TY - CONF A1 - Johann, Sergej A1 - Winkler, Nicolas P. A1 - Bartholmai, Matthias ED - Kourkoulis, S. K. T1 - Multi-sensor system for long-term monitoring with WiFi and LoRaWAN technology T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics - Extended abstracts N2 - In many cases, science falls back on self-developed prototype systems, which are used and developed for the measurement and execution of the tasks. About 80 % of the development is based on the same hardware design, which is used in only one application scenario and then discarded. For the most part, there are also uncalibrated sensors, since it is costly to calibrate a complicated sensor measurement system or the entire measurement chain since access to the sensor systems is not always available. This paper describes a conceptual design to implement a versatile sensor system with the motivation to fuse the data recording and data reception, which can cover large areas with the help of LoRaWAN (Long Range Wide Area Network) technology. To overcome disadvantages of LoRaWAN, namely the slow data rates, the proposed sensor system can also cover smaller areas with the widespread WiFi technology. An enormous advantage over individual complete systems in the form of a prototype, is the rapid expansion, uncomplicated calibration of the individual sensors and the ecological relief. A modular design is used, where individual stacks with sensors and peripherals can be added separately. The stacks are standalone low-power systems and can be calibrated, maintained, and replaced separately and do not require the entire measurement chain. The measured and sent values are stored locally on the main stack and sent to the data collector (gateway) and evaluated by means of the automated selection between WiFi and LoRaWAN. T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Athens, Greece DA - 20.09.2022 KW - Multisensor system KW - LoRaWAN KW - WiFi KW - Long term monitoring PY - 2022 SP - 1 EP - 2 AN - OPUS4-56462 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Hüllmann, Dino A1 - Winkler, Nicolas P. A1 - Schugardt, Jan ED - Rudnitskaya, A. T1 - Remote Drone-to-Drone Gas Sensing: A Feasibility Study T2 - 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) Proceedings N2 - Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or poorly accessible areas. A challenging task however, is the generation of three-dimensional distribution maps from these spatially sparse gas measurements. To obtain high-quality reconstructions, the choice of optimal measuring poses is of great importance. Remote gas sensors, that are commonly used in Robot Assisted Gas Tomography (RAGT), require reflecting surfaces within the sensor’s range, limiting the possible sensing geometries, regardless of whether the robots are ground-based or airborne. By combining ground and aerial robots into a heterogeneous swarm whose agents are equipped with reflectors and remote gas sensors, remote inter-robot gas measurements become available, taking RAGT to the next dimension – releasing those constraints. In this paper, we demonstrate the feasibility of drone-to-drone measurements under realistic conditions and highlight the resulting opportunities. T2 - 19th International Symposium on Olfaction and Electronic Nose CY - Aveiro, Portugal DA - 29.05.2022 KW - Aerial robot KW - TDLAS KW - Inter-robot measurements KW - Gas tomography KW - Plume PY - 2022 SN - 978-1-6654-5860-3 DO - https://doi.org/10.1109/isoen54820.2022.9789627 SP - 1 EP - 3 PB - IEEE CY - USA AN - OPUS4-54926 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Kohlhoff, Harald A1 - Johann, Sergej A1 - Erdmann, Jessica A1 - Winkler, Nicolas P. ED - Kourkoulis, S. K. T1 - The RASEM System: A Technical Overview T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics - Extended abstracts N2 - Occupational health is an important topic, especially in industry, where workers are exposed to airborne by-products (e.g., dust particles and gases). Therefore, continuous monitoring of the air quality in industrial environments is crucial to meet safety standards. For practical and economic reasons, high-quality, costly measurements are currently only carried out sparsely, both in time and space, i.e., measurement data are collected in single day campaigns at selected locations only. The project “Robot-assisted Environmental Monitoring for Air Quality Assessment in Industrial Scenarios” (RASEM) addresses this issue by bringing together the benefits of both – low- and high-cost – measuring technologies enabling costefficient long-term air quality monitoring in realtime: A stationary network of low-cost sensors that is augmented by mobile units carrying high-quality