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168,657 papers · 148 categories

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2 results for “Ironmaking”
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industrycs.LG

MTS-CycleGAN adapts multivariate time series data for ironmaking industry.

problem Creating a domain invariant dataset from multivariate time series data of different blast furnaces.
method Adversarial-based deep mapping learning network (CycleGAN) with LSTM-based AutoEncoder and discriminator.
result MTS-CycleGAN successfully translates multivariate time series data between different blast furnaces.
CycleGANMultivariate Time SeriesDomain AdaptationIronmaking IndustryBlast Furnace
2020-07-15abs ↗pdf ↗
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industrycs.LG

A new method improves the interpretability of data-driven models in ironmaking processes.

problem Lack of transparency in machine learning models used in industrial processes.
method Combines Variational Autoencoder (VAE) with Local Interpretable Model-agnostic Explanations (LIME) for better model interpretability.
result Improved local fidelity of local interpretable linear models compared to LIME.
VAELIMEInterpretabilityIronmakingBlast Furnace
2020-07-15abs ↗pdf ↗