As a new type of electrical contact material, Ag/SnO2 has poor processing performance and large contact resistance, which limits its application so far. In order to improve the machinability and electrical performance of the Ag/SnO2 electrical contact materials, a new kind of nanoAg/SnO2 electrical contact material doped rare earth element Ce was prepared by sol-gel-chemical plating method. The purity of the powders was analyzed by X-ray diffraction (XRD) and the crystallite size of the nanoparticle was calculated according to the Scherrer equation. The distribution of Ce-doped SnO2 powers were studied using scanning electron microscopy (SEM). In parallel, rated making and breaking experiments on nanoAg/SnO2 were conducted. The results of XRD and SEM show that the nanoSnO2 powders are small, uniform and with no obvious phenomenon of reunion, and thus significantly improve the density, strength and machinability of the sample. Furthermore, the results of arc erosion show that the nanoAg/SnO2 electricity contact materials doped element Ce have superior fusion welding resistance properties.
Generally, the test data of the low voltage switchgear are very few. In order to assess the reliability more accurately, historical data are used to maximum the capacity of the test data. The premise of using the prior information is the prior data and test data should approximately come from the same overall. This is just the problem of compatibility check. In this paper, the method of fitting data and the law of testing the distribution are used to analyze both the priori and test data. Furthermore, we present a novel method of using Wilcoxon rank sum test to check the compatibility of the prior and test data of the low voltage switchgear. Both methods lead to the conclusion that the prior and the test data of the low voltage switchgear are compatible.
The energy functional of the CV and LBF model is single, which makes the curve to get into the local minimum easily during the evolution process, and results inaccurate segmentation of the images with nonuniform grayscale and nonsmooth edges. The proposed algorithm, which is based on local entropy fitting under the constraint of nonconvex regularization term, is used to deal with such problems. In this algorithm, global information and local entropy are fitted to avoid segmentation falling into local optimum, and nonconvex regularization term is imported for constraint to protect edge smoothing. First, global information is used to evolve the approximate contour curve of the target segmentation. Then, a local energy functional with local entropy information is constructed to avoid the segmentation process from falling into a local minimum, and to precisely segment the image. Finally, nonconvex regularization terms are used in the energy functional to protect the smoothness of edge information during image segmentation process. The experimental results clearly indicate that the new algorithm can effectively resist noise, precisely segment images with nonuniform grayscale, and achieve the global optimal.
With crystalline silicon solar battery industry is developing rapidly, there are scientific significance and application value for guiding the industrial production using analysis of the electrical properties of crystalline silicon solar battery. This paper studies that the main parameters of monocrystalline crystal silicon solar battery: the junction depth and superficial concentrations influence on electrical characteristics of monocrystalline silicon solar battery. The result shows that for maximum efficiency, it is bound to get the largest possible open circuit voltage, short circuit current and fill factor of the product, therefore, it is necessary to control the junction depth and doping parameters. If the junction depth is constant, with the increased superficial doping concentration of monocrystalline silicon solar battery, the photoelectric conversion efficiency of the battery increases slowly at first and then rapidly decreases, and the deeper the junction depth is, the more obvious trend of the photoelectric conversion efficiency is. If the superficial doping concentration is constant, the photoelectric conversion efficiency of the battery is increased with the reductive junction depth of surface of monocrystalline silicon solar battery.
Reliability Assessment of low-voltage switchgear is of great significance to the safety of power system and its equipment. In this thesis, Bayes theory is introduced into the reliability Assessment of low-voltage switchgear. The main failure modes and failure rate of low-voltage switchgear has been summarized, and the fault arrangement diagram of low voltage switchgear is given in this paper. In view of the low-voltage switchgear with high reliability and relatively large number of samples, a feasible compatibility test method is proposed. Firstly, the distribution type can be tested through engineering experience and Goodness of fit test for weibull distribution. Then a prior information compatibility test is carried out by Wilcoxon rank sum test and Kolmogorov-Smirnov nonparametric test method. Finally, the prior information which is incompatible with credible site information is removed and reliable prior information is obtained. This paper lays a solid foundation for the Low-voltage switchgear reliability assessment based on Bayes theory.
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