In traffic accident, an accurate and timely severity prediction method is necessary for the successful deployment of an intelligent transportation system to provide corresponding levels of medical aid and transportation in a timely manner. The existing traffic accident's severity prediction methods mainly use shallow severity prediction models and statistical models. To promote the prediction accuracy, a novel traffic accident's severity prediction-convolutional neural network (TASP-CNN) model for traffic accident's severity prediction is proposed that considers combination relationships among traffic accident's features. Based on the weights of traffic accident's features, the feature matrix to gray image (FM2GI) algorithm is proposed to convert a single feature relationship of traffic accident's data into gray images containing combination relationships in parallel as the input variables for the model. Moreover, experiments demonstrated that the proposed model for traffic accident's severity prediction has a better performance.
The problem of secure distributed storage systems (DSS) with regenerating codes is concerned in this paper. We consider an eavesdropper model where an eavesdropper wiretaps a subset of storage nodes, and either their repairing data or stored data can be wiretapped. We focus on two typical and special cases, the Minimum Bandwidth Repair (MBR) and the Minimum Storage Repair (MSR). Our main contribution is to draw a connection between this problem and secure network coding theory introduced by Cai and Yeung, and the secrecy capacity can be determined in this method. We prove that for both MBR and MSR cases, if the maximal wiretapped information rate can be determined, the secrecy capacity can be achieved by linear secure network coding. Particularly, a static exact regenerating code can be transformed into a secure regenerating code for the MBR and MSR cases.
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