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Taiwan that is located at the junction of the Eurasian Plate and the Philippine Sea Plate is one of the most active seismic zones in the world. Devastating earthquakes have occurred around the island and have caused severe damages from time to time. To avoid the severe loss, earthquake early warning (EEW) is of great importance, and one of the most critical issues of EEW is fast and reliable detection for the presence of earthquakes. Traditional methods for earthquake detection usually use criterion-based algorithms to detect the onset of the earthquake waves. Currently, the thresholds for those criteria are usually decided empirically and may result in excessive false alarms. Obviously, false alarms can cause undue panics and diminish the credibility of the system. In this article, the recurrent neural network (RNN) models are adopted to develop a real-time EEW system. The developed system is designed to identify the occurrence of an earthquake event, and the duration of the P-wave and the S-wave. It was trained and tested using the seismograms recorded in Taiwan from 2016 to 2017. From the simulation results, the proposed scheme outperforms the traditional criterion-based schemes in terms of detection accuracy and processing time. [ABSTRACT FROM AUTHOR]

This work considers data query applications in tree-structured networks, where a given set of source nodes generate (or collect) data and forward the data to some halfway storage nodes for satisfying queries that call for data generated by all source nodes. The goal is to determine an optimal set of storage nodes that minimizes overall communication cost. Prior work toward this problem assumed homogeneous channel cost, which may not be the case in many network environments. We generalize the optimal storage problem for a tree-structured network by considering heterogeneous channel costs. The necessary and sufficient conditions for the optimal solution are identified, and an algorithm that incurs a linear time cost is proposed. We have also conducted extensive simulations to validate the algorithm and to evaluate its performance. [ABSTRACT FROM AUTHOR]