论文标题
使用深度复发自动编码器中的蜂箱中的异常检测
Anomaly Detection in Beehives using Deep Recurrent Autoencoders
论文作者
论文摘要
精确养蜂可以通过为蜂箱配备传感器来监视蜜蜂的生活条件。这些蜂箱记录的数据可以通过机器学习模型来分析，以学习蜜蜂菌落中异常事件的行为模式。一个典型的目标是早期发现蜜蜂蜂群，因为Apiarists希望由于经济原因避免这种情况。高级方法应该能够检测出因蜜蜂疾病或技术原因而引起的任何其他异常或异常行为，例如传感器故障。 在该职位上，我们提出了一个自动编码器，即一种深度学习模型，该模型检测到与其起源无关的数据中的任何类型的异常。我们的模型能够揭示与简单的基于规则的群检测算法相同的群，但也由任何其他异常触发。我们评估了我们在不同蜂箱和不同传感器设置上收集的现实世界数据集的模型。
Precision beekeeping allows to monitor bees' living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to learn behavioral patterns of or search for unusual events in bee colonies. One typical target is the early detection of bee swarming as apiarists want to avoid this due to economical reasons. Advanced methods should be able to detect any other unusual or abnormal behavior arising from illness of bees or from technical reasons, e.g. sensor failure. In this position paper we present an autoencoder, a deep learning model, which detects any type of anomaly in data independent of its origin. Our model is able to reveal the same swarms as a simple rule-based swarm detection algorithm but is also triggered by any other anomaly. We evaluated our model on real world data sets that were collected on different hives and with different sensor setups.
