The Research on Artificial Olfaction System-Electronic Nose

Li, C W; Wang, G D

doi:10.1088/1742-6596/48/1/125

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…This array of sensors generates the data from all individual sensors and this data generated is processed further to create a pattern. This pattern generated is in response to the individual gas sensor (Gardner, 1991;Li & Wang, 2006) . In this way, the database is created from the sensor array.…”

Section: : Algorithms For Discriminationmentioning

confidence: 99%

“…The study of these patterns can be done and a perfect database can be created by a lot of repetitions, then these databases can be called later as reference databases, and finally, we get the artificial intelligence in these sensor arrays (Chiu & Tang, 2013;S. Feng et al, 2019;Gardner, 1991;Hallil&Heidari, 2020;Li & Wang, 2006) . Following is the discussion of these sensor arrays and pattern recognition made for various gases.…”

Section: : Algorithms For Discriminationmentioning

confidence: 99%

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Review on Discrimination of Hazardous Gases by Smart Sensing Technology

Bandewad

Datta

Gawali

et al. 2023

AIA

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Real-time detection of hazardous gases in the ambient and indoors has become the prime motive for curbing the problem of air pollution. Keeping the concentration of hazardous gases in control is the main task before human society so as to keep environmental balance. Smart sensors are attracting researchers to focus on fabricating devices that could detect and predict the presence of gas in real-time, give accurate results about the gas concentration, detection of a target gas from the mixture of gases. This smart gas sensor system can have applications in the field of military, space, underwater, indoor, outdoors, factories, vehicles, and wearable smart devices. This study reviews recent advances in smart sensor technology with respect to the material structure, sensing technique, and discrimination algorithm. Focus is given on reducing the power consumption and area of a sensor circuitry with the help of different techniques.

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Section: : Algorithms For Discriminationmentioning

confidence: 99%

Section: : Algorithms For Discriminationmentioning

confidence: 99%

Review on Discrimination of Hazardous Gases by Smart Sensing Technology

Bandewad

Datta

Gawali

et al. 2023

AIA

View full text Add to dashboard Cite

show abstract

“…6. Uncited references Bishop, Bushnell, and Westland (1990), Bratko and Suc (2003), Coghill, Srinivasan, andKing (2008), Cristianini andShawe-Taylor (2000), Hau and Coiera (1997), Konik and Laird (2006), Li and Wang (2006), Mozina, Zabkar, and Bratko (2007), Price et al (2006), Agell (2006, 2008), and Sicard, Perrot, Leclercq-Perlat, Baudrit, and Corrieu (2011).…”

Section: The Pigmentary and The Colorimetric Spacesmentioning

confidence: 99%

A learning system for adjustment processes based on human sensory perceptions

Ruiz

Agell

Ángulo

et al. 2018

Cognitive Systems Research

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Creating, designing and adjusting products are essential decision processes underlying creative industries, such as painting, perfume, food and beverage industries. These processes require the participation and continuous supervision of professionals with highlydeveloped expert sensory abilities. Training of these experts is very complex due to the difficulty of transmitting intuitive knowledge obtained from perception. A new methodology for capturing this sensory expert knowledge that relies on a machine learning tool, previously trained with 'state-action' type patterns, jointly with an actions generator module, is proposed in this work. The method is based on a closed loop architecture together with the decomposition of complex sensory knowledge into basic elements capable of being handled by standard machine learning systems. A real case application to color-adjustment in the automotive paint manufacturing industry is presented showing the potential benefits of the method.

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Smart and Intelligent E‐nose for Sensitive and Selective Chemical Sensing Applications

Dhanekar

2020

Smart Sensors for Environmental and Medical Applications

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The Research on Artificial Olfaction System-Electronic Nose

Cited by 6 publications

References 7 publications

Review on Discrimination of Hazardous Gases by Smart Sensing Technology

Review on Discrimination of Hazardous Gases by Smart Sensing Technology

A learning system for adjustment processes based on human sensory perceptions

Smart and Intelligent E‐nose for Sensitive and Selective Chemical Sensing Applications

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