Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77430
Title: Efficient solutions for discreteness, drift, and disturbance (3D) in electronic olfaction
Authors: Zhang, L
Zhang, D 
Issue Date: 2018
Source: IEEE transactions on systems, man, and cybernetics. Systems, 2018, v. 48, no. 2, 7549043, p. 242-254
Abstract: In this paper, we aim at presenting the new challenges of electronic noses (E-noses) and proposing effective methods for handling the new challenging scientific issues to be solved, such as signal discreteness (reproducibility), systematical drift and nontarget disturbances. We first review the progress of E-noses in applications, systems, and algorithms during the past two decades. Recall a number of significant achievements and motivated by the current issues that hinder large-scale application pace of E-nose technology, we propose to address three key issues: 1) discreteness; 2) drift; and 3) disturbance (simplified as 3D issues), which are sensor induced and sensor specific. For each issue, a highly effective and efficient method is proposed. Specifically, for discreteness issue, a global affine transformation method is introduced for E-nose instruments batch calibration; for drift issue, an unsupervised feature adaptation model is proposed to achieve effective drift adaptation; additionally, for disturbance issue, we proposed a simple targets-to-targets self-representation classifier method for fast nontargets detection, without knowing any prior knowledge of thousands of nontarget disturbances in real world. For each method, a closed form solution can be analytically determined and the simplicity is guaranteed. Experiments demonstrate the effectiveness and efficiency of the proposed methods for addressing the proposed 3D issues in real applications of E-noses.
Keywords: Discreteness
Disturbance
Drift
Electronic nose (E-nose)
Sensor
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on systems, man, and cybernetics. Systems 
ISSN: 2168-2216
EISSN: 2168-2232
DOI: 10.1109/TSMC.2016.2597800
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