Recent Research Projects and Publications

Machine Learning and Data Assimilation for improving Air Quality Modelling

M. Xu, J. Jin, G. Wang, A Segers, T. Deng, H.X. Lin (2021), Machine learning based bias correction for numerical chemical transport models, Atmospheric Environment, 118022

J. Jin, H.X. Lin, A. Segers, Y. Xie, A.W. Heemink (2019), Machine learning for observation bias correction with application to dust storm data assimilation, Atmospheric Chemistry and Physics, Vol.19, pp. 10009—10026.

H.X. Lin, J. Jin, H.J. van den Herik (2019), Air Quality Forecast through Integrated Data Assimilation and Machine Learning, in Proc. 11^th International Conference on Agents and Artificial Intelligence (ICAART 2019).

T. Deng, A. Cheng, W. Han, H.X. Lin (2019), Visibility Forecast for airport operations by LSTM Neural Network, in Proc. 11^th International Conference on Agents and Artificial Intelligence (ICAART 2019).

J. Jin, H.X. Lin, A.W. Heemink, A. Segers (2018), Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar, Atmospheric Environment, Vol.187, pp. 358-373.

G. Fu, F. Prata, H.X. Lin, A.W. Heemink, S. Lu, A.J. Segers (2017) Data assimilation for volcanic ash plumes using a Satellite Observational Operator: a case study on the 2010 Eyjafjallajokull volcanic eruption, Atmospheric Chemistry and Physics, Vol17(2), pp. 1187—1205.

S. Lu, H.X. Lin, A. Heemink, A Segers, G. Fu (2016) Estimation of volcanic ash emissions through assimilating satellite data and ground-based observations, Journal of Geophysical Research: Atmospheres, Vol. 121 (18), pp. 10971–10994.

G. Fu, A.W. Heemink, S. Lu, A.J. Segers, K. Weber, H.X. Lin (2016) Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in-situ measurements, Atmospheric Chemistry and Physics.

Machine Learning Methods and other Applications

C. Xiao, O. Leeuwenburgh, H.X. Lin, A.W. Heemink (2021), Conditioning of Deep-Learning Surrogate Models to Image Data with Application to Reservoir Characterization, Knowledge Based Systems.

C. Xiao, H.X. Lin, O. Leeuwenburgh, A.W. Heemink (2022), Surrogate-assisted inversion for large-scale history matching: comparative study between projection-based reduced-order modelling and deep neural network, Journal of Petroleum Science and Engineering.

J. Zhou, Y. Bai, Y. Guo, H.X. Lin (2021), Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification, Journal of Operations Research Society of China.

R. Liang, Y. Bai, H.X. Lin (2019), A splitting method for the locality regularized semi-supervised subspace clustering, Optimization.

R. Liang, Y. Bai, H.X. Lin (2019), An inexact splitting method for the subspace segmentation from incomplete and noisy observations, Journal of Global Optimization, 73 (2), pp 411–429.

C. Xiao, A.W. Heemink, H.X. Lin, O. Leeuwenburgh (2020), Deep-Learning Inversion to Efficiently Handle Big-Data Assimilation: Application to Seismic History Matching, Proceedings ECMOR XVII, Volume 2020, pp. 1-16.

Y. Gu, E. Postma, H. X. Lin, H. J. Van Den Herik (2016), Speech Emotion Recognition Using Voiced Segment Selection Algorithm, Proceedings ECAI 2016.

A. van Rossum, H.X. Lin, J. Dubbeldam, H.J. van den Herik (2016), Nonparametric Bayesian Line Detection - Towards Proper Priors for Robotic Computer Vision, in Proc. 5^th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016), pp.119-127. (Best Paper Award)

J. Zhou, T.J. Luo, H.X. Lin (2010), A novel recommendation system with collective intelligence, Proceedings of the 2010 IEEE 2nd Symposium on Web Society.

Variable Renewable Energy (VREs) and Future Mobility Systems (EVs)

S. Wang, G.H. Careira, H.X. Lin (2019), Exploring the Performance of Different On-Demand Transit Services Provided by a Fleet of Shared Automated Vehicles: An Agent-Based Model, Journal of Advanced Transportation

L.C. Ye, H.X. Lin, A. Tukker (2018), Future scenarios of variable renewable energies and flexibility requirements for thermal power plants in China, Energy.

