Multimodal learning provides a path to fully utilize all types of information related to the
modeling target to provide the model with a global vision. Zero‐shot learning (ZSL) is a …

Purpose This paper aims to explore recent advances in drift compensation algorithms for
Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline …

Traffic Sign Recognition (TSR) is an essential component of Intelligent Transportation
Systems (ITS) and intelligent vehicles. TSR systems based on deep learning have grown in …

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 …

In the field of electronic nose systems, sensor drift poses a significant challenge, affecting
the reliability and accuracy of gas detection. Current solutions often require labeled data and …

In practical applications, electronic noses (EN) must tackle not only sensor drift but also
resist interference from unknown gases. In prolonged open environments, electronic noses …

The emergence of Industry 4.0 has transformed modern-day factories into high-tech
industrial sites through rapid automation and increased access to real-time data. Deep …

To mitigate the impact of single sensor drift on gas turbine thrust estimation and enhance
fault tolerance, this study introduces a three-stage multi-target domain adaptation (TSMTDA) …

Gas sensor drift has consistently been a bottleneck in the progression of electronic noses (E-
noses) systems. In contrast to target-domain-adaptive anti-drift classification algorithms …

Molecular imprinting-based ratiometric fluorescence (MI-RFL) nanosensors receive
increasing concerns in various fields such as food security, owing to high selectivity and …