According to previous reports software clones are considered harmful for software maintenance. Likewise, model clones are problematic in model-based development. It is significant to detect model clones in software models. In this paper, we present a novel optimized path-based model clone detection algorithm (OPMCD). It first builds paths from block graphs, and then identifies clone instances from the common subsequence of paths. Moreover, an experiment is designed to evaluate the algorithm through comparing with the state-of-the-art of model clone detection algorithm ConQAT model clone detection (CMCD). The experiment result illustrates that OPMCD has better performance in terms of efficiency, and it is practically suitable for large-scale MATLAB/Simulink models.

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In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance.

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