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《Laser Journal》 2017-12
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High precision classification of optical laser network abnormal data in cloud computing environment

LIANG Jianping;Zhejiang Changzheng Vocational College of Technology;  
Traditional classification method based on distance can only obtain globular clusters,not the irregular shape clusters of optical fiber laser network in cloud computing environment which cannot realize accurate classification of network abnormal data. Therefore,abnormal data precision classification method based on DBSCAN is put forward,which includes training and testing processes. In training process,the optical laser sensor collects data first and feeds back the data to sink node center through the base station. Then adopts the DBSCAN algorithm to train data. Collect and pass the valuable environmental features to the sink node. In testing process,the Sink node will pass the features to laser sensor nodes and the laser sensor node will calculate and test the Euclid distance of core center in the environmental features. If the distance is higher than the training radius of DBSCAN algorithm,the testing data is abnormal data. The experimental results indicate that the proposed method has high classification efficiency,accuracy and lower energy consumption.
【Fund】: 浙江省高校省级自然科学研究项目(KJ2012Z420)
【CateGory Index】: TN929.1
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