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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Multi-View Diffusion Process for Spectral Clustering and Image Retrieval
Published in IEEE Transactions on Image Processing, 2023
This paper is about image retrieval and clustering using a novel multi-view diffusion process.
Recommended citation: Li, Qilin, Senjian An, Ling Li, Wanquan Liu, and Yanda Shao. "Multi-View Diffusion Process for Spectral Clustering and Image Retrieval." IEEE Transactions on Image Processing (2023).
Paper | Code
Machine learning prediction of structural dynamic responses using graph neural networks
Published in Computers & Structures, 2023
This paper is about data-driven spatiotemporal simulation of structural dynamic responses.
Recommended citation: Li, Qilin, Zitong Wang, Ling Li, Hong Hao, Wensu Chen, and Yanda Shao. "Machine learning prediction of structural dynamic responses using graph neural networks." Computers & Structures 289 (2023): 107188.
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A comparative study on the most effective machine learning model for blast loading prediction: From GBDT to Transformer
Published in Engineering Structures, 2023
This paper is a comparative study of commonly used machine learning approach for blast loading prediction.
Recommended citation: Li, Qilin, Yang Wang, Yanda Shao, Ling Li, and Hong Hao. "A comparative study on the most effective machine learning model for blast loading prediction: From GBDT to Transformer." Engineering Structures 276 (2023): 115310.
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A novel transformer-based semantic segmentation framework for structural condition assessment
Published in Structural Health Monitoring, 2024
This paper is about structural component and structural damage identification via vision-based semantic segmentation with SOTA Transformer networks.
Recommended citation: Wang, Ruhua, Yanda Shao, Qilin Li, Ling Li, Jun Li, and Hong Hao. "A novel transformer-based semantic segmentation framework for structural condition assessment." Structural Health Monitoring 23, no. 2 (2024): 1170-1183.
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Machine learning prediction of BLEVE loading with graph neural networks
Published in Reliability Engineering & System Safety, 2024
This paper is about data-driven simulation of BLEVE blast wave propagation
Recommended citation: Li, Qilin, et al. "Machine learning prediction of BLEVE loading with graph neural networks." Reliability Engineering & System Safety 241 (2024):109639.
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Advancing blast fragmentation simulation of RC slabs: A graph neural network approach
Published in Engineering Structures, 2024
This paper is about data-driven simulation of close-in blast fragmentation of concrete slabs using GNN
Recommended citation: Li, Qilin, et al. "Advancing blast fragmentation simulation of RC slabs: A graph neural network approach." Engineering Structures 308 (2024):118009.
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talks
Advancing Blast Fragmentation Simulation of RC Slabs: A Graph Neural Network Approach
Published:
The flowchart of FGN
Multi-View Diffusion Proces for Spectral Clustering and Image Retrieval
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The flowchart of MVD
teaching
Foundamental Concepts of Cryptography
Undergraduate course, Curtin University, Department of Computing, 2020
An introduction to basic concept of cryptography with an emphasis on coding theory, classical cryptosystems and public key cryptography. Principles of information theoretic security. Computational hardness and number theory (Euclid’s algorithm, Euler and Fermat’s theorems). Public and private-key encryption, message authentication and digital signatures. More information here.
Foundation of Computer Science
Undergraduate course, Curtin University, Department of Computing, 2020
This unit introduces the mathematical theory that underlies the computing profession. It introduces proof and logic concepts central to computer science and programming methodology, including an introduction to set theory and mathematical relations, graph theory. Computational and mathematical recursion is also addressed, along with the paired concept of induction proofs. Finally, the analysis of software using discrete statistics is also addressed, including univariate statistics and confidence intervals. More infomation here.
Explainable Approaches to Machine Learning
Postgraduate course, Curtin University, Department of Computing, 2022
The unit will focus on special machine learning approaches, named explainable artificial intelligence or XAI, that generate solutions that can be trusted and are easy to understand and particularly well suited to fields such as medicine, finance, security, legal, military, and where human-machine interaction is needed. It will provide students with XAI fundamentals such as interpretability, explainability, and visualisation as well as legal and ethical issues surrounding XAI. The unit will also cover different classes of techniques from model-agnostic, example-based, to neural network interpretation. Finally, common XAI applications and current trends in XAI will also be discussed. More infomation here.
Machine Learning
Undergraduate course, Curtin University, Department of Computing, 2024
This unit introduces the foundational concepts, algorithms, and applications of Machine Learning (ML). Students will gain hands-on experience with diverse data types including tabular, image, text, and time series, and will learn to develop ML models such as linear models, SVM, decision trees, and neural networks. Covering a broad spectrum of topics within supervised and unsupervised learning, self-supervised learning, and deep learning, the unit also focuses on practical applications in computer vision and natural language processing, equipping students with the skills to implement ML solutions and solve real-world problems effectively. More infomation here.