no code implementations • 23 Feb 2024 • Xuyang Li, Hamed Bolandi, Mahdi Masmoudi, Talal Salem, Nizar Lajnef, Vishnu Naresh Boddeti
Structural health monitoring (SHM) is vital for ensuring the safety and longevity of structures like buildings and bridges.
no code implementations • 13 Nov 2023 • Danfeng Hong, Bing Zhang, Xuyang Li, YuXuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, Hao Li, Pedram Ghamisi, Xiuping Jia, Antonio Plaza, Paolo Gamba, Jon Atli Benediktsson, Jocelyn Chanussot
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
1 code implementation • 7 Apr 2023 • Xuyang Li, Jianwu Fang, Kai Du, Kuizhi Mei, Jianru Xue
This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method for a large-scale 3D complex environment.
no code implementations • 7 Mar 2023 • Yuhan Cao, Haoran Jiang, Zhenghong Yu, Qi Li, Xuyang Li
While Generative Adversarial Networks (GANs) have recently found applications in image editing, most previous GAN-based image editing methods require largescale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images.
no code implementations • 30 Jan 2023 • Xuyang Li, Guangchun Ruan, Haiwang Zhong
Because the Nash bargaining solution satisfies Pareto effectiveness, we analyze the computational complexity of Pareto frontiers with parametric linear programming and interpret the inefficiency of the method.
no code implementations • 19 Dec 2022 • Hamed Bolandi, Gautam Sreekumar, Xuyang Li, Nizar Lajnef, Vishnu Naresh Boddeti
Therefore, to reduce computational cost while preserving accuracy, a deep learning model, Neuro-DynaStress, is proposed to predict the entire sequence of stress distribution based on finite element simulations using a partial differential equation (PDE) solver.
no code implementations • 28 Nov 2022 • Hamed Bolandi, Gautam Sreekumar, Xuyang Li, Nizar Lajnef, Vishnu Naresh Boddeti
Therefore, to reduce computational cost while maintaining accuracy, a Physics Informed Neural Network (PINN), PINN-Stress model, is proposed to predict the entire sequence of stress distribution based on Finite Element simulations using a partial differential equation (PDE) solver.
1 code implementation • 26 Aug 2022 • Xuyang Li, Hamed Bolandi, Talal Salem, Nizar Lajnef, Vishnu Naresh Boddeti
Structural monitoring for complex built environments often suffers from mismatch between design, laboratory testing, and actual built parameters.
no code implementations • 30 Apr 2021 • Yubin Ge, Site Li, Xuyang Li, Fangfang Fan, Wanqing Xie, Jane You, Xiaofeng Liu
The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk.
no code implementations • 29 Jan 2021 • Guangming Shi, Dahua Gao, Xiaodan Song, Jingxuan Chai, Minxi Yang, Xuemei Xie, Leida Li, Xuyang Li
In this article, we deploy semantics to solve the spectrum and power bottleneck and propose a first understanding and then transmission framework with high semantic fidelity.
Networking and Internet Architecture