The phased-array weather radar (PAWR) is a new-generation weather radar that can make a 100-m-resolution three-dimensional (3D) volume scan every 30 s for 100 vertical levels, producing ~100 times more data than the conventional parabolic-antenna radar with a volume scan typically made every 5 min for 15 scan levels. This study takes advantage of orders of magnitude more rapid and dense observations by PAWR and explores high-precision nowcasting of 3D evolution at 1–10-km scales up to several minutes, which are compared with conventional horizontal two-dimensional (2D) nowcasting typically at O(100) km scales up to 1–6 h. A new 3D precipitation extrapolation system was designed to enhance a conventional algorithm for dense and rapid PAWR volume scans. Experiments show that the 3D extrapolation successfully captured vertical motions of convective precipitation cores and outperformed 2D nowcasting with both simulated and real PAWR data.
Most of the data, which is in the field of network intrusion detection, have the characteristics of a mixture of high-dimensional datasets of continuous and categorical variables. It easily leads the traditional multivariate control chart to get the error detection results. Hotelling's T 2 multivariate control charts based on Principal Component Analysis mix (PCA mix) with bootstrap control limit were proposed, and applied to the network intrusion detection system. It was compared with the conventional Hotelling's T 2 control chart based on PCA and the performance of the control limits obtained with the bootstrap method was compared to the ones calculated using the most commonly used kernel density estimation. The experimental results revealed that the proposed method had better performance in intrusion detection than its counterparts.
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