Zobrazeno 1 - 10
of 760
pro vyhledávání: '"Tao Chao"'
Publikováno v:
Defence Technology, Vol 26, Iss , Pp 157-179 (2023)
The large-range uncertainties of specific impulse, mass flow per second, aerodynamic coefficients and atmospheric density during rapid turning in solid launch vehicles (SLVs) ascending leads to the deviation of the actual trajectory from the referenc
Externí odkaz:
https://doaj.org/article/699f52e13fd44d71a882b4a5d02920e7
Publikováno v:
IET Control Theory & Applications, Vol 17, Iss 8, Pp 985-1001 (2023)
Abstract This paper aims to improve the efficiency of parameter identification of the nonlinear state‐space model (SSM). The commonly used particle Markov chain Monte Carlo (PMCMC) method is time‐consuming. The surrogate model is a useful acceler
Externí odkaz:
https://doaj.org/article/3f2d4e67313142da9228ad6ef5a3573a
End-to-end interpretation is currently the prevailing paradigm for remote sensing fine-grained ship classification (RS-FGSC) task. However, its inference process is uninterpretable, leading to criticism as a black box model. To address this issue, we
Externí odkaz:
http://arxiv.org/abs/2408.06631
Publikováno v:
IEEE Access, Vol 7, Pp 68106-68118 (2019)
This paper presents a performance recoverable control scheme for air-breathing hypersonic vehicles (HSVs) with nonminimum phase characteristics. As elevator and throttle are the only two control inputs available for longitudinal trajectory tracking o
Externí odkaz:
https://doaj.org/article/5ac4b02daf224afba07ca35426acb5cc
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing 2024
The tokenizer, as one of the fundamental components of large models, has long been overlooked or even misunderstood in visual tasks. One key factor of the great comprehension power of the large language model is that natural language tokenizers utili
Externí odkaz:
http://arxiv.org/abs/2403.18593
In the later training stages, further improvement of the models ability to determine changes relies on how well the change detection (CD) model learns hard cases; however, there are two additional challenges to learning hard case samples: (1) change
Externí odkaz:
http://arxiv.org/abs/2402.16242
Data-driven deep learning methods have shown great potential in cropland mapping. However, due to multiple factors such as attributes of cropland (topography, climate, crop type) and imaging conditions (viewing angle, illumination, scale), croplands
Externí odkaz:
http://arxiv.org/abs/2310.10219
Self-supervised contrastive learning (SSCL) has achieved significant milestones in remote sensing image (RSI) understanding. Its essence lies in designing an unsupervised instance discrimination pretext task to extract image features from a large num
Externí odkaz:
http://arxiv.org/abs/2306.15868
Publikováno v:
Journal of Applied Physics; 9/21/2024, Vol. 136 Issue 11, p1-12, 12p
Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between feature learning paradigms (e.g., unsupervised feature learning (USFL), supervised feature learning (
Externí odkaz:
http://arxiv.org/abs/2211.08129