Abstract-In this paper we consider how to efficiently identify tags on the moving conveyor. Considering conditions like the path loss and multi-path effect in realistic settings, we first propose a probabilistic model for RFID tag identification. Based on this model, we propose efficient solutions to identify moving RFID tags, according to the fixed-path mobility on the conveyor. A dynamic program based solution and an adaptive solution are proposed to select optimized frame sizes during the query cycles. Simulation results indicate that by leveraging the probabilistic model our solutions can achieve much better performance than using parameters for the ideal propagation situations.
Software fault prediction is a valuable exercise in software quality assurance to best allocate limited testing resources. Classification is one of the effective methods for software fault prediction. The classification models are trained based on the datasets obtained by mining software historical repositories. However, the performance of the models depends on the quality of datasets. In this paper, we propose a novel two-stage data preprocessing approach which incorporates both feature selection and instance reduction. Specifically, in the feature selection stage, we first perform relevance analysis, and then propose a threshold-based clustering method, called novel threshold-based clustering algorithm, to conduct redundancy control. In the instance reduction stage, we apply random under-sampling to keep the balance between the faulty and non-faulty instances. In empirical studies, we chose datasets from real-world software projects, such as Eclipse and NASA. Then we compared our approach with some classical baseline methods, and further investigated the influencing factors in our approach. The final results demonstrate the effectiveness of our approach, and provide a guideline for achieving cost-effective data preprocessing when using our two-stage approach.
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