Empirical prediction of traffic noise transmission loss across plenum windows

A parametric study on the traffic noise transmission loss across plenum windows was carried out experimentally in this investigation in an attempt to establish a simple empirical model for predicting this transmission loss. A total of fourteen full scale plenum windows were included in this study. The results of a site mockup measurement were used for model validation. The present model was developed based on the existing plenum chamber theory in which the sound fields inside the plenum window cavities were assumed to make up of a diffracted wave and a reverberant field. Results suggest that both the diffracted and reverberant field inside the plenum window cavities are weaker than those assumed in existing plenum chamber theory. It is found that a model, which assumes frequency-independent diffraction directivity and percentage reverberant field attenuation, gives the best prediction of traffic noise transmission loss. This prediction model is also able to predict site test results with good accurac

Flow-based Influence Graph Visual Summarization

Visually mining a large influence graph is appealing yet challenging. People are amazed by pictures of newscasting graph on Twitter, engaged by hidden citation networks in academics, nevertheless often troubled by the unpleasant readability of the underlying visualization. Existing summarization methods enhance the graph visualization with blocked views, but have adverse effect on the latent influence structure. How can we visually summarize a large graph to maximize influence flows? In particular, how can we illustrate the impact of an individual node through the summarization? Can we maintain the appealing graph metaphor while preserving both the overall influence pattern and fine readability? To answer these questions, we first formally define the influence graph summarization problem. Second, we propose an end-to-end framework to solve the new problem. Our method can not only highlight the flow-based influence patterns in the visual summarization, but also inherently support rich graph attributes. Last, we present a theoretic analysis and report our experiment results. Both evidences demonstrate that our framework can effectively approximate the proposed influence graph summarization objective while outperforming previous methods in a typical scenario of visually mining academic citation networks.Comment: to appear in IEEE International Conference on Data Mining (ICDM), Shen Zhen, China, December 201