Sensor drift has been recognized as the root cause of decreased effectiveness in the gas
sensor community. To date, most drift compensation strategies have focused on …

This paper addresses an important issue known as sensor drift, which exhibits a nonlinear
dynamic property in electronic nose (E-nose), from the viewpoint of machine learning …

The drift caused by gas sensors has always been a bottleneck in the development of
electronic nose (E-nose) systems. Traditional drift compensation methods directly correct the …

Gas sensor array (GSA) data usually has high-dimensional features and a small sample
size. When a classifier is directly used for GSA data classification, it is prone to overfitting …

Machine learning approaches have been widely used to tackle the problem of sensor array
drift in E‐Nose systems. However, labeled data are rare in practice, which makes supervised …

Sensor drift is an urgent issue in the machine olfaction community. To date, most studies
have focused on gas classification tasks based on an offline method, while neglecting …

Subspace learning is a popular machine learning method that has been frequently applied
for gas sensor calibration; however, there are the following limitations in the latent subspace …

Sensor drift is a common issue in E-Nose systems and various drift compensation methods
have received fruitful results in recent years. Although the accuracy for recognizing diverse …

Sensor drift/shift is a challenging and high-profile issue in the field of sensors and
measurements. Because of the time variability and unpredictable properties of drift/shift …

Artificial Olfactory Systems (AOS) mimic the Biological Olfaction (BO) using sensors and
artificial intelligence algorithms. The performance of an AOS is based on the sensitivity and …