Xiaodong Zhuang
Technical University of Sofia
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Featured researches published by Xiaodong Zhuang.
international conference on mathematics and computers in sciences and in industry | 2014