Efficient Solutions for Discreteness, Drift, and Disturbance (3D) in Electronic Olfaction
本文针对电子鼻技术中的信号离散性、系统漂移和非目标干扰三大难题，分别提出了全局仿射变换校准、无监督特征适应和简单目标自表示分类器方法，实验验证了其有效性。
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.