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Auflistung nach Autor:in "Behrens, Grit"

1 - 10 von 34
  • Konferenzbeitrag
    5. Workshop Künstliche Intelligenz in der Umweltinformatik
    (INFORMATIK 2024, 2024) Abecker, Andreas; Behrens, Grit; Naumann, Stefan; Willenbacher, Martina
    Im Rahmen des INFORMATIK FESTIVAL 2024 der Gesellschaft für Informatik (GI) e.V. im Herbst 2024 in Wiesbaden findet die fünfte Auflage des Workshops KIU zur Nutzung von Methoden der Künstlichen Intelligenz in der Umweltinformatik statt. In der KIU-Workshopreihe werden seit 2020 anwendungsorientiert und interdisziplinär innovative Beiträge der KI für wichtige Fragen von Umweltschutz und Nachhaltigkeit vorgestellt und diskutiert. Auch der fünfte Workshop soll dabei helfen, eine deutschsprachige Wissenschafts- und Anwendungscommunity zu diesen Themen zu etablieren und konsolidieren, um langfristig die Kreativität und die Wirkung dieses wichtigen Aufgabenfelds zu unterstützen.
  • Textdokument
    8. Workshop Umweltinformatik zwischen Nachhaltigkeit und Wandel (UINW 2020)
    (INFORMATIK 2020, 2021) Naumann, Stefan; Voigt, Kristina; Kern, Eva; Wohlgemuth, Volker; Behrens, Grit
    Der Workshop „Umwelinformatik zwischen Nachhaltigkeit und Wandel“ schlägt auch in seinem bereits achten Durchlauf die Brücke zwischen Informatik, Umwelt-und Nachhaltigkeitswissenschaften und präsentiert interdisziplinäre wissenschaftliche Lösungen aus der Schnittstelle zwischen Digitalisierung und Nachhaltiger Entwicklung.
  • Textdokument
    Analyse von Heizungs- und Lüftungsverhalten mit Data Mining Methoden
    (INFORMATIK 2020, 2021) Westhäusser, Lutz; Nickel, David; Behrens, Grit; Schlender, Klaus
    In dem hier beschriebenen Projekt wird interdisziplinär mit Psychologen zusammen gearbeitet. Ziel der Arbeit ist es, Modelle zu entwickeln, um das Umweltverhalten von Hausbewohnern positiv zu beeinflussen und zu verstetigen. In der hier beschriebenen Arbeit werden die ersten Daten aus dem ‚Reallabor' Sennestadt genutzt, die in den Wohnungen von freiwilligen Studienteilnehmern zu ihrem Heizungs-und Lüftungsverhalten erhoben werden. Mittels Machine Learning Technologien werden diese Daten analysiert.
  • Konferenzbeitrag
    Analysis and evaluation of mobile apps with regard to resource efficiency and data volumes - Methodologies and tools
    (EnviroInfo 2022, 2022) Obergöker, Kira
    The impact of software on energy and resource consumption is receiving more and more attention. While the examination of desktop software already provides initial sresults and criteria for its evaluation, the consideration of mobile apps is not quite as advanced. This paper is a first step to get an overview of which methods and tools can be used to analyse the resource and data consumption of mobile apps and to evaluate their sustainability. First, I present the previous criteria for desktop software products. In the next step, I present an existing measurement environment for determining the data volume of mobile apps. I created simple environments to identify and test components that can be used to build new measurement environments. I evaluate and compare the measurement environments based on their results. This showed variations between the environments, but an internally equal proportionality. Finally, I used the results obtained to consider how mobile apps can be analysed in terms of their resource consumption, as well as
  • Konferenzbeitrag
    The application of image recognition methods to improve the performance of waste-to-energy plantsplants
    (EnviroInfo 2022, 2022) Schwark, Fenja; Garmatter, Henriette; Davila, Maria; Dawel, Lisa; Pehlken, Alexandra; Cyris, Fabian; Scharf, Roland
    In this paper, we present an image recognition method to improve the performance of waste-to-energy plants. Thermal treatment of waste in waste-to-energy plants is central for the treatment of municipal solid waste. The heterogeneous nature of municipal solid waste results in a fluctuating lower calorific value to which plant operation must be adapted. Compensating for drastic changes in the lower calorific value is challenging for plant operation and can require short-term interventions. Estimating the lower calorific value prior to the combustion process should reduce the number of short-term interventions. In this work, we propose a process-engineering approach to estimate the lower calorific value of waste as a new application of image recognition in waste-to-energy plants. The method is implemented using videos and sensor data from a case study in a real waste-to-energy plant in Germany.
