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Dynamic Fusion Algorithm of Building Surface Data in Heterogeneous Environment
Mobile Networks and Applications ( IF 3.8 ) Pub Date : 2020-11-05 , DOI: 10.1007/s11036-020-01677-2
Jing Zhu , Jing Gao

The existing building surface data fusion algorithms do not extract the segmented data features, resulting in inaccurate fusion results. In heterogeneous environment, a Clustering Fusion Algorithm Based on mutual information and fractal dimension is proposed. The regression coefficient is used to express the sequence, and the data feature representation and data dimension reduction are realized. The dynamic data series are processed by similarity measure function method. For the long dynamic data series, the piecewise aggregation approximation method is used to segment the data and then extract the features. Through the incremental clustering processing data based on fractal dimension clustering algorithm, the research of data fusion algorithm is realized. The experimental results show that the accuracy of building surface data fusion is greatly improved by using the dynamic data fusion algorithm, the highest is 0.98, the sum of square error is reduced, and the lowest is only 90.44.



中文翻译:

异构环境中建筑物表面数据的动态融合算法

现有的建筑物表面数据融合算法无法提取分段数据特征,从而导致融合结果不准确。在异构环境下,提出了一种基于互信息和分形维数的聚类融合算法。利用回归系数表示序列,实现了数据特征表示和数据降维。动态数据序列通过相似性度量函数方法进行处理。对于长动态数据序列,使用分段聚合逼近方法对数据进行分段,然后提取特征。通过基于分形维数聚类算法的增量聚类处理数据,实现了数据融合算法的研究。

更新日期:2020-11-05
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