Discovering and tracking spatiotemporal event patterns have many applications. For example, in a smart-home project, a set of spatiotemporal pattern learning automata are used to monitor a user's repetitive activities, by which the home's automaticity can be promoted while some of his/her burdens can be reduced. Existing algorithms for spatiotemporal event pattern recognition in dynamic noisy environment are based on fixed structure stochastic automata whose state transition function is fixed and predesigned to guarantee their immunity to noise. However, such design is conservative because it needs continuous and identical feedbacks to converge, thus leading to its very low convergence rate. In many real-life applications, such as ambient assisted living, consecutive nonoccurrences of an elder resident's routine activities should be treated with an alert as quickly as possible. On the other hand, no alert should be output even for some occurrences in order to diminish the effects caused by noise. Clearly, confronting a pattern's change, slow speed and low accuracy may degrade a user's life security. This paper proposes a fast and accurate leaning automaton based on variable structure stochastic automata to satisfy the realistic requirements for both speed and accuracy. Bias toward alert is necessary for elder residents while the existing method can only support the bias toward “no alert.” This paper introduces a method to allow bias toward alert or no alert to meet a user's specific bias requirement. Experimental results show its better performance than the state-of-the-art methods.

中文翻译：

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用于发现和跟踪时空事件模式的快速变结构随机自动机


发现和跟踪时空事件模式有很多应用。例如，在智能家居项目中，使用一组时空模式学习自动机来监控用户的重复活动，通过这些自动机可以提高家庭的自动化程度，同时减轻他/她的一些负担。现有的动态噪声环境下时空事件模式识别算法基于固定结构随机自动机，其状态转移函数是固定的并预先设计的，以保证其对噪声的抗扰性。然而，这种设计是保守的，因为它需要连续且相同的反馈才能收敛，从而导致其收敛速度非常低。在许多现实生活应用中，例如环境辅助生活，应尽快通过警报来处理老年居民连续不进行日常活动的情况。另一方面，即使在某些情况下也不应该输出警报，以减少噪声造成的影响。显然，面对模式的变化，速度慢、精度低可能会降低用户的生命安全。本文提出了一种基于变结构随机自动机的快速准确的学习自动机，以满足速度和精度的现实要求。对于老年居民来说，偏向警觉是必要的，而现有的方法只能支持偏向“不警觉”。本文介绍了一种允许偏向警报或无警报的方法，以满足用户的特定偏向要求。实验结果表明其性能优于最先进的方法。