scholarly journals Principal Component Analysis of the Summertime Winds over the Gulf of California: A Gulf Surge Index

2006 ◽  
Vol 134 (11) ◽  
pp. 3395-3414 ◽  
Author(s):  
Simona Bordoni ◽  
Bjorn Stevens
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Gulf Of California ◽  
Large Scale ◽  
Monsoon Season ◽  
Principal Component ◽  
Component Analysis ◽  
Near Surface ◽  
The North ◽  
Gulf Surges

Abstract A principal component analysis of the summertime near-surface Quick Scatterometer (QuikSCAT) winds is used to identify the leading mode of synoptic-scale variability of the low-level flow along the Gulf of California during the North American monsoon season. A gulf surge mode emerges from this analysis as the leading EOF, with the corresponding principal component time series interpretable as an objective index for gulf surge occurrence. This index is used as a reference time series for regression analysis and compositing meteorological fields of interest, to explore the relationship between gulf surges and precipitation over the core and marginal regions of the monsoon, as well as the manifestation of these transient events in the large-scale circulation. It is found that, although seemingly mesoscale features confined over the Gulf of California, gulf surges are intimately linked to patterns of large-scale variability of the eastern Pacific ITCZ and greatly contribute to the definition of the northward extent of the monsoonal rains.

scholarly journals Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

2021 ◽  
Vol 13 (20) ◽  
pp. 4123
Author(s):  
Hanqi Wang ◽  
Zhiling Wang ◽  
Linglong Lin ◽  
Fengyu Xu ◽  
Jie Yu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Pose Estimation ◽  
Point Cloud ◽  
Principal Component ◽  
Component Analysis ◽  
Evaluation Index ◽  
Evaluation Indexes ◽  
Learning Network ◽  
Estimation Algorithms

Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

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