This paper introduces a multi-hopped wireless sensor network for remote bridge health monitoring, including system architecture, transmission protocol, data acquiring and processing. This system based on detecting the three-dimension vibration or acceleration data of the bridge, which data is caused by external impacts such as a car or a truck. Comparing with traditional monitoring systems, this bridge health monitoring system has advantages in human-free, long-life, and real-time responses. Additionally, to compare data of a damaged bridge and a healthy one, an experiment of making artificial damage to a bridge is carried out with consent of related departments. Moreover, this system can be applied in any form of bridges. In the future, it is even expected to be applied in other kind of buildings more than bridges.
SUMMARYThis paper represents an illumination modeling method for lighting control which can model the illumination distribution inside office buildings. The algorithm uses data from the illumination sensors to train Radial Basis Function Neural Networks (RBFNN) which can be used to calculate 1) the illuminance contribution from each luminaire to different positions in the office 2) the natural illuminance distribution inside the office. This method can be used to provide detailed illumination contribution from both artificial and natural light sources for lighting control algorithms by using small amount of sensors. Simulations with DIALux are made to prove the feasibility and accuracy of the modeling method. key words: office lighting, energy saving system, radial basis function neural networks
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