Classification of 1D signals using deep neural networks

机译：使用深神经网络分类1D信号

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The absorbance spectrum technique is as old as the first alchemists. They sought to identify and understand their elixirs by looking at the color and opacity of solutions as different reagents were mixed, heated, and stirred. Today it remains the most widely used spectroscopic technique for studying liquids and gases due to its simplicity, accuracy, and ease of use. An absorbance spectrum can be used to identify substances or measure the concentration of a molecule in solution. In this work, PLSR, GBR, RF and CNN models are trained on absorbance spectrum data of different liquid solvents and concentration of a specific molecule is predicted. A large data set is collected to train and test the models. The proposed deep convolutional neural networks gave the best results.

机译：吸光度谱技术与第一个炼金术家一样古老。他们试图通过看着解决方案的颜色和不透明度来识别和了解他们的酏剂，因为不同的试剂混合，加热和搅拌。如今，由于其简单，准确性和易用性，仍然是研究液体和气体最广泛使用的光谱技术。吸光度谱可用于鉴定物质或测量溶液中分子的浓度。在该工作中，PLSR，GBR，RF和CNN模型培训对不同液体溶剂的吸光光谱数据，并且预测特定分子的浓度。收集大数据集以培训和测试模型。所提出的深度卷积神经网络得到了最佳结果。

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来源
《Signal Processing and Communications Applications Conference》|2018年|672p|共4页
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作者
Emre Ardi?; Yakup Gen?;
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中图分类 TN911.7-53;
关键词
Radio frequency; Dogs; Liquids; Data models; Convolutional neural networks; Support vector machines;

机译：射频;狗;液体;数据模型;卷积神经网络;支持矢量机器;

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Classification of 1D signals using deep neural networks

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