Abstract. Tobacco grading is the first step in the transfer of tobacco leaves from agricultural products to commodities and is key to determining the quality of tobacco. Manual grading is conventionally used for tobacco grading. However, it is time-consuming, expensive, and may require specialized labor. To overcome these limitations, a method for grade identification of tobacco leaves based on machine vision is proposed in this article. Based on a fuzzy pattern recognition algorithm, the tobacco leaf samples of the model set and prediction set could be classified by extracting appearance characteristics of the tobacco leaves. The identification system for tobacco leaves based on fuzzy pattern recognition was developed in MATLAB. The rate of correct grading was 85.81% and 80.23% for the modeling set and prediction set, respectively. This result shows that machine vision based automatic tobacco grading has a great advantage over manual grading, and this method can be explored for viable commercial use. Keywords: Fuzzy pattern recognition, Grade identification, Machine vision, Tobacco leaf.
This article focuses on a class of distributed security estimation for cyber-physical systems against denial-of-service attacks. First, an energy-constrained periodic denial-of-service jamming attack model and a Markov stochastic process-based switching topology model are developed. Then, Bernoulli random variables are applied to indicate whether measurement and estimation between nodes are successfully transmitted, where the data loss phenomenon is subject to denial-of-service attacks and inherent network properties. With the help of Lyapunov functions and linear matrix inequalities, sufficient conditions are established to guarantee that the error system satisfies exponentially mean-square stability and robust [Formula: see text] performance. Finally, two numerical simulation examples prove the effectiveness of the proposed distributed filtering method.
According to the facile melt blending method, a series of conductive blend composites based on polypropylene with the excellent electrical conductivityare prepared by Multi-walled carbon nanotube (MWCNT) and ionic liquid (IL). In light of the results of the FTIR and Raman, it was confirmed that the MWCNT was non-covalent modified by IL. The dispersion of MWCNT and the electrical conductivity of the composites are enhanced by the addition of a master-batch as a compatibilizer and IL as a modifier, respectively. With the adding of MWCNTs, the surface resistivity of the conductive blend composites was reduced by 4∼6 orders of magnitude under the same IL loading.
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