sensors. By mapping the distribution of gases and particles in industrial environments with the proposed RASEM system, measures can be identified to improve on-site working conditions much faster than using traditional methods. In this paper, we detail the technical aspects of RASEM and introduce the mobile platforms used. T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Poros, Greece DA - 20.09.2022 KW - RASEM KW - Dust sensor KW - Low-cost KW - Sensor network KW - Ground Robot KW - Aerial Robot PY - 2022 SP - 1 EP - 2 CY - Athens, Greece AN - OPUS4-55915 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Kohlhoff, Harald A1 - Säämänen, A. A1 - Kangas, A. A1 - Poikkimäki, M. A1 - Schaffernicht, E. A1 - Lilienthal, A. J. T1 - RASEM - Robot-Assisted Environmental Monitoring for Air Quality Assessment N2 - This presentation was held at the SAF€RA symposium and gives an overview to the research project RASEM and its results. T2 - 2022 SAF€RA Symposium CY - Rome, Italy DA - 19.05.2022 KW - Air Pollution KW - Air Quality KW - Environmental Monitoring KW - Mobile Robot Olfaction KW - Wireless Sensor Network PY - 2022 AN - OPUS4-54953 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Matsukura, H. A1 - Neumann, Patrick P. A1 - Schaffernicht, E. A1 - Ishida, H. A1 - Lilienthal, A. J. T1 - Super-Resolution for Gas Distribution Mapping: Convolutional Encoder Decoder Network N2 - 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. T2 - 19th International Symposium on Olfaction and Electronic Nose CY - Aveiro, Portugal DA - 29.05.2022 KW - Gas Distribution Mapping KW - Spatial Interpolation KW - Deep Learning KW - Super-Resolution KW - Sensor Network PY - 2022 AN - OPUS4-54954 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Kotlyar, O. A1 - Schaffernicht, E. A1 - Fan, H. A1 - Matsukura, H. A1 - Ishida, H. A1 - Neumann, Patrick P. A1 - Lilienthal, A. J. T1 - Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping N2 - 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. T2 - ROBOT2022: Fifth Iberian Robotics Conference CY - Zaragoza, Spain DA - 22.11.2022 KW - Gas Distribution Mapping KW - Spatial Interpolation KW - Sequential Learning KW - Convolutional LSTM PY - 2022 AN - OPUS4-56415 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Kohlhoff, Harald A1 - Säämänen, A. A1 - Schaffernicht, E. A1 - Lilienthal, A. J. T1 - Robot-Assisted Air Quality Monitoring N2 - This presentation gives an introduction to robot-assisted air quality monitoring based and shows results of the research project RASEM. T2 - The 6th Meeting of the Investigation Committee on Measurement of Odors and Applications of Their Psychological/Physiological Effects CY - Online meeting DA - 21.01.2022 KW - Environmental Monitoring KW - Air Pollution KW - Air Quality KW - Wireless Sensor Network KW - Mobile Robot Olfaction PY - 2022 AN - OPUS4-54251 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Schaffernicht, E. A1 - Lilienthal, A. J. A1 - Poikkimäki, M. A1 - Kangas, A. A1 - Säämänen, A. T1 - Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory N2 - 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. T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Poros, Greece DA - 20.09.2022 KW - Low-cost KW - Sensor network KW - Dust sensor KW - Sensor drift PY - 2022 AN - OPUS4-55908 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Matsukura, H. A1 - Neumann, Patrick P. A1 - Schaffernicht, E. A1 - Ishida, H. A1 - Lilienthal, A. J. ED - Rudnitskaya, A. T1 - Super-Resolution for Gas Distribution Mapping: Convolutional Encoder-Decoder Network T2 - 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) Proceedings N2 - 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. T2 - 19th International Symposium on Olfaction and Electronic Nose CY - Aveiro, Portugal DA - 29.05.2022 KW - Gas Distribution Mapping KW - Spatial Interpolation KW - Deep Learning KW - Super-Resolution KW - Sensor Network PY - 2022 SN - 978-1-6654-5860-3 DO - https://doi.org/10.1109/isoen54820.2022.9789555 SP - 1 EP - 3 PB - IEEE CY - USA AN - OPUS4-54955 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Winkler, Nicolas P. A1 - Neumann, Patrick P. A1 - Schaffernicht, E. A1 - Lilienthal, A. J. A1 - Poikkimäki, M. A1 - Kangas, A. A1 - Säämänen, A. ED - Kourkoulis, S. K. T1 - Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics - Extended abstracts N2 - 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. T2 - 38th Danubia-Adria Symposium on Advances in Experimental Mechanics CY - Poros, Greece DA - 20.09.2022 KW - Low-cost KW - Dust sensor KW - Sensor network KW - Sensor drift PY - 2022 SP - 1 EP - 2 CY - Athens, Greece AN - OPUS4-55909 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -