Gas sensor array low-concentration

Donated on 11/16/2024

This dataset contains 6 gas responses collected by a sensor array consisting of 10 metal oxide semiconductor sensors, with gas concentrations at the ppb level (below the minimum detection limit of the sensors)

Dataset Characteristics

Multivariate, Time-Series

Subject Area

Computer Science

Associated Tasks

Classification, Regression, Clustering

Feature Type

Real

# Instances

90

# Features

-

Dataset Information

Has Missing Values?

No

Introductory Paper

Feature Ensemble Learning for Sensor Array Data Classification Under Low-Concentration Gas

By Leilei Zhao, Fengchun Tian, Junhui Qian, Hantao Li, Zhiyuan Wu. 2023

Published in IEEE Transactions on Instrumentation and Measurement

Variable Information

In the gsalc. csv file, each row represents a gas sample, and the first and second columns of each row represent gas labels and concentration labels, respectively. Starting from the third column, the response of the sensor array to the sample is recorded. Each sample has 9000 response points, which are concatenated from the responses of 10 sensors, in the order of TGS2603, TGS2630, TGS813, TGS822, MQ-135, MQ-137, MQ-138, 2M012, VOCS-P, 2SH12 (i.e. the first 900th sample points are the responses of sensor TGS2603, and the 901-1800th sample points are the responses of sensor TGS2630...).

Class Labels

We collected response signals from the array for six gases including ethanol, acetone, toluene, ethyl acetate, isopropanol, and n-hexane. Each gas had three concentrations of 50ppb, 100ppb, and 200ppb, and five samples were collected for each gas and concentration, for a total of 90 samples. The collection process is divided into three stages: baseline (5 minutes), injection (10 minutes), and cleaning (15 minutes), with a sampling frequency of 1Hz. This dataset provides the response of the baseline and injection stages.

Dataset Files

FileSize
gsalc.csv5 MB

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download (595.9 KB)
1 citations
903 views

Creators

Fengchun Tian

fengchuntian@cqu.edu.cn

Chongqing University

Leilei Zhao

leileizhao@cqu.edu.cn

Chongqing University

Siyuan Deng

siyuandeng@cqu.edu.cn

Chongqing University

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy