Search Results for author:
Found 5 papers, 0 papers with code
Controllable Image Synthesis of Industrial Data Using Stable Diffusion
Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments.
Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization
The design process of centrifugal compressors requires applying an optimization process which is computationally expensive due to complex analytical equations underlying the compressor's dynamical equations.
Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors
The application is challenging due to the large image resolutions in which defects are very small and hardly captured by the commonly used anchor sizes, and also due to the small size of the available dataset.
Deep Surrogate of Modular Multi Pump using Active Learning
Based on these considerations, we develop an active learning framework for estimating the operating point of a Modular Multi Pump used in energy field.
DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift
We propose an adversarial learning method to tackle a Domain Adaptation (DA) time series regression task (DANNTe).