  • Konferenzbeitrag
    Architecting for Sustainability
    (EnviroInfo 2022, 2022) Lago, Patricia; Greefhorst, Danny; Woods, Eoin
    Sustainability is becoming an increasingly important topic. Information Technology (IT) is an important factor for sustainability; it consumes a substantial, and growing, part of the world supply of energy, but it can also enable significant insights and improvements related to sustainability. These factors need to be taken into account in the design of IT systems, meaning that we need to architect for sustainability. This paper provides insights into the experience and beliefs of IT practitioners and researchers into current and desired practices of architecting for sustainability. It reports on the results of three workshop sessions with practitioners and researchers, providing insight into the state of research and practice.
  • Konferenzbeitrag
    An Artificial Intelligence of Things based Method for Early Detection of Bark Beetle Infested Trees
    (EnviroInfo 2022, 2022) Knebel, Peter; Appold, Christian; Guldner, Achim; Horbach, Marius; Juncker, Yasmin; Müller, Simon; Matheis, Alfons
    Bark beetles, like the European Spruce Bark Beetle (Ips typographus), are inherent partsof a forest ecosystem. However, with favorable conditions, they can multiply quickly and infest vastamounts of trees and cause their extinction. Therefore, it is important for forest officials and rangers ofe. g. a national park, to monitor the population of the beetles and the infested trees. There are severalways to approach this, but they are often costly and time-consuming. Therefore, we design and test abark beetle early warning system with AI-based data analysis: Audio data, data on pheromones andinformation for a drought stress assessment of the affected trees are to be collected and used as a basisfor the analysis. The aim is to devise a micro-controller-based sensor system that detects the infestationof a tree as early as possible and warns the forest officials, e. g. via a message on their cell phone.
  • Konferenzbeitrag
    BITS: A Key Performance Indicators (KPIs) supported approach to assess traffic safety for cyclists at intersections in the Netherlands
    (EnviroInfo 2022, 2022) Schering, Johannes; Gómez, Jorge Marx
    Traffic safety is an important factor in the decision process whether people decide to use the bicycle or not. Critical situations that do not lead to an accident are often not reported to the police. To fill this knowledge gap, several regions as the city of Zwolle and the Province of Friesland (Netherlands) have started to detect near accidents at intersections among vehicles and bicycles by 3D camera data to evaluate traffic safety. Four intersections in Friesland and Zwolle were monitored. Different types of intersections (e.g. shared space concept) were considered. Near accidents can be divided into different conflict categories depending on vehicle speed and time to collision (Post-Encroachment Time PET). The preprocessed data including Key Performance Indicators (KPIs) to make cycling safety at the intersections measurable and comparable are provided. Based on the numbers and visualizations, it will be discussed which of the discussed intersections show critical profiles regarding numbers of near accidents, distribution and amount of very critical situations. With the results the intersections can be adjusted to increase traffic safety.Encroachment Time PET). The pre-processed data including relevant Key Performance Indicators (KPIs) to make cycling safety at the intersections measurable and comparable are provided. Based on the numbers and visualizations, it will be discussed which of the dis-cussed intersections show critical profiles regarding the total numbers of near accidents, its distribution and the amount of very critical situations. Based on the results the intersections can be adjusted to increase the safety situation in a city.
  • Konferenzbeitrag
    Classification of consumption data for energy management with Smart Metering
    (EnviroInfo & ICT4S, Adjunct Proceedings, 2015) Domnik, Alexander; Ertelt, Sven; Steckel, Florian; Witthaus, Fabian; Behrens, Grit
  • Konferenzbeitrag
    Cloud-based Processing of data from Non-Target-Analysis for Tracking Micropollutants in Surface Water
    (EnviroInfo 2022, 2022) Pauw, Viktoria; Hayek Mohamad; Shojaei, Elham; Hachinger, Stephan; Müller, Uwe; Bader, Tobias
    Tens of thousands of chemicals used by consumers, agriculture and industry enter the aquatic environment as micropollutants every day. Using targeted analysis we are so far only able to detect a small subset of the chemicals that are present. Therefore so called non-target screening (NTS) using liquid chromatography in combination with high-resolution mass spectrometry (LCHRMS) is increasingly used by labs to perform more comprehensive monitoring. However, a high degree of variance in measurements and processing workflows results in low comparability of data from separate laboratories. On one hand this is caused by differences in processing techniques which are due to stationary laboratory equipment and on the other hand by differing priorities in the detection strategy and evaluation workflow. The K2I project funded by BMBF aims at fostering collaboration between laboratories by providing a joint platform for uploading and processing LCHRMS data. A cloud based datalake and processing pipeline is being developed. A standardized processing workflow can then be executed which is enhanced by data mining tools including machine learning techniques. An indexing and searching software is employed to create a web based access to the processed data for participants.