Hejia QIU (Henry)

Hejia QIU (Henry)

Computer Scientist, Programmer, Game Player

Publications

Benchmarking Dimension-Inverted Transformers for High-Precision Forward Kinematics of Industrial Manipulators

Published in The 9th IEEE International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026), 2026

Tansformer-based solutions for high-precision forward kinematics of industrial manipulators

Recommended citation: You, X., Liu, Y., Xu, P., & Qiu, H. (Accepted/In press). Benchmarking Dimension-Inverted Transformers for High-Precision Forward Kinematics of Industrial Manipulators. In The 9th IEEE International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) IEEE. https://scholar.xjtlu.edu.cn/en/publications/benchmarking-dimension-inverted-transformers-for-high-precision-f/

Submersible Pump Impeller Product Anomaly Detection Using an Advanced Deep Learning Model ─ EfficientNetV2

Published in The 9th IEEE International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026), 2026

EfficientNetV2 for submersible pump impeller product anomaly detection.

Recommended citation: Liu, Y., Xu, P., Gan, H. S., & Qiu, H. (Accepted/In press). Submersible Pump Impeller Product Anomaly Detection Using an Advanced Deep Learning Model ─ EfficientNetV2. In The 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) IEEE. https://scholar.xjtlu.edu.cn/en/publications/submersible-pump-impeller-product-anomaly-detection-using-an-adva/

Q2VA: A Bayesian Adaptive Method for Visual Acuity Assessment with Tumbling E Stimuli

Published in The 39th IEEE International Symposium on Computer-Based Medical Systems (CBMS-2026), 2026

Q2VA has the potential to provide a robust, efficient, and population-inclusive solution for high-precision visual acuity monitoring in eye clinics.

Recommended citation: Tao, H., Liang, X., Wang, J., Xu, B., Qiu, H., & Xu, P. (Accepted/In press). Q2VA: A Bayesian Adaptive Method for Visual Acuity Assessment with Tumbling E Stimuli. In The 39th IEEE International Symposium on Computer-Based Medical Systems (CBMS-2026) https://scholar.xjtlu.edu.cn/en/publications/q2va-a-bayesian-adaptive-method-for-visual-acuity-assessment-with/

Machine Learning based Breast Cancer Prognosis Analysis on Multi-Source Big Data

Published in 2025 8th International Conference on Big Data and Artificial Intelligence (BDAI), 2025

In our study, we apply various machine learning methods on multi-source breast cancer big data for feature analysis and prognosis evaluation.

Recommended citation: H. Qiu and Y. Weng, "Machine Learning based Breast Cancer Prognosis Analysis on Multi-Source Big Data," 2025 8th International Conference on Big Data and Artificial Intelligence (BDAI), Taicang, China, 2025, pp. 278-283, doi: 10.1109/BDAI66031.2025.11325245. https://ieeexplore.ieee.org/abstract/document/11325245/

Fractional Tensor Recurrent Unit (fTRU): A Stable Forecasting Model With Long Memory

Published in IEEE Transactions on Neural Networks and Learning Systems, 2023

Our new model, named fractional tensor recurrent unit (fTRU), is expected to seek the saddle point between long memory property and model stability during the training. We experimentally show that the proposed model achieves competitive performance with a long memory and stable manners in several forecasting tasks compared to various advanced RNNs.

Recommended citation: H. Qiu, C. Li, Y. Weng, Z. Sun and Q. Zhao, "Fractional Tensor Recurrent Unit (fTRU): A Stable Forecasting Model With Long Memory," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3338696. https://ieeexplore.ieee.org/document/10361837

On the Memory Mechanism of Tensor-Power Recurrent Model

Published in International Conference on Artificial Intelligence and Statistics, 2021

Focus on the long-term memory and stability of tensor recurrent model with developing a degree-differentiable model benefit from long-term effect in a stable manner, cooperate with RIKEN AIP Tensor Learning Team.

Recommended citation: Qiu, H., Li, C., Weng, Y., Sun, Z., He, X. and Zhao, Q., 2021, March. On the Memory Mechanism of Tensor-Power Recurrent Models. In International Conference on Artificial Intelligence and Statistics (pp. 3682-3690). PMLR. http://proceedings.mlr.press/v130/qiu21a.html