Short-Term and Long-Term Solutions of Two-Dimensional Shallow Water Equations Using the Modified Decomposition Method

Author(s):  
Hossein Saboorkazeran ◽  
Mehdi Raoofian Naeeni ◽  
Mohammad Ali Banihashemi
Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2152
Author(s):  
Gonzalo García-Alén ◽  
Olalla García-Fonte ◽  
Luis Cea ◽  
Luís Pena ◽  
Jerónimo Puertas

2D models based on the shallow water equations are widely used in river hydraulics. However, these models can present deficiencies in those cases in which their intrinsic hypotheses are not fulfilled. One of these cases is in the presence of weirs. In this work we present an experimental dataset including 194 experiments in nine different weirs. The experimental data are compared to the numerical results obtained with a 2D shallow water model in order to quantify the discrepancies that exist due to the non-fulfillment of the hydrostatic pressure hypotheses. The experimental dataset presented can be used for the validation of other modelling approaches.


2021 ◽  
pp. 105152
Author(s):  
Victor Michel-Dansac ◽  
Christophe Berthon ◽  
Stéphane Clain ◽  
Françoise Foucher

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141769231 ◽  
Author(s):  
Ning An ◽  
Shi-Ying Sun ◽  
Xiao-Guang Zhao ◽  
Zeng-Guang Hou

Visual tracking is a challenging computer vision task due to the significant observation changes of the target. By contrast, the tracking task is relatively easy for humans. In this article, we propose a tracker inspired by the cognitive psychological memory mechanism, which decomposes the tracking task into sensory memory register, short-term memory tracker, and long-term memory tracker like humans. The sensory memory register captures information with three-dimensional perception; the short-term memory tracker builds the highly plastic observation model via memory rehearsal; the long-term memory tracker builds the highly stable observation model via memory encoding and retrieval. With the cooperative models, the tracker can easily handle various tracking scenarios. In addition, an appearance-shape learning method is proposed to update the two-dimensional appearance model and three-dimensional shape model appropriately. Extensive experimental results on a large-scale benchmark data set demonstrate that the proposed method outperforms the state-of-the-art two-dimensional and three-dimensional trackers in terms of efficiency, accuracy, and robustness.


2019 ◽  
Author(s):  
Lachlan Kent ◽  
George Van Doorn ◽  
Britt Klein

This study uses a combined categorical-dimensional approach to depict a hierarchical framework for consciousness similar to, and contiguous with, factorial models of cognition (cf., intelligence). On the basis of the longstanding definition of time consciousness, the analysis employs a dimension of temporal extension, in the same manner that psychology has temporally organised memory (i.e., short-term, long-term, and long-lasting memories). By defining temporal extension in terms of the structure of time perception at short timescales (< 100 s), memory and time consciousness are proposed to fit along the same logarithmic dimension. This suggests that different forms of time consciousness (e.g., experience, wakefulness, and self-consciousness) are embedded within, or supported by, the ascending timescales of different modes of memory (i.e., short-term, long-term, etc.). A secondary dimension is also proposed to integrate higher-order forms of consciousness/emotion and memory/cognition. The resulting two-dimensional structure accords with existing theories of cognitive and emotional intelligence.


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