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- Fan, Han, 1989-, et al.
(author)
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Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
- 2019
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In: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN). - : IEEE.
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Conference paper (peer-reviewed)abstract
- Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment.
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| 2. |
- Fan, Han, 1989-, et al.
(author)
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Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
- 2019
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In: Sensors. - : MDPI. - 1424-8220. ; 19:3
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Journal article (peer-reviewed)abstract
- Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.
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| 3. |
- Xing, Yuxin, et al.
(author)
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FireNose on Mobile Robot in Harsh Environments
- 2019
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In: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 19:24, s. 12418-12431
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Journal article (peer-reviewed)abstract
- In this work we present a novel multi-sensor unit, a.k.a. FireNose, to detect and discriminate both known and unknown gases in uncontrolled conditions to aid firefighters under harsh conditions. The unit includes three metal oxide (MOX) gas sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infrared (NDIR) sensor optimized for the detection of CO2, a commercial temperature humidity sensor, and a flow sensor. We developed custom film coatings for the MOX sensors (SnO2, WO3 and NiO) which greatly improved the gas sensitivity, response time and lifetime of the miniature devices. Our proposed system exhibits promising performance for gas sensing in harsh environments, in terms of power consumption (∼ 35 mW at 350°C per MOX sensor), response time (<10 s), robustness and physical size. The sensing unit was evaluated with plumes of gases in both, a laboratory setup on a gas testing rig and on-board a mobile robot operating indoors. These high sensitivity, high-bandwidth sensors, together with online unsupervised gas discrimination algorithms, are able to detect and generate their spatial distribution maps accordingly. In the robotic experiments, the resulting gas distribution maps corresponded well to the actual location of the sources. Therefore, we verified its ability to differentiate gases and generate gas maps in real-world experiments.
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