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 - 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 - 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 - 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. ED - Tardioli, D. ED - Matellán, V. ED - Heredia, G. ED - Silva, M. F. ED - Marques, L. T1 - Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping T2 - ROBOT2022: Fifth Iberian Robotics Conference 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 - 2023 SN - 978-3-031-21061-7 DO - https://doi.org/10.1007/978-3-031-21062-4_15 SP - 178 EP - 188 PB - Springer International Publishing AN - OPUS4-56414 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Haratsu, T. A1 - Sakaue, M. A1 - Matsukura, H. A1 - Neumann, Patrick P. A1 - Ishida, H. ED - Tardioli, D. ED - Matellán, V. ED - Heredia, G. ED - Silva, M. F. ED - Marques, L. T1 - Simulating a Gas Source Localization Algorithm with Gas Dispersion Produced by Recorded Outdoor Wind T2 - Proceedings of the Fifth Iberian Robotics Conference (ROBOT2022) N2 - This paper reports the use of the first gas dispersion simulator capable of introducing large wind fluctuations into simulations. The proposed simulator enables testing of a modification made to a gas source localization algorithm in a realistic scenario in order to study how the change affects it. Gas source localization in an outdoor environment is a challenging task mainly due to the complexity of the gas spread caused by the unpredictable nature of constantly changing wind. Therefore, a novel use of outdoor wind in developing a gas source localization system by simulation is presented in this paper. To consider the characteristic of sudden but large and unpredictable changes in wind direction, we propose to use recorded outdoor wind to simulate a realistic outdoor gas dispersion which has been done for the first time to the best of our knowledge. With the use of this simulator, we have tested a modification to a mobile robot-based gas source localization algorithm. Multiple simulations of the modified and the original particle filter-based algorithm have been done to study the effect of the tested modification. The results showed that a small difference in the algorithm can greatly impact the results. From this study, we show that the use of simulation consisting of the necessary traits to evaluate outdoor gas source localization, has the potential to accelerate the development of a reliable localization system. T2 - ROBOT2022: Fifth Iberian Robotics Conference CY - Zaragoza, Spain DA - 23.11.2022 KW - Gas Dispersion Simulator KW - Gas Source Localization KW - Mobile Robot Olfaction KW - Particle Filter PY - 2023 SN - 978-3-031-21061-7 DO - https://doi.org/10.1007/978-3-031-21062-4_9 SP - 105 EP - 116 PB - Springer AN - OPUS4-56416 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Moufid, M. A1 - Tiebe, Carlo A1 - El Bari, N. A1 - Bartholmai, Matthias A1 - Bouchikhi, B. T1 - Advance in electronic nose technology developed for the detection and discrimination of ethanol, ammonia, and hydrogen sulfide gases T2 - IEEE International symposium on olfaction and electronic nose (ISOEN 2022) N2 - This work focuses on the design and fabrication of low-cost and fast-response of an electronic nose (E-nose) based on semiconductor gas sensors, for discriminating some synthetic gases such as ammonia (NH3), ethanol (C2H5OH), and hydrogen sulfide (H2S). Additionally, the capability of separating different concentration levels of each considered gases was checked. Dataset treatment of E-nose by using Principal Component Analysis (PCA) showed a good discrimination of the different synthetic gases. Furthermore, perfect classification was reached of different concentration levels of the analysed gases by using Discriminant Function Analysis (DFA). In the light of these results, it could be stated that the developed E-nose system constitutes an inexpensive, rapid, simple to use, and efficient tool for synthetic gases detection. T2 - IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) CY - Aveiro, Portugal DA - 29.05.2022 KW - Electronic nose KW - Metal oxide semiconductor KW - Chemometric techniques KW - Environmental analysis PY - 2022 SN - 978-1-6654-5860-3 DO - https://doi.org/10.1109/ISOEN54820.2022.9789636 SP - 1 EP - 3 AN - OPUS4-56574 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Moufid, M. A1 - Tiebe, Carlo A1 - El Bari, N. A1 - Bartholmai, Matthias A1 - Bouchikhi, B. T1 - Combining of TD-GC-MS and home developed electronic nose for road traffic air monitoring T2 - 2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS) N2 - In this work, we demonstrate the ability of an electronic nose system based on an array of six-semiconductor gas sensors for outdoor air quality monitoring over a day at a traffic road in downtown of Meknes city (Morocco). The response of the sensor array reaches its maximum in the evening of the investigated day which may due to high vehicular traffic or/and human habits resulting in elevated concentrations of pollutants. Dataset treatment by Principal Component Analysis and Discriminant Function Analysis shows a good discrimination between samples collected at different times of the day. Moreover, Support Vector Machines were used and reached a classification success rate of 97.5 %. Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS) technique was used to validate the developed e-nose system by identifying the composition of the analyzed air samples. The discrimination obtained by e-nose system was in good agreement with the TD-GC-MS results. This study demonstrates the usefulness of TD-GC-MS and e-nose, providing high accuracy in discriminating outdoor air samples collected at different times. This demonstrates the potential of using the e-nose as a rapid, easy to use and inexpensive environmental monitoring system. T2 - 2021 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS) CY - Sfax, Tunisia DA - 07.06.2021 KW - GC-MS KW - Electronic nose KW - Gas sensors KW - Urban air monitoring PY - 2021 DO - https://doi.org/10.1109/DTS52014.2021.9498110 SP - 1 EP - 6 AN - OPUS4-54401 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Neumann, Patrick P. A1 - Hirschberger, Paul A1 - Bartholmai, Matthias T1 - Flying Ant Robot – Aerial Chemical Trail Detection and Localization T2 - Proceedings of the IEEE Sensors 2021 N2 - This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize the chemical trail, a novel detection criterion was developed that uses only relative changes in the transient phase of the sensor response, making it more robust in its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot is able to correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future. T2 - IEEE Sensors 2021 CY - Online meeting DA - 31.10.2021 KW - Nano aerial robot KW - Trail following KW - Trail detection KW - Localization PY - 2021 DO - https://doi.org/10.1109/sensors47087.2021.9639857 SP - 1 EP - 4 PB - IEEE AN - OPUS4-53933 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. T1 - Using Redundancy in a Sensor Network to Compensate Sensor Failures T2 - 2021 IEEE SENSORS N2 - 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. T2 - IEEE SENSORS 2021 CY - Online meeting DA - 31.10.2021 KW - Environmental monitoring KW - Wireless sensor network KW - Sensor placement KW - Machine learning PY - 2021 SN - 978-1-7281-9501-8 DO - https://doi.org/10.1109/sensors47087.2021.9639479 SP - 1 EP - 4 PB - IEEE AN - OPUS4-53939 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wu, Cheng-Chieh A1 - Völker, Daniel A1 - Weisbrich, S. A1 - Neitzel, F. ED - Holl, H. T1 - The Finite Volume Method in point of view of Finite Element Method T2 - Book of Abstracts 37th Danubia Adria Symposium on Advances in Experimental Mechanics N2 - The best-known discretization methods for solving engineering problems formulated as partial differential equations are finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). While the finite volume method is used in fluid mechanics, the finite element method is predominant in solid state mechanics. At first glance, FVM and FEM are two highly specialized methods. However, both methods can solve problems of both solid mechanics and fluid mechanics well. Since experimental mechanics deals not only with solid state physics but also with fluid mechanics problems, we want to understand FVM in the sense of FEM in this work. In the long term, we want to use the variational calculus to unify many important numerical methods in engineering science into a common framework. In this way, we expect that experiences can be better exchanged between different engineering sciences and thus innovations in the field of experimental mechanics can be advanced. But in this work, we limit ourselves to the understanding of the FVM with the help of the variational calculus already known in FEM. We use a simple 1D Poisson equation to clarify the point. First, we briefly summarize the FVM and FEM. Then we will deal with the actual topic of this paper, as we establish the FEM and the FVM on a common basis by variation formulation. It is shown here that the FVM can be understood in terms of the finite element method with the so-called Galerkin-Petrov approach. T2 - 37th Danubia Adria Symposium on Advances in Experimental Mechanics CY - Linz, Österreich DA - 21.09.2021 KW - Finite element method KW - Finite volume method KW - Variational calculation KW - Simulation KW - Computational physics PY - 2021 SN - 978-3-9504997-0-4 SP - 12 EP - 13 AN - OPUS4-53422 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -