Zobrazeno 1 - 10
of 539
pro vyhledávání: '"SUN Qiyu"'
Publikováno v:
Meikuang Anquan, Vol 52, Iss 11, Pp 205-210 (2021)
Taking Daliuta Mine as the engineering background, the law of surface movement and subsidence observation in Daliuta Mine was studied by using numerical simulation method and field data analysis. The numerical simulation analysis shows that the rapid
Externí odkaz:
https://doaj.org/article/c8ed404550d446d0bc3f0b56311f3fc6
This paper presents a Carleman-Fourier linearization method for nonlinear dynamical systems with periodic vector fields involving multiple fundamental frequencies. By employing Fourier basis functions, the nonlinear dynamical system is transformed in
Externí odkaz:
http://arxiv.org/abs/2411.11598
Let ${\mathcal B}$ be a Banach algebra and ${\mathcal A}$ be its Banach subalgebra that admits a norm-controlled inversion. In this work, we take $A, B$ in the Banach subalgebra ${\mathcal A}$ with the spectra of $A$ and $B$ in the Banach algebra ${\
Externí odkaz:
http://arxiv.org/abs/2407.09752
Computer vision tasks are crucial for aerospace missions as they help spacecraft to understand and interpret the space environment, such as estimating position and orientation, reconstructing 3D models, and recognizing objects, which have been extens
Externí odkaz:
http://arxiv.org/abs/2407.06513
Autor:
Pandey, Vivek, Amini, Arash, Liu, Guangyi, Topcu, Ufuk, Sun, Qiyu, Daniilidis, Kostas, Motee, Nader
We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measurements with neighbors. We select a set of the most informative features
Externí odkaz:
http://arxiv.org/abs/2403.12279
Autor:
Chung, Seok-Young, Sun, Qiyu
Graph convolutional neural network (GCNN) operates on graph domain and it has achieved a superior performance to accomplish a wide range of tasks. In this paper, we introduce a Barron space of functions on a compact domain of graph signals. We prove
Externí odkaz:
http://arxiv.org/abs/2311.02838
Autor:
Zhao, Chaoqiang, Poggi, Matteo, Tosi, Fabio, Zhou, Lei, Sun, Qiyu, Tang, Yang, Mattoccia, Stefano
This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture. We ease the learning process by obtaining coarse camera poses from monocular sequences through m
Externí odkaz:
http://arxiv.org/abs/2309.16019
Domain generalized semantic segmentation (DGSS) is a critical yet challenging task, where the model is trained only on source data without access to any target data. Despite the proposal of numerous DGSS strategies, the generalization capability rema
Externí odkaz:
http://arxiv.org/abs/2309.06282
Most nighttime semantic segmentation studies are based on domain adaptation approaches and image input. However, limited by the low dynamic range of conventional cameras, images fail to capture structural details and boundary information in low-light
Externí odkaz:
http://arxiv.org/abs/2307.15942
Deep learning has revolutionized the field of artificial intelligence. Based on the statistical correlations uncovered by deep learning-based methods, computer vision has contributed to tremendous growth in areas like autonomous driving and robotics.
Externí odkaz:
http://arxiv.org/abs/2307.13992