A review of algorithms for SAW sensors e-nose based volatile compound identification
| Autor: | Christine Mer-Calfati, Emmanuel Scorsone, Jean-Philippe Poli, Samuel Saada, Olivier Hotel |
|---|---|
| Přispěvatelé: | Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire Capteurs Diamant (LCD-LIST), French trans-governmental CBRN-E R&D program., Intelligence Artificielle et Apprentissage Automatique (LI3A), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
ELECTRONIC-NOSE online learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Odour recognition NEURAL NETWORK 02 engineering and technology SAW sensors 01 natural sciences Electronic nose CLASSIFICATION diamond [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] [CHIM.ANAL]Chemical Sciences/Analytical chemistry Machine learning trustworthy artificial intelligence Materials Chemistry medicine PATTERN RECOGNITION Electrical and Electronic Engineering [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics signal processing Instrumentation Nose Data processing NEURAL-NETWORK Artificial neural network INDEPENDENT COMPONENT ANALYSIS 010401 analytical chemistry Metals and Alloys artificial intelligence 021001 nanoscience & nanotechnology Condensed Matter Physics 0104 chemical sciences Surfaces Coatings and Films Electronic Optical and Magnetic Materials Support vector machine Identification (information) SUPPORT VECTOR MACHINES medicine.anatomical_structure Pattern recognition (psychology) PATTERN-RECOGNITION fuzzy logic 0210 nano-technology Algorithm |
| Zdroj: | Sensors and Actuators B: Chemical Sensors and Actuators B: Chemical, Elsevier, 2018, 255 (3), pp.2472-2482. ⟨10.1016/j.snb.2017.09.040⟩ Sensors and Actuators B: Chemical, 2018, 255, Part 3, pp.2472-2482. ⟨10.1016/j.snb.2017.09.040⟩ |
| ISSN: | 0925-4005 |
| Popis: | International audience; Recent advances in odour sensors have led to the development of new applications; among them, electronic noses have gained major interest and found successful applications in many fields. An electronic nose is a device composed of an array of odour sensors with sensitivity to a wide range of chemical compounds. Reliable electronic nose systems rely on advanced data processing techniques. Among them, machine learning has become a core technique for electronic nose design. In this document, we describe several machine learning algorithms and compare their performances on different features used in state of the art electronic nose systems. |
| Databáze: | OpenAIRE |
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