Topological effects on the mechanical properties of polymer knots

Internet of Moving Things are connected to a variety of different types of sensors to form a world of moving things, including people, animals, vehicles, drones, and boats, etc. As the data of collectible moving things continue to increase, anomaly detection of moving things has become an increasingly popular data mining task. Traditional trajectory outlier detection algorithms can detect common anomalies effectively, but it is hard to detect generalized anomalies, such as viewable direction anomalies, gravity anomalies, and magnetic field anomalies which can be collected by the accelerometer, gyroscope, magnetometer, and RPM sensor, etc. For this, we proposed a generalized approach for anomaly detection from the Internet of Moving Things, called the moving things outlier detection algorithm (MTOD). We propose the distance of moving things, which is equal to the weighted sum of the location distance and the multi-sensor distance, and then use the multi-sensor data generalization and moving things partitioning and anomaly detection threestep framework to detect the generalized anomaly. The experimental results show that our MTOD algorithm can detect moving things anomaly efficiency and accurately.

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“…Then formally defining the three components through Eq. (3)(4)(5). Suppose there are two line segments L i = x i y i and L j = x j y j .…”

Section: ) Hausdorff Distance Functionmentioning

confidence: 99%