TY - JOUR A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Säämänen, A. A1 - Schaffernicht, E. A1 - Lilienthal, A. J. ED - Zemčík, R. ED - Krystek, J. T1 - High-quality meets low-cost: Approaches for hybrid-mobility sensor networks N2 - 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. T2 - 36th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Pilsen, Czech Republic DA - 24.09.2019 KW - Mobile robot olfaction KW - Air quality monitoring KW - Wireless sensor network KW - Gas distribution mapping KW - Occupational health PY - 2020 DO - https://doi.org/10.1016/j.matpr.2020.05.799 VL - 32 SP - 250 EP - 253 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-51108 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Winkler, Nicolas P. A1 - Kotlyar, O. A1 - Schaffernicht, E. A1 - Matsukura, H. A1 - Ishida, H. A1 - Neumann, Patrick P. A1 - Lilienthal, A. J. T1 - Super-resolution for Gas Distribution Mapping N2 - Gas Distribution Mapping (GDM) is a valuable tool for monitoring the distribution of gases in a wide range of applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This study aims to address the lack of deep learning approaches in GDM and explore their potential for improved mapping of gas distributions. In this paper, we introduce Gas Distribution Decoder (GDD), a learning-based GDM method. GDD is a deep neural network for spatial interpolation between sparsely distributed sensor measurements that was trained on an extensive data set of realistic-shaped synthetic gas plumes based on actual airflow measurements. As access to ground truth representations of gas distributions remains a challenge in GDM research, we make our data sets, along with our models, publicly available. We test and compare GDD with state-of-the-art models on synthetic and real-world data. Our findings demonstrate that GDD significantly outperforms existing models, demonstrating a 35% improvement in accuracy on synthetic data when measured using the Root Mean Squared Error over the entire distribution map. Notably, GDD appears to have superior capabilities in reconstructing the edges and characteristic shapes of gas plumes compared to traditional models. These potentials offer new possibilities for more accurate and efficient environmental monitoring, and we hope to inspire other researchers to explore learning-based GDM. KW - Gas distribution mapping KW - Spatial interpolation KW - Sensor networks KW - Deep learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607786 DO - https://doi.org/10.1016/j.snb.2024.136267 SN - 0925-4005 VL - 419 SP - 1 EP - 12 PB - Elsevier B.V. AN - OPUS4-60778 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -