Low-power and reliable gas sensing system based on recurrent neural networks

Kwon, Dongseok; Jung, Gyuweon; Shin, Wonjun; Jeong, Yujeong; Hong, Seongbin; Oh, Seongbin; Bae, Jong-Ho; Park, Byung‐Gook; Lee, Jong-Ho

doi:10.1016/j.snb.2020.129258

Cited by 20 publications

(9 citation statements)

References 34 publications

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“…Thanks to its dependable solutions, speed, affordability, and miniaturization, the e-nose is used in a variety of industries, including agriculture, food, medicine, and security [10][11][12][13][14]. Electronic nose systems with oxide semiconductor gas sensor arrays were thought to be promising platforms that could be improved to identify gases and odors using machine learning [15][16][17]. Due to their high sensitivity, quick response times, and simple structures, oxide semiconductor gas sensors have been widely used to detect harmful and toxic gases; however, the sensors' poor gas selectivity frequently limited their ability to detect these gases [18].…”

Section: B Electronic Nose Systemmentioning

confidence: 99%

A Study of Drift Effect in a Popular Metal Oxide Sensor and Gas Recognition Using Public Gas Datasets

et al. 2023

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Metal oxide sensor is widely used in many research fields, including E-nose for gas detection due to their tunable sensitivity, space efficiency and low cost. One of the most popular open data sets in electronic nose research contains data on various gases sampled using a MOx sensor in a wind tunnel over 16 months. A recent study published in 2022 by Nik Dennler has reported the discovery of the drift effect of a public dataset due to incorrect experimental design. they reported that the order of gas collection was not randomized and further discovered that a select set of gases were collected over a particular period. This paper expands the previous paper, by analyzing the drift effect with low signal, zero-offset subtracted signal's mean, and standard deviation value by location and time, and examining it with TSNE, a dimensional reduction method. In addition, the accuracy by time and location was analyzed by applying it to various Deep Learning methods. According to the results, we confirmed that gas information is already classified before the gas leaks in terms of temporal and spatial domain. Therefore, the classification accuracy overestimates the actual accuracy that can be obtained due to the drift effect. Based on the results of this study, it is necessary to thoroughly verify the temporal and spatial validity of the gas dataset when using the publicly available gas dataset to develop gas detection algorithms.

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Section: B Electronic Nose Systemmentioning

confidence: 99%

A Study of Drift Effect in a Popular Metal Oxide Sensor and Gas Recognition Using Public Gas Datasets

et al. 2023

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“…In sensor test set-ups, several mass flow meters are most often used. The set-ups are prepared for the use of several gases in a shift [ 20 , 72 , 73 , 74 , 75 ]. This is a better solution (measurement errors were limited) than using one gas line and one flow meter with simultaneous cylinder replacement or switching [ 76 ], although this is much more expensive.…”

Section: Semiconductor Gas Sensors—electrical Resistance Measurementsmentioning

confidence: 99%

“…In 2020, Kwon et al [ 73 ] described a study in which gas was admitted to the measuring chamber with the tested sensor for only thirty microseconds, while the ventilation of the chamber lasted five seconds. Such short gas delivery times require precise valve control, small volumes in the system and high flows in relation to the connection volume and the measuring chamber.…”

Section: Semiconductor Gas Sensors—electrical Resistance Measurementsmentioning

confidence: 99%

A Review of Gas Measurement Set-Ups

Fuśnik¹,

Szafraniak²,

Paleczek³

et al. 2022

Sensors

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Measurements of the properties of gas-sensitive materials are a subject of constant research, including continuous developments and improvements of measurement methods and, consequently, measurement set-ups. Preparation of the test set-up is a key aspect of research, and it has a significant impact on the tested sensor. This paper aims to review the current state of the art in the field of gas-sensing measurement and provide overall conclusions of how the different set-ups impact the obtained results.

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“…[ 15 , 16 , 17 ] However, when data are transferred from the sensor to the processors, hardware cost and energy consumption can be increased by the number of required components, such as the conversion circuits used for the signal preprocessing. [ 18 , 19 , 20 ] In addition, implementing multistage data processing imposes a heavy workload on hardware systems with a von Neumann architecture, which is composed of a few subcomponents, e.g., a central processing unit (CPU), memory, etc. The input and output data bus between memory and processors also inevitably add hardware cost and power consumption.…”

Section: Introductionmentioning

confidence: 99%

Artificial Olfactory Neuron for an In‐Sensor Neuromorphic Nose

et al. 2022

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A neuromorphic module of an electronic nose (E-nose) is demonstrated by hybridizing a chemoresistive gas sensor made of a semiconductor metal oxide (SMO) and a single transistor neuron (1T-neuron) made of a metal-oxide-semiconductor field-effect transistor (MOSFET). By mimicking a biological olfactory neuron, it simultaneously detects a gas and encoded spike signals for in-sensor neuromorphic functioning. It identifies an odor source by analyzing the complicated mixed signals using a spiking neural network (SNN). The proposed E-nose does not require conversion circuits, which are essential for processing the sensory signals between the sensor array and processors in the conventional bulky E-nose. In addition, they do not have to include a central processing unit (CPU) and memory, which are required for von Neumann computing. The spike transmission of the biological olfactory system, which is known to be the main factor for reducing power consumption, is realized with the SNN for power savings compared to the conventional E-nose with a deep neural network (DNN). Therefore, the proposed neuromorphic E-nose is promising for application to Internet of Things (IoT), which demands a highly scalable and energy-efficient system. As a practical example, it is employed as an electronic sommelier by classifying different types of wines.

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Low-power and reliable gas sensing system based on recurrent neural networks

Cited by 20 publications

References 34 publications

A Study of Drift Effect in a Popular Metal Oxide Sensor and Gas Recognition Using Public Gas Datasets

A Study of Drift Effect in a Popular Metal Oxide Sensor and Gas Recognition Using Public Gas Datasets

A Review of Gas Measurement Set-Ups

Artificial Olfactory Neuron for an In‐Sensor Neuromorphic Nose

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