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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]

Earthquake early warning system uses high-speed computer network to transmit earthquake information to population center ahead of the arrival of destructive earthquake waves. This short (10 s of seconds) lead time will allow emergency responses such as turning off gas pipeline valves to be activated to mitigate potential disaster and casualties. However, the excessive false alarm rate of such a system imposes heavy cost in terms of loss of services, undue panics, and diminishing credibility of such a warning system. At the current, the decision algorithm to issue an early warning of the onset of an earthquake is often based on empirically chosen features and heuristically set thresholds and suffers from excessive false alarm rate. In this paper, we experimented with three advanced machine learning algorithms, namely, $K$ -nearest neighbor (KNN), classification tree, and support vector machine (SVM) and compared their performance against a traditional criterion-based method. Using the seismic data collected by an experimental strong motion detection network in Taiwan for these experiments, we observed that the machine learning algorithms exhibit higher detection accuracy with much reduced false alarm rate. [ABSTRACT FROM AUTHOR]

Group services are highly important for a variety of computing application domains. In this paper, we study the fundamental problem of allocating a set of service patrons to a set of service groups in an attempt to maximize the total profit gained by the grouping platform. The problem under consideration is unique in that group service is not provided at all unless its lower bound requirement is satisfied. In addition, we allow each service patron to join multiple groups. In this paper, after proving the hardness property of the problem, we focus first on a special case of the problem. To this end, we propose two approaches. One aims at providing a suboptimal solution using a 1/2-approximation algorithm. The other approach turns to seeking an optimal solution using a branch and bound technique. For this purpose, we introduce a theorem that captures a useful property of an optimal allocation. Based on this theorem, we design an efficient branch and bound algorithm to find an optimal solution. We then extend these methods to solve the general problem. Extensive experiments show that our branch and bound algorithm is able to obtain an optimal solution with a small amount of computation time in many different settings. [ABSTRACT FROM PUBLISHER]

Target detection is one of the most important topics in wireless sensor networks. Many studies in the literature have addressed the problem of evaluating the performance of a sensor network based on detection probability. However, it is difficult to guarantee detection probability in a sensor network since it depends on the topology of the sensor deployment and the location of the target. A sensor network without a careful sensor location arrangement may experience very low detection probability. This paper integrates collaborative fusion and sequential detection to guarantee the quality of the decisions made by a sensor network and analytically derives the average detection latency based on value fusion and decision fusion. Specifically, sensors periodically report their local measurements or decisions to a fusion center. The fusion center makes final decisions only when both the pre-defined false alarm probability and missing probability are satisfied. Otherwise, it will continue to collect data and repeat the decision making operations. Simple and elegant detection rules are provided for the collaborative sequential detection operations. Extensive simulations are conducted to show the performance of a sensor network in terms of detection latency. The correctness of the analytical results for detection latency is also verified by simulations. [ABSTRACT FROM AUTHOR]