2 citations · 4 across the 7 of their papers we have counts for
7 papers
A Conditional Diffusion Model for Electrical Impedance Tomography Image Reconstruction
Shuaikai Shi, Ruiyuan Kang, Panos Liatsis
Electrical impedance tomography (EIT) is a non-invasive imaging technique, capable of reconstructing images of the electrical conductivity of tissues and materials. It is popular i…
Physics-Driven AI Correction in Laser Absorption Sensing Quantification
Ruiyuan Kang, Panos Liatsis, Meixia Geng +1
Laser absorption spectroscopy (LAS) quantification is a popular tool used in measuring temperature and concentration of gases. It has low error tolerance, whereas current ML-based…
Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
Ruiyuan Kang, Tingting Mu, Panos Liatsis +1
When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero…
EEE, Remediating the failure of machine learning models via a network-based optimization patch
Ruiyuan Kang, Dimitrios Kyritsis, Panos Liatsis
A network-based optimization approach, EEE, is proposed for the purpose of providing validation-viable state estimations to remediate the failure of pretrained models. To improve o…
Spatially-resolved Thermometry from Line-of-Sight Emission Spectroscopy via Machine Learning
Ruiyuan Kang, Dimitrios C. Kyritsis, Panos Liatsis
A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in non…
Flame-state monitoring based on very low number of visible or infrared images via few-shot learning
Ruiyuan Kang, Panos Liatsis, Dimitrios C. Kyritsis
The current success of machine learning on image-based combustion monitoring is based on massive data, which is costly even impossible for industrial applications. To address this…
Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling
Ruiyuan Kang, Dimitrios C. Kyritsis, Panos Liatsis
Physics-based inverse modeling techniques are typically restricted to particular research fields, whereas popular machine-learning-based ones are too data-dependent to guarantee th…