Implementation and Analysis of the TCP "Adaptive-Selection" Concept in ns-2 and Linux

2008 ◽  
Author(s):  
Carlo Caini ◽  
Federico Farina ◽  
Rosario Firrincieli ◽  
Angelo Tornese ◽  
Daniele Lacamera
Keyword(s):  
Adaptive Selection

Adaptive Selection of Sampling Interval in Frequency Domain for Estimating the Poles of Scatterers

PIERS Online ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 106-110
Author(s):  
Shaogang Wang ◽  
Xinpu Guan ◽  
Dangwei Wang ◽  
Xingyi Ma ◽  
Yi Su
Keyword(s):  
Frequency Domain ◽  
Sampling Interval ◽  
Adaptive Selection ◽  
Selection Of

SSAS: Spatiotemporal Scale Adaptive Selection for Improving Bias Correction on Precipitation

2021 ◽  
pp. 1-14
Author(s):  
Yiqun Liu ◽  
Junping Zhang ◽  
Lei Chen ◽  
Hai Chu ◽  
James Z. Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Adaptive Selection ◽  
Spatiotemporal Scale ◽  
Selection For

scholarly journals A Digital Framework to Predict the Sunshine Requirements of Landscape Plants

2021 ◽  
Vol 11 (5) ◽  
pp. 2098
Author(s):  
Heyi Wei ◽  
Wenhua Jiang ◽  
Xuejun Liu ◽  
Bo Huang
Keyword(s):  
Solar Radiation ◽  
Plant Species ◽  
Large Scale ◽  
Normal Growth ◽  
Adaptive Selection ◽  
Landscape Plant ◽  
Landscape Plants ◽  
The Stability ◽  
Stability And Accuracy ◽  
The City

Knowledge of the sunshine requirements of landscape plants is important information for the adaptive selection and configuration of plants for urban greening, and is also a basic attribute of plant databases. In the existing studies, the light compensation point (LCP) and light saturation point (LSP) have been commonly used to indicate the shade tolerance for a specific plant; however, these values are difficult to adopt in practice because the landscape architect does not always know what range of solar radiation is the best for maintaining plant health, i.e., normal growth and reproduction. In this paper, to bridge the gap, we present a novel digital framework to predict the sunshine requirements of landscape plants. First, the research introduces the proposed framework, which is composed of a black-box model, solar radiation simulation, and a health standard system for plants. Then, the data fitting between solar radiation and plant growth response is used to obtain the value of solar radiation at different health levels. Finally, we adopt the LI-6400XT Portable Photosynthetic System (Li-Cor Inc., Lincoln, NE, USA) to verify the stability and accuracy of the digital framework through 15 landscape plant species of a residential area in the city of Wuhan, China, and also compared and analyzed the results of other researchers on the same plant species. The results show that the digital framework can robustly obtain the values of the healthy, sub-healthy, and unhealthy levels for the 15 landscape plant species. The purpose of this study is to provide an efficient forecasting tool for large-scale surveys of plant sunshine requirements. The proposed framework will be beneficial for the adaptive selection and configuration of urban plants and will facilitate the construction of landscape plant databases in future studies.

An adaptive selection approach for the 2D rectangle packing area minimization problem

Omega ◽  
2018 ◽  
Vol 80 ◽  
pp. 22-30 ◽  
Author(s):  
Lijun Wei ◽  
Wenbin Zhu ◽  
Andrew Lim ◽  
Qiang Liu ◽  
Xin Chen
Keyword(s):  
Minimization Problem ◽  
Adaptive Selection ◽  
Rectangle Packing ◽  
Area Minimization ◽  
Selection Approach
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