K. Xi, J.L.A. Dubbeldam, H.X. Lin, J.H. van Schuppen (2018), Power-Imbalance Allocation Control of Power Systems-Secondary Frequency Control, Automatica 92, pp. 72-85.

L.C. Ye, J.F.D. Rodrigues, H.X. Lin (2017), Analysis of feed-in tariff policies for solar photovoltaic in China 2011–2016, Applied Energy, 203, pp.296-505.

Ongoing PhD Research Projects

- M. Pang, (Collaboration: TNO, Nanjing University of Information Science and Technology, TUD)

- K. Li, (Collaboration: Leiden University, TUD)

- Y. Huan, and L. Ye, Big Data, Integration of EV battery as Virtual Storage for VREs (Collaboration: Leiden University, BIT, TUD)

- T. Deng, and X. Li, Air Quality Forecast by Integrating Machine Learning with Data Assimilation (Collaboration: TNO, TUD, Shandong University)

- Z. Chen, (Collaboration: Leiden University, TUD)

- Z. Yuan, Improving Retrieval of Satellite Data using Data Assimilation and Deep Learning Methods (Collaboration: Leiden University, SRON)

PhD dissertation in AI, Data Assimilation: Air Quality, Renewable Energy and Mobility (2017-2024)

J. Verkaik, Facilitating and enabling large-scale, hyper-resolution, groundwater modeling with distributed-memory parallel computing, Promotores: M.F.P. Bierkens, H.X. Lin, co-pomotor: G.H.P. Oude-Essink, 2024, Utrecht University

S. Wang, Modeling Urban Automated Mobility on-Demand Systems: an Agent-based Approach, Promotor: H.X. Lin, co-promotor: G.H. Correia, 2023, TU Delft

X. Zhong, Sheltering 10 billion people in a warming and resource-scarce world: challenges and opportunities, Promotores: H.X. Lin, A. Tukker, 2023, Leiden University

X. Wang, Improving Global Tide and Storm Surge Forecast, Promotores: H.X. Lin, M. Verlaan, 2022, TU Delft

A. van Rossum, Nonparametric Bayesian Methods in Robotics, 2021, Promotores: J. van den Herik, H.X. Lin, 2021, Leiden University

X. Cong, DATA-DRIVEN Surrogate-assisted Reservoir History Matching, January 2021, Promotores: A.W. Heemink, H.X. Lin, TU Delft

J. Jin, Dust Storm Emission Inversion using Data Assimilation, December 2019, Promotores: H.X. Lin, A.W. Heemink, TU Delft

Y. Gu, Automatic Emotion Recognition from Mandarin Speech, November 2018, Promotores: E. Postma, J. van den Herik, H.X. Lin, Tilburg University

K. Xi, Power System Stability and Frequency Control for Transient Performance Improvement, June 2018, Promotores: H.X. Lin, J.H. van Schuppen, co-promotor: J. Dubbedlam, TU Delft

S. Lu, Variational Data Assimilation of Satellite Observations to Estimate Volcanic Ash Emissions, March 2017, Promotores: H.X. Lin, A.W. Heemink, TU Delft

G. Fu, Improving Volcanic Ash Forecasts with Ensemble-based Data Assimilation, January 2017, Promotores: H.X. Lin, A.W. Heemink, TU Delft

MSc thesis in AI (2018-2021)

Guus van Hemert, Using Artificial Intelligence for Aerosol Data Assimilation, 2022, TU Delft

Tycho Jongenelen, Random Forest Modelling of Surface Ammonia Concentrations in the Netherlands, 2022, Leiden University

P.O. Sturm, Advecting Superspecies - Reduced order modeling of organic aerosols in LOTOSEUROS using machine learning, 2021, TU Delft

B.X. van Leeuwen, Deepfake Detection Using Convolutional Neural Networks Working - Towards Understanding the Effects of Design Choices, 2020, TU Delft

R. Hegeman, Predicting the air quality by combining model simulation with machine learning, 2020, TU Delft

T. Ament, GPU Implementation of Grid Search based Feature Selection - Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets, 2020, TU Delft

J. Huang, Machine Learning Based Error Modelling for surrogate model in Oil Reservoir Problem, 2019, TU Delft

S. Guan, PM2.5 concentration predication and early warning system of extreme condition based on the long short term memory recurrent neural network, 2018, TU Delft

Y. Xie, Deep Learning Architectures for PM2.5 and Visibility Predictions, 2018, TU Delft