.png){kind=icon}
.png){kind=icon}
Return
Efficient Multifeature Extraction and Embedded Array Optimization for Drift-Calibrated Multisensor Odor Detection System
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2024.3390699.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article focuses on achieving a reliable identification of herbal medicine while minimizing the impact of sensor drift. Therefore, 21 metal oxide sensors are implemented as sensitive units during the initial construction of the sensor array. Based on this, to fully exploit odor data and reduce computational burdens, we propose a weighted feature combination selection strategy that evaluates a feature set based on both label relevance and feature stability. To further enhance the long-term stability of the system and reduce the number of sensors, we introduce a combined scheme involving drift calibration and array optimization design. Specifically, we optimize a subspace projection matrix that considers minimizing the maximum mean discrepancy (MMD), preserving the Laplace manifold for the source domain, and maximizing the correlation coefficient between the source and target domains to compensate for sensor drift. Subsequently, we develop sensor array optimization using an embedded composition of feature relevancy (CFR) framework to eliminate redundant information in the target domain. Experimental results demonstrate that, compared to existing approaches, the proposed calibration framework achieves the best system performance with the minimum number of sensors.
Keywords:
Detection system
feature combination selection
sensor array calibration
sensor array optimization
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W