Guided Explicit Mechanism Property Generation Using WGAN and Integrated Regression Model for Insufficient Gasoline NIR Data Augmentation

Near-infrared analysis has demonstrated superiority in constructing calibration models for evaluating gasoline properties during blending. However, the intricacies of sampling and the time-intensive analysis process frequently pose challenges in obtaining sufficient labeled samples, thereby hindering the establishment of calibration model with the satisfactory performance. Generative adversarial networks (GANs) bridge this gap by generating virtual samples. Nevertheless, existing methods neglect the global distribution of samples and lack explicit mechanisms for generating properties. To expand labeled sample sets sufficiently for constructing precise and reliable calibration models, a regression Wasserstein deep convolutional generative adversarial network with divergence (RWDCGAN-div) is proposed. Wasserstein divergence is designed in the RWDCGAN-div framework to broaden the global spatial distribution of labeled samples and consistent convolutional neural networks are utilized to simplify model construction. Regression models are integrated to facilitate the discriminator learn the relationship between spectra and properties, thereby guiding the generator in producing spectra and corresponding properties with explicit mechanisms. The effectiveness of the proposed method is verified through prediction experiments based on gasoline blending process.