scholarly journals Balanced Distribution Adaptation for Metal Oxide Semiconductor Gas Sensor Array Drift Compensation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3403
Author(s):  
Zongze Jiang ◽  
Peng Xu ◽  
Yongbin Du ◽  
Feng Yuan ◽  
Kai Song
Keyword(s):  
Machine Learning ◽  
Metal Oxide ◽  
Gas Sensor ◽  
Metal Oxide Semiconductor ◽  
Compensation Method ◽  
Oxide Semiconductor ◽  
Data Set ◽  
Balanced Distribution ◽  
Drift Compensation ◽  
Balance Factor

Drift compensation is an important issue for metal oxide semiconductor (MOS) gas sensor arrays. General machine learning methods require constant calibration and a large amount of label gas data. At the same time, recalibration will cause a lot of costs, and label gas is difficult to obtain in practice. In this paper, a novel drift compensation method based on balanced distribution adaptation (BDA) is proposed. First, the BDA drift compensation method can adjust the conditional distribution and marginal distribution between the two domains through the weight balance factor, thereby more effectively reducing the mismatch between the two domains. When the BDA method performs classification tasks through machine learning, no labeled data is required in the target domain. Then, the particle swarm optimization algorithm is used to improve the accuracy of drift compensation. Individuals in the population are initialized randomly, and their fitness values are calculated. Iterative optimization of the population individuals is conducted until the optimal weight balance factor parameters are calculated. Finally, the BDA method is experimentally verified on the public gas sensor drift data set. Experimental results showed that the BDA method was significantly better than the existing joint distribution adaptation (JDA) method and other standard drift compensation methods such as K-Nearest Neighbor (KNN). In the two setting groups, the recognition accuracy was 4.54% and 1.62% ahead of the JDA method, and 12.23% and 15.83% ahead of the KNN method.

scholarly journals Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 647
Author(s):  
Tobias Baur ◽  
Johannes Amann ◽  
Caroline Schultealbert ◽  
Andreas Schütze
Keyword(s):  
Air Quality ◽  
Metal Oxide ◽  
Gas Sensor ◽  
Gas Sensors ◽  
Indoor Air ◽  
Ambient Air ◽  
Quantitative Agreement ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
High Temporal Resolution

More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard operating mode often does not make full use of their true capabilities. More sophisticated operation modes, extensive calibration and advanced data evaluation can significantly improve VOC measurements and, furthermore, achieve selective measurements of single gases or at least types of VOCs. This study provides an overview of the potential and limits of MOS gas sensors for IAQ monitoring using temperature cycled operation (TCO), calibration with randomized exposure and data-based models trained with advanced machine learning. After lab calibration, a commercial digital gas sensor with four different gas-sensitive layers was tested in the field over several weeks. In addition to monitoring normal ambient air, release tests were performed with compounds that were included in the lab calibration, but also with additional VOCs. The tests were accompanied by different analytical systems (GC-MS with Tenax sampling, mobile GC-PID and GC-RCP). The results show quantitative agreement between analytical systems and the MOS gas sensor system. The study shows that MOS sensors are highly suitable for determining the overall VOC concentrations with high temporal resolution and, with some restrictions, also for selective measurements of individual components.

Design and optimization of heating plate for metal oxide semiconductor gas sensor

2019 ◽  
Vol 25 (9) ◽  
pp. 3511-3519
Author(s):  
Yi Wu ◽  
Lijing Yuan ◽  
Zhongqiu Hua ◽  
Dong Zhen ◽  
Zhilei Qiu
Keyword(s):  
Metal Oxide ◽  
Gas Sensor ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Design And Optimization ◽  
Semiconductor Gas Sensor ◽  
Heating Plate

scholarly journals Development Of Portable Electronic Nose Device For Perfume Odour Classification

2012 ◽  
Author(s):  
Fauzan Khairi Che Harun ◽  
Nur Atiqah Ibrahim ◽  
Mohd Ariffanan Mohd Basri
Keyword(s):  
Metal Oxide ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Current Source ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Constant Current ◽  
Constant Current Source ◽  
Body Shop ◽  
National Instrument

