Evaluation SSG-LRR Model on an Homogeneous Turbulence under Inclined Shear for High and Low Stratification: Shear Number Effect

Autor: Lamia Thamri, Taoufik Naffouti

Publikováno v: Boundary Layer Flows-Modelling, Computation, and Applications of Laminar, Turbulent Incompressible and Compressible Flows
Boundary Layer Flows-Modelling, Computation, and Applications of Laminar, Turbulent Incompressible and Compressible Flows ISBN: 9781803562186

This chapter develops proposals for an evaluation coupled second order model of SSG-LRR on an homogeneous turbulence submitted to an inclined shear for high and low stratification. The effect of Shear number on thermal and dynamic turbulent fields of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f7102e1f30a2cd99d43d27f201c0b5b
https://mts.intechopen.com/articles/show/title/evaluation-ssg-lrr-model-on-an-homogeneous-turbulence-under-inclined-shear-for-high-and-low-stratifi

Low-Rank Discriminative Embedding Regression for Robust Feature Extraction of Hyperspectral Images via Weighted Schatten p-Norm Minimization.

Autor: Long, Chen-Feng^1,2 (AUTHOR), Li, Ya-Ru^1,2 (AUTHOR), Deng, Yang-Jun^1,2 (AUTHOR) dengyangjun@hunau.edu.cn, Wang, Wei-Ye³ (AUTHOR), Zhu, Xing-Hui² (AUTHOR), Du, Qian⁴ (AUTHOR)

Publikováno v: Remote Sensing. Aug2024, Vol. 16 Issue 16, p3081. 20p.

Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary.

Autor: Cheng, Xi¹ (AUTHOR) cheng_xi@stu.xidian.edu.cn, Mu, Ruiqi¹ (AUTHOR), Lin, Sheng¹ (AUTHOR), Zhang, Min¹ (AUTHOR), Wang, Hai¹ (AUTHOR) wanghai@mail.xidian.edu.cn

Publikováno v: Remote Sensing. Jun2024, Vol. 16 Issue 11, p1837. 25p.

Rational Pessimism, Rational Exuberance, and Asset Pricing Models.

Autor: BANSAL, RAVI¹, GALLANT, A. RONALD¹, TAUCHEN, GEORGE²

Publikováno v: Review of Economic Studies. Oct2007, Vol. 74 Issue 4, p1005-1033. 29p. 10 Charts, 4 Graphs.

Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior

Autor: Liu, Yidan, Xie, Weiying, Jiang, Kai, Zhang, Jiaqing, Li, Yunsong, Fang, Leyuan

The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e.g., $\ell_{

Externí odkaz: http://arxiv.org/abs/2404.13342