Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring
Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring
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DOI：
10.1016/j.snb.2015.03.028
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发表时间：
2015-08-01
影响因子：
8.4
通讯作者：
Marco, Santiago
中科院分区：
文献类型：
--
作者：
Fonollosa, Jordi;Sheik, Sadique;Marco, Santiago
金属氧化物 (MOX) 气体传感器阵列是执行化学检测基本任务的主要技术选择。然而，它们的使用主要限于相对受控的仪器配置，其中传感器阵列放置在封闭的测量室内。通常，实验方案是预先定义的，它包括三个阶段：阵列首先暴露于气体参考，然后暴露于气体样本，最后再次暴露于参考以恢复初始状态。这种采样过程需要在整个实验协议期间采集信号，并且通常会延迟输出预测，直到预定义的测量持续时间完成。由于化学传感器的时间响应较慢，测量通常需要几分钟才能完成。在本文中，我们建议使用储层计算（RC）算法来克服化学传感器阵列的缓慢时间动态，从而能够连续识别和量化感兴趣的化学物质并减少测量延迟。我们生成了两个数据集来测试 RC 算法对快速变化的气体浓度实时提供准确、连续预测的能力。两个数据集（一个由合成数据生成，另一个从实际气体传感器获取）提供了暴露于二元气体混合物中的 MOX 传感器的时间序列，其中浓度水平随时间随机变化。我们的结果表明，我们的方法提高了传感系统的时间响应并实时提供准确的预测，使该系统特别适合在线监测应用。最后，收集的数据集和开发的代码公开提供给研究界以供进一步研究。 (C) 2015 Elsevier B.V. 保留所有权利。
Metal oxide (MOX) gas sensors arrays are a predominant technological choice to perform fundamental tasks of chemical detection. Yet, their use has been mainly limited to relatively controlled instrument configurations where the sensor array is placed within a closed measurement chamber. Usually, the experimental protocol is defined beforehand and it includes three stages: the array is first exposed to a gas reference, then to the gas sample, and finally to the reference again to recover the initial state. Such sampling procedure requires signal acquisition during the complete experimental protocol and usually delays the output prediction until the predefined measurement duration is complete. Due to the slow time response of chemical sensors, the completion of the measurement typically requires minutes. In this paper we propose the use of reservoir computing (RC) algorithms to overcome the slow temporal dynamics of chemical sensor arrays, allowing identification and quantification of chemicals of interest continuously and reducing measurement delays. We generated two datasets to test the ability of RC algorithms to provide accurate and continuous prediction to fast varying gas concentrations in real time. Both datasets - one generated with synthetic data and the other acquired from actual gas sensors provide time series of MOX sensors exposed to binary gas mixtures where concentration levels change randomly over time. Our results show that our approach improves the time response of the sensory system and provides accurate predictions in real time, making the system specifically suitable for online monitoring applications. Finally, the collected dataset and developed code are made publicly available to the research community for further studies. (C) 2015 Elsevier B.V. All rights reserved.