Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches
Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches
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DOI：
10.1016/j.snb.2017.07.155
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发表时间：
2018-02-01
影响因子：
8.4
通讯作者：
Di Francia, G.
中科院分区：
文献类型：
--
作者：
De Vito, S.;Esposito, E.;Di Francia, G.
化学多传感器设备需要校准算法来估计气体浓度。由于需要在不受控制的环境中以连续监测模式运行，它们可能被用作指示性空气质量测量设备提出了新的挑战。几个问题，包括缓慢的动态，继续影响他们在现实世界中的表现。与此同时，估计设备上的污染物浓度的需求变得非常必要，特别是对于可穿戴设备和物联网部署。在这个框架中，已经提出了几种校准方法，并在各种专有设备和数据集上进行了测试；然而，研究人员还没有得到彻底的比较。根据最近的文献，这项工作试图对最有希望的校准算法进行基准测试，重点是机器学习方法。我们使用三个共享连续监测操作方法的不同数据集，测试了这些技术的绝对和动态性能、泛化能力和计算/存储需求。我们的研究结果可以指导研究人员和工程师选择最优策略。它们表明，非线性多变量技术产生的结果是可重现的，表现优于线性方法。具体地说，支持向量回归方法在所考虑的所有场景中都表现出了良好的性能。我们强调了浅层神经网络在性能和计算/存储需求之间权衡的增强适应性。我们在更广泛的基础上确认了动态方法相对于静态方法的优势，静态方法仅依赖于传感器阵列的瞬时响应。事实证明，每当需要迅速和准确的反应时，后者都是最佳选择。(C)2017爱思唯尔B.V.保留所有权利。
Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, especially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming linear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed. (C) 2017 Elsevier B.V. All rights reserved.