Papers by Keyword: Gauss Distribution Function

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Authors: Kuo Lan Su, Jr Hung Guo, Cheng Yun Chung, Cheng Yun Chung
Abstract: The paper develops a fire detection system using mobile robots, and calculates the risk values of the escaping paths using Bayesian estimated method. Mobile robots contain two types moving in the platform. One is fire detection robot (FDR) to search fire sources. The other represents the people walking in the platform autonomously. The controller of the mobile robot detects fire source using flame sensor, and receives the motion command from the supervised compute via wireless RF interface. The mobile robot transmits ID code, position and orientation information, positions of fire sources to the supervised computer via wireless RF interface, too. We program the motion path of fire detection robots to search fire sources, and uses Gauss distribution function to describe the risk values of each fire source. The supervised computer uses Bayesian estimated algorithm to calculate the relation risk value of each cross point for multiple fire sources. In the fire condition, each FDR calculates shortest displacement from the people. The assigned FDR carries the people leaving the dangerous area. Then the user interface programs the escaping paths using A* searching algorithm for mobile robots. The mobile robot guides the people (mobile robot) leaving the fire area according the programmed safety escaping path.
Authors: Chun Long Ma, Nan Li, Ying Li
Abstract: WSN’s practical application has high level of spontaneity in positioning information and is susceptible to the outside interference, which may lead to the reduction of positioning accuracy. This paper studies Gauss distribution function, the single sensor batch estimate fusion theory and RSSI positioning technique based on the design of the CC2430/1 wireless sensor network positioning system hardware and puts forward a kind of positioning algorithm of WSN based on the Gauss distribution function and batch estimation fusion theory. The algorithm first uses Gauss model to analyse RSSI, reject RSSI value of a small probability event through the Gauss model, and then applies single sensor batch estimation fusion theory to estimate in batches the fushion theory’s calculation of the RSSI value of high probability events as the RSSI value of the final positioning operation. Experiments show that using this algorithm, the positioning precision of the system can be improved to be within 0.5 meters.
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