and scalable way to provide appliance-specific consumption is provided by Non-Intrusive Load Monitoring methods. These methods use a single electricity meter to record the aggregated consumption of all appliances and disaggregate it into the consumption of each individual appliance using advanced algorithms usually utilizing machine-learning approaches. Since these approaches are often supervised, labelled ground-truth data need to be collected in advance. Labeling on-phases of devices is already a tedious process, but, if further information about internal device states is required (e.g., intensity of an HVAC), manual post-processing quickly becomes infeasible. We propose a novel data collection and labeling framework for Non-Intrusive Load Monitoring. The framework is comprised of the hardware and software required to record and (semi-automatically) label the data. The hardware setup includes a smart-meter device to record aggregated consumption data and multiple socket meters to record appliance level data. Labeling is performed in a semi-automatic post-processing step guided by a graphical user interface, which reduced the labeling effort by 72% compared to a manual approach. We evaluated our framework and present the FIRED dataset. The dataset features uninterrupted, time synced aggregated, and individual device voltage and current waveforms with distinct state transition labels for a total of 101 days.
• Benjamin Völker, Marc Pfeifer, Philipp M Scholl, Bernd Becker
  Annoticity: A Smart Annotation Tool and Data Browser for Electricity Datasets
  2020 5th International Workshop on Non-Intrusive Load Monitoring ACM
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  Abstract

  The growing request for eco-feedback and smart living concepts accelerated the development of Non-Intrusive Load Monitoring (NILM) algorithms during the last decade. Comparing and evaluating these algorithms still remains challenging due to the absence of a common benchmark datasets, and missing best practises for their application. Despite the fact that multiple datasets were recorded for the purpose of comparing NILM algorithms, many researchers still have to record their own dataset in order to meet the requirements of their specific application. Adding ground truth labels to these datasets is a cumbersome and time consuming process as it requires an expert to visually inspect all the data manually. Therefore, we propose the Annoticity inspection and labeling tool which simplifies the process of visualizing and labeling of electricity data. We use an event detector based on the log likelihood ratio test which achieved an F1 score of 90.07% in our experiments. Preliminary results indicate that the effort of generating event labels is reduced by 80.35% using our tool.
• Benjamin Völker, Marc Pfeifer, Philipp M Scholl, Bernd Becker
  FIRED: A Fully-labeled hIgh-fRequency Electricity Disaggregation Dataset
  2020 The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Virtual Event Japan November, 2020 ACM
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  Abstract

  As more and more homes are equipped with smart electricity meters, home owners gain the ability to monitor their total electricity consumption on a daily or hourly basis. Techniques such as load forecasting, load disaggregation, and activity recognition try to provide even better insights into our electricity consumption, highlight saving potential or improve our daily living. To develop and evaluate these techniques, publicly available datasets are used. We identified a lack of high frequency fully labeled electricity datasets in the residential domain and present the FIRED dataset. It contains 52 of 8kHz aggregated current and voltage readings of the 3-phase supply of a typical residential apartment in Germany. The dataset also contains synchronized ground truth data as 2kHz readings of 21 individual appliances, as well as room temperature readings and fully labeled state changes of the lighting system, resulting in a complete and versatile residential electricity dataset.

2019

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• Benjamin Völker, Marc Pfeifer, Philipp M Scholl, Bernd Becker
  A Versatile High Frequency Electricity Monitoring Framework for Our Future Connected Home
  2019 EAI International Conference on Sustainable Energy for Smart Cities, Braga Portugal Springer, pages: 221 - 231
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  Abstract

  In our homes a lot of devices are powered by electricity with- out us knowing the specific amount. As electricity production has a large, negative environmental impact, we should be more aware about how de- vices consume power and how we can adapt our daily routine to decrease our electricity requirements. Methods such as Non-Intrusive Load Mon- itoring (NILM) can provide the user with precise device level electricity data by measuring at a single point in a houses’ electricity network. However, the time resolution of most off-the-shelf power meters is not sufficient for NILM or the meters are locked down for security reasons. Therefore, we have developed our own versatile energy metering frame- work which consists of a high frequency electricity metering device, a versatile backend for data processing and a webapp for data visualiza- tion. The developed hardware is capable of sampling up to 32 kHz, while the software framework allows to extract other power related metrics such as harmonic content. The system’s application ranges from pro- viding transparent electricity usage to the user up to generating load forecasts with fine granularity.
• Marc Pfeifer, Philipp M. Scholl, Rainer Voigt, Bernd Becker
  Active Stereo Vision with High Resolution on an FPGA
  2019 27th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines Proceedings of the 27th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, pages: 118 - 126
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  Abstract

  We present a novel FPGA based active stereo vision system, tailored for the use in a mobile 3D stereo camera. For the generation of a single 3D map the matching algorithm is based on a correlation approach, where multiple stereo image pairs instead of a single one are processed to guarantee an improved depth resolution. To efficiently handle the large amounts of incoming image data we adapt the algorithm to the underlying FPGA structures, e.g. by making use of pipelining and parallelization. Experiments demonstrate that our approach provides high-quality 3D maps at least three times more energy-efficient (5.5 fps/W) than comparable approaches executed on CPU and GPU platforms. Implemented on a Xilinx Zynq-7030 SoC our system provides a computation speed of 12.2 fps, at a resolution of 1.3 megapixel and a 128 pixel disparity search space. As such it outperforms the currently best passive stereo systems of the Middlebury Stereo Evaluation in terms of speed and accuracy. The presented approach is therefore well suited for mobile applications, that require a highly accurate and energy-efficient active stereo vision system.

2017

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• Tobias Schubert, Benjamin Völker, Marc Pfeifer, Bernd Becker
  The Smart MiniFab: An Industrial IoT Demonstrator Anywhere at Any Time
  2017 Smart Education and e-Learning 2017, Vilamoura/Portugal, KES International Springer International Publishing, pages: 253 - 262

2016

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• Marc Pfeifer, Tobias Schubert, Bernd Becker
  PackSens: A Condition and Transport Monitoring System Based on an Embedded Sensor Platform
  2016 7th EAI International Conference on Sensor Systems and Software Proceedings of the 7th EAI International Conference on Sensor Systems and Software, pages: 81 - 92
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  Abstract

  As a consequence of the growing globalization, transports which need a safe handling are increasing. Therefore, this paper introduces an innovative transport and condition monitoring system based on a mobile embedded sensor platform. The platform is equipped with a variety of sensors needed to extensively monitor a transport and can be attached directly to the transported good. The included microcontroller processes all relevant data served by the sensors in a very power efficient manner. Furthermore, it provides possible violations of previously given thresholds through a standardized Near Field Communication (NFC) interface to the user. Since falls are one major cause of damages while transportation, the presented system is the first one that not only detects every fall but also analyses the fall height and other parameters related to the fall event in real-time on the platform. The whole system was tested in different experiments where all critical situations and in particular all fall situations have been detected correctly.

2014

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• Tobias Schubert, Marc Pfeifer, Bernd Becker
  Accurate Controlling of Velocity on a Mobile Robot
  2014 29th International Conference on Computers and Their Applications