Abstract
Specifically for quantify and extract meridian information, a comprehensive multi-acupoint feature index was given. The feature parameters of single acupoint were extracted and reconstructed based on AR parameter model. Then feature weight was obtained by objective weighting method and feature matrix was weighted. The ideal feature vector was built based on orthogonal transformation of eigenvalues in meridian feature space. Based on PCP, the distance between each feature vector and the ideal model vector was calculated, and the projection value of fixed-weighted feature matrix on ideal feature vector was obtained. The simulation results show that the method can be more stability and higher around 3 % in the recognition rate than the main acupoint in human multi-acupoint system. The same results also show that the recognition rates can be coincided with sort results.
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Qin, J., Yu, C. (2013). Comprehensive Feature Index for Meridian Information Based on Principal Component Projection. In: Wong, W.E., Ma, T. (eds) Emerging Technologies for Information Systems, Computing, and Management. Lecture Notes in Electrical Engineering, vol 236. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7010-6_5
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DOI: https://doi.org/10.1007/978-1-4614-7010-6_5
Publisher Name: Springer, New York, NY
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