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Akademický článek

Temperature and Fault Prediction of Transformer in Distribution Station Based on Digital Twin Model

Autor: Zhang Xi, Li Wei, Chen Zhuang, Huang Fazheng, Hu Yaqiong, Xu Bo, Hui Shuang, Li Heyuan

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

This article presents an innovative method for predicting transformer temperature and faults in distribution substations using a digital twin model combined with deep learning techniques. By constructing a digital model of the transformer, real-time

Externí odkaz: https://doaj.org/article/79b77c7cd3ee4082b033ae7eb65966e8

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SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane Representation

Autor: Li, Heyuan, Chen, Ce, Shi, Tianhao, Qiu, Yuda, An, Sizhe, Chen, Guanying, Han, Xiaoguang

While recent advances in 3D-aware Generative Adversarial Networks (GANs) have aided the development of near-frontal view human face synthesis, the challenge of comprehensively synthesizing a full 3D head viewable from all angles still persists. Altho

Externí odkaz: http://arxiv.org/abs/2404.05680

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DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment

Autor: Li, Heyuan, Wang, Bo, Cheng, Yu, Kankanhalli, Mohan, Tan, Robby T.

Sensitivity to severe occlusion and large view angles limits the usage scenarios of the existing monocular 3D dense face alignment methods. The state-of-the-art 3DMM-based method, directly regresses the model's coefficients, underutilizing the low-le

Externí odkaz: http://arxiv.org/abs/2305.11522

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