FIDE: Fast and Interpretable 2D Embedding with correlation, distance, and rank considerations

Author(s):  
Nikolaos M. Freris ◽  
Chuhan Yang ◽  
Michalis Vlachos
Keyword(s):  
Author(s):  
Aidan Hughes ◽  
Sung Yun Jun ◽  
Camillo Gentile ◽  
Derek Caudill ◽  
Jack Chuang ◽  
...  

1997 ◽  
pp. 47-47
Author(s):  
Arsen R. Hajian ◽  
Adam Frank ◽  
Bruce Balick ◽  
Yervant Terzian
Keyword(s):  

Author(s):  
Yang Yong ◽  
Haifeng Zhang ◽  
Zhao Zhihai ◽  
Fan Wen ◽  
Jiang Guichun
Keyword(s):  

2014 ◽  
Vol 15 (2) ◽  
pp. 801-812 ◽  
Author(s):  
Ali Tokay ◽  
Rigoberto J. Roche ◽  
Paul G. Bashor

Abstract Spatial variability of rainfall was studied through a gauge network on the Delmarva Peninsula. The gauge network consists of 11 dual- or triple-tipping-bucket sites ranging from 1- to 150-km separation distances. The time of the tip (0.254 mm) was recorded to a datalogger, and a continuous dataset was available from all sites for over 5 yr (May 2005–July 2010). A three-parameter exponential function was fitted to the paired correlations and the resultant correlation distance was 8–13 km during summer and 51–85 km during winter. The correlation distances showed pronounced year-to-year variability as being 8–43 km and 13–67 km during spring and autumn, respectively. The airmass convection was the main weather system during summer while nor’easters played an important role during winter. The 30-min integration and two-tip rain/no-rain threshold was selected for the base of this study. The correlation distance increased with longer integration periods and was 17 and 32 km for 30 min and 1 h, respectively.


1983 ◽  
Vol 22 (1) ◽  
pp. 170 ◽  
Author(s):  
N. Takai ◽  
T. Iwai ◽  
T. Asakura
Keyword(s):  

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4433 ◽  
Author(s):  
Mikael Nilsson ◽  
Carl Gustafson ◽  
Taimoor Abbas ◽  
Fredrik Tufvesson

The non line-of-sight (NLOS) scenario in urban intersections is critical in terms of traffic safety—a scenario where Vehicle-to-Vehicle (V2V) communication really can make a difference by enabling communication and detection of vehicles around building corners. A few NLOS V2V channel models exist in the literature but they all have some form of limitation, and therefore further research is need. In this paper, we present an alternative NLOS path loss model based on analysis from measured V2V communication channels at 5.9 GHz between six vehicles in two urban intersections. We analyze the auto-correlation of the large scale fading process and the influence of the path loss model on this. In cases where a proper model for the path loss and the antenna pattern is included, the de-correlation distance for the auto-correlation is as low as 2–4 m, and the cross-correlation for the large scale fading between different links can be neglected. Otherwise, the de-correlation distance has to be much longer and the cross-correlation between the different communication links needs to be considered separately, causing the computational complexity to be unnecessarily large. With these findings, we stress that vehicular ad-hoc network (VANET) simulations should be based on the current geometry, i.e., a proper path loss model should be applied depending on whether the V2V communication is blocked or not by other vehicles or buildings.


2011 ◽  
Vol 480-481 ◽  
pp. 1412-1417
Author(s):  
Xiao Yong Li

The correlation distance is one of the important parameters for the application of random field theory to reliability analyses. Soil spatial variability is related to soil point variability with the reduction factor of variance in random field theory, and the reduction factor of variance depends on both soil auto-correlation distance and spatial area. The sampling space effect on auto-correlation distance is studied. The vertical and horizontal correlation distances of typical stratum are analyzed in statistics based on a large amount of investigation data and the representative values of correlation distance of local area are obtained. It is concluded that that the correlation distances estimated by different soil parameters are similar, and the horizontal correlation distance is much larger than the vertical one for the same soil parameter. The sampling space should be paid attention to when calculating correlation distance of soil parameter.


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