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Open access

Minghan Yang, Xuedong Gao and Ling Li

Abstract

Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.

Open access

Danping Jia, Ximeng Gao and Chunhua Li

Abstract

In the paper presented the temperature of an oil-immersed transformer was measured, based on the principles of the fluorescence afterglow life. Three methods were used to calculate the fluorescence afterglow life τ by using the least squares method, the integral area ratio method and Prony algorithm. The leastsquare method, the integral area ratio method and the program of Prony algorithm are written using Matlab and C++. The Least-square fitting is susceptible to the influence of the DC component. When the DC location is different, the fluorescence afterglow life τ values vary widely. The integral area ratio method is not influenced by DC component, but it has low sensitivity. Prony algorithm is not affected by DC, it has high sensitivity. So Prony algorithm is selected as a way to obtain the fluorescence afterglow lifetime value τ.