Artikel ini membentangkan pembangunan sebuah hidung elektronik (e–Hidung) mudah alih berdasarkan kad perolehan data National Instrument dan LabView. Kajian ini merangkumi rekabentuk litar e–Hidung yang terdiri daripada pelbagai jenis semikonduktor oksida logam daripada FIGARO sebagai sensor gas. Rintangan dari setiap sensor gas diukur melalui litar arus tetap yang dikawal melalui LabView. Sumber arus tetap digunakan sebagai antara muka elektronik bolehubah yang membolehkan pemetaan voltan keluaran sensor untuk profil rintangan sensor dilakukan secara tepat. Rintangan dari setiap sensor dikira secara tepat dan dipaparkan oleh LabView. Keputusan kajian menunjukkan bahawa e – Hidung yang dihasilkan boleh mengesan dan mengklasifikasikan antara dua jenama terkenal iaitu Body Shop dan Avon. Kaedah analisis komponen utama (PCA) yang digunakan menunjukkan diskriminasi besar iaitu 99.53% untuk bau tersebut. Ini menunjukkan bahawa sistem yang dihasilkan mampu membezakan baud an akan digunakan untuk tugas yang lebih kompleks pada masa akan datang. Kata kunci: Hidung elektronik; semikonduktor oksida logam; sensor gas; pengecaman corak; This paper presents the development of a portable electronic nose (e–Nose) based on a National Instrument data acquisition card and LabView. The study includes the design of e–Nose circuits that consist of different types of metal oxide semiconductor from FIGARO as gas sensors. The resistances of each gas sensor are measured through a constant current circuit controlled via LabView. The constant current source is used as an adaptive electronic interface that allows the accurate mapping of the sensor’s voltage output to sensor resistance profiles. The resistance of each sensor is accurately computed and displayed by LabView. The result of the study showed that the created e – Nose can detect and classify between two famous brands Body Shop and Avon. The applied principal component analysis (PCA) method shows great discrimination of 99.53% for the mentioned odour. This suggests that the system is able to discriminate between simple odours and will be use for a more complex task in the future. Key words: Electronic Nose: metal oxide semiconductor; gas sensor; pattern recognition; PCA

A Low Power Cantilever-Based Metal Oxide Semiconductor Gas Sensor

2019 ◽  
Vol 40 (7) ◽  
pp. 1178-1181 ◽  
Author(s):  
Dongcheng Xie ◽  
Dongliang Chen ◽  
Shufeng Peng ◽  
Yujie Yang ◽  
Lei Xu ◽  
...  
Keyword(s):  
Metal Oxide ◽  
Low Power ◽  
Gas Sensor ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Semiconductor Gas Sensor

scholarly journals Calibration of Metal Oxide Semiconductor Gas Sensors by High School Students

2021 ◽  
Vol 17 (04) ◽  
pp. 4
Author(s):  
Sebastian Höfner ◽  
Andreas Schütze ◽  
Michael Hirth ◽  
Jochen Kuhn ◽  
Benjamin Brück
Keyword(s):  
High School Students ◽  
Air Quality ◽  
Metal Oxide ◽  
Gas Sensor ◽  
Gas Sensors ◽  
Objective Assessment ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Calibration Process ◽  
Wide Range

A wide range of pollutants cannot be perceived with human senses, which is why the use of gas sensors is indispensable for an objective assessment of air quality. Since many pollutants are both odorless and colorless, there is a lack of awareness, in particular among students. The project SUSmobil (funded by DBU – Deutsche Bundesstiftung Umwelt) aims to change this. In three modules on the topic of gas sensors and air quality, the students (a) learn the functionality of a metal oxide semiconductor (MOS) gas sensor, (b) perform a calibration process and (c) carry out environmental measurements with calibrated sensors. Based on these introductory experiments, the students are encouraged to develop their own environmental questions. In this paper, the student experiment for the calibration of a MOS gas sensor for ethanol is discussed. The experiment, designed as an HTML-based learning, addresses both theoretical and practical aspects of a typical sensor calibration process, consisting of data acquisition, feature extraction and model generation. In this example, machine learning is used for generating the evaluation model as existing physical models are not sufficiently exact.

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