Unveiling Hidden Insights in Gas Chromatography Data Analysis with Generative Adversarial Networks
Namkyung Yoon,
Wooyong Jung,
Hwangnam Kim
Abstract:The gas chromatography analysis method for chemical substances enables accurate analysis to precisely distinguish the components of a mixture. This paper presents a technique for augmenting time-series data of chemicals measured by gas chromatography instruments with artificial intelligence techniques such as generative adversarial networks (GAN). We propose a novel GAN algorithm called GCGAN for gas chromatography data, a unified model of autoencoder (AE) and GAN for effective time-series data learning with a… Show more
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