(1) Interpretable machine learning for tree ensembles
Research details | This work was part of the Advanced Training Program for Smart Manufacturing Intelligence which aimed to cultivate experts for smart factory. In this project I developed a rule extraction algorithm to understand the decision mechanism of complex tree ensembles that can be used to explain predictive models in different domains. |
Research achievements |
|
Tasks | My involvement in this project was into conceptualization, methodology, software implementation, formal analysis, investigation, writing and visualization. |
Publications | RuleCOSI: Combination and Simplification of Production Rules from Boosted Decision Trees for Imbalanced Classification, Expert Systems with Applications (2019) RuleCOSI+: Rule extraction for interpreting classification tree ensembles, Information Fusion (2023) |
(2) Fault detection in the injection molding process
Research details | This work was developed in collaboration with the Singapore Institute of Manufacturing Technology (SIMTech), A*STAR, Singapore. It consisted in generating explainable visualizations of predictive models for fault detection in the injection molding manufacturing process. A rule-based explanations framework was developed that combines decision rules, feature importance and dependence visualization plots, to aid quality engineers in understanding the main manufacturing process conditions affecting the quality of the products |
Research achievements |
|
Tasks | My involvement in this project was into conceptualization, methodology, software implementation, formal analysis, investigation, writing and visualization. |
Publications | Rule-based Explanations Based on Ensemble Machine Learning for Detecting Sink Mark Defects in the Injection Moulding Process, Journal of Manufacturing Systems (2021) |
(3) Visualization method for quality prediction in the die-casting process
Research details | This work was also part of the Advanced Training Program for Smart Manufacturing Intelligence, and it also consisted in the generation of explanations of predictive models for fault detection in the die-casting manufacturing process. The framework is based in a mathematical formal language called Formal Concept Analysis (FCA). |
Research achievements |
|
Tasks | My involvement in this project was into conceptualization, methodology, software implementation, formal analysis, investigation, writing and visualization. |
Publications | Rule-based visualization of faulty process conditions in the die-casting manufacturing, Journal of Intelligent Manufacturing Systems (2022) |
(4) State-of-Health prediction for lithium-ion batteries
Research details | This work is part of the Next-Generation Engineers for Smart Energy program, and it consisted in the application of Convolutional Autoencoders (CAE) and deep neural networks (DNN) to SOH prediction for lithium-ion batteries using electrochemical impedance spectroscopy. This work was developed in collaboration with researchers of the Chemical Engineering department or Kyung Hee University. |
Research achievements |
|
Tasks | My involvement in this project was into conceptualization, methodology, software implementation, formal analysis, investigation, writing and visualization. |
Publications | Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy“, Journal of Energy Storage (2023) |
(5) Photovoltaic power generation forecasting
Research details | This work was part of the Next-Generation Engineers for Smart Energy program, and it consisted in the application of Transformer networks for multistep ahead photovoltaic power generation forecasting for Ulsan and Dangjin solar power stations. |
Research achievements |
|
Tasks | My involvement in this project was into validation, conceptualization, and writing. |
Publications | The project produced one research paper that is on the final stage of writing, and it will be submitted to Applied Energy journal. |