As a kind of powerful anti-counterfeiting device, diffractive optically variable image (DOVI) has been developed and widely used in information security field. However, the identification of DOVI today by bare eyes is not reliable. In this paper we investigate the recognition of DOVI with machine learning method, and five kinds of algorithms, namely quadratic discriminate analysis (QDA), linear discriminate analysis (LDA), regularized discriminate analysis (RDA), leave-one-out covariance matrix estimate (LOOC), and Kullback-Leibler information measure based method (KLIM) are applied to the recognition of DOVI. Considering both time cost and correct classification rate, KLIM classifier exceeds others.
In previous studies on thermal performance of solid-liquid phase change thermal energy storage (TES), it could be found that the lower phase change material (PCM) of TES was difficult to melt due to the influence of natural convection. Therefore, many scholars improved the overall thermal performance by enhancing the heat transfer in the lower region, increasing the distribution of the lower fins, increasing the length of the lower fins, or changing the porosity of the lower metal foam to improve the lower heat transfer. However, it had not been found that by changing the ratio of the upper and lower PCM to improve the heat storage performance. Therefore, this paper designed ten cases to study the effect of different ratios of the upper and lower PCM on the heat storage performance and finally found that the case 2 performed best, and the full melting time is 17240 s, reduced 39.05% compared with 28290 s of case 6.
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