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  • 1.
    Winkler, Nicolas P.
    et al.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Kotlyar, Oleksandr
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Matsukura, Haruka
    University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan.
    Ishida, Hiroshi
    Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo, Japan.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping2022In: ROBOT2022: Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2 / [ed] Danilo Tardioli; Vicente Matellán; Guillermo Heredia; Manuel F. Silva; Lino Marques, Springer, 2022, Vol. 590, p. 178-188Conference paper (Refereed)
    Abstract [en]

    To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.

  • 2.
    Winkler, Nicolas P.
    et al.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Matsukura, Haruka
    University of Electro-Communications, Tokyo, Japan.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Ishida, Hiroshi
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network2022In: 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE , 2022Conference paper (Refereed)
    Abstract [en]

    Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.

  • 3.
    Winkler, Nicolas P.
    et al.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Kohlhoff, Harald
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Erdmann, Jessica
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring2021In: SMSI 2021 Proceedings, 2021, p. 260-261Conference paper (Refereed)
    Abstract [en]

    A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown.

    Download full text (pdf)
    Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring
  • 4.
    Winkler, Nicolas P.
    et al.
    Örebro University, School of Science and Technology. Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Using Redundancy in a Sensor Network to Compensate Sensor Failures2021In: 2021 IEEE SENSORS, 2021Conference paper (Refereed)
    Abstract [en]

    Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.

  • 5.
    Winkler, Nicolas P.
    et al.
    Örebro University, School of Science and Technology. Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Using Redundancy in a Sensor Network to Compensate Sensor Failures2021In: 2021 IEEE SENSORS, IEEE , 2021Conference paper (Refereed)
    Abstract [en]

    Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.

  • 6.
    Winkler, Nicolas P.
    et al.
    Örebro University, School of Science and Technology. Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Poikkimäki, Mikko
    Finnish Institute of Occupational Health, Työterveyslaitos, Finland.
    Kangas, Anneli
    Finnish Institute of Occupational Health, Työterveyslaitos, Finland.
    Säämänen, Arto
    Finnish Institute of Occupational Health, Työterveyslaitos, Finland.
    Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory2022Conference paper (Refereed)
    Abstract [en]

    The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation.

    Download full text (pdf)
    Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory
  • 7.
    Winkler, Nicolas P.
    et al.
    Örebro University, School of Science and Technology. Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
    Säämänen, Arto
    Occupational Safety, Finnish Institute of Occupational Health, Tampere, Finland.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    High-quality meets low-cost: Approaches for hybrid-mobility sensor networks2020In: MATERIALS TODAY-PROCEEDINGS, Elsevier, 2020, Vol. 32, p. 250-253Conference paper (Refereed)
    Abstract [en]

    Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment's characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network's quality will be investigated.

1 - 7 of 7
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