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Correlation filter (CF) tracker has many advantages in visual tracking. But the update strategies of most CF trackers today such as STC are so simple that it can not handle more complex situations. For this problem, the paper presents a novel CF tracker based on templates, spatio-temporal context template set (STCTS) tracker. The algorithm not only improves the tracking strategy, but also improves...
Modularization is a significant way to ease the challenge of evaluating large-scale fault trees known as a NP-hard problem, especially for BDD algorithm. In our previous work, we proposed an effective parallel modularization algorithm for large-scale coherent fault trees which only include gates with AND logic and OR logic. But in real engineering scenarios, fault trees usually consist of many complex...
Fault-tree analysis is a useful analytic tool for the reliability and safety of complex system. However, fault tree is not suitable for repairable system. In this paper, we will propose a new method called TTF (time to failure) and TTM (time to maintenance) to analyze repairable system. Nevertheless, Monte Carlo simulation may be time consuming. In order to reduce simulation time, a parallel algorithm...
Driven by the problems that the Tracking and Data Relay Satellite System (TDRSS) cannot meet the requirement of cooperative burst communication (CBC), a spreading code structure based on the burst signal pilot channel and a fast acquisition algorithm based on this new signal structure are proposed. The new designed spreading code is composed of short spreading code sequence modulated by overlay code...
Anomaly detection has been an important research topic in data mining and machine learning. Many real-world applications such as intrusion or credit card fraud detection require an effective and efficient framework to identify deviated data instances. However, most anomaly detection methods are typically implemented in batch mode, and thus cannot be easily extended to large-scale problems without...
In order to improve the accuracy and efficiency of directly use of the k-means clustering for semi-supervised learning, the paper proposes a new semi-supervised learning algorithm based on radius-distance. In the algorithm, according to radius, farthest distance of sample to the cluster center of unlabelled samples using k-means, and distance, from cluster center of unlabelled samples to center of...
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