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首页> 外文会议>International Conference on Acoustics, Speech and Signal Processing;ICASSP 2013;IEEE International Conference on Acoustics, Speech and Signal Processing >ACTIVE ANALYSIS OF CHEMICAL MIXTURES WITH MULTI-MODAL SPARSE NON-NEGATIVE LEAST SQUARES
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ACTIVE ANALYSIS OF CHEMICAL MIXTURES WITH MULTI-MODAL SPARSE NON-NEGATIVE LEAST SQUARES

机译：ACTIVE ANALYSIS OF CHEMICAL MIXTURES WITH MULTI-MODAL SPARSE NON-NEGATIVE LEAST SQUARES

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New sensor technologies such as Fabry-Pérot interferometers (FPI) offer low-cost and portable alternatives to traditional infrared absorption spectroscopy for chemical analysis. However, with FPIs the absorption spectrum has to be measured one wavelength at a time. In this work, we propose an active-sensing framework to select a subset of wavelengths that best separates the specific components of a chemical mixture. Compared to passive feature-selection approaches, in which the subset is selected offline, active sensing selects the next feature on-the-fly based on previous measurements so as to reduce uncertainty. We propose a novel multi-modal non-negative least squares method (MM-NNLS) to solve the underlying linear system, which has multiple nearoptimal solutions. We tested the framework on mixture problems of up to 10 components from a library of 100 chemicals. MMNNLS can solve complex mixtures using only a small number of measurements, and outperforms passive approaches in terms of sensing efficiency and stability.
机译：法布里-珀罗干涉仪（FPI）等新型传感器技术为化学分析提供了传统红外吸收光谱法的低成本便携式替代品。然而，使用FPIs，必须一次测量一个波长的吸收光谱。在这项工作中，我们提出了一个主动传感框架，以选择最能分离化学混合物特定成分的波长子集。与离线选择子集的被动特征选择方法相比，主动传感基于之前的测量动态选择下一个特征，以减少不确定性。我们提出了一种新的多模态非负最小二乘法（MM-NNLS）来求解具有多个近似最优解的线性系统。我们在100种化学品的库中测试了多达10种成分的混合物问题框架。MMNNLS只需少量测量就能解决复杂的混合问题，在传感效率和稳定性方面优于被动方法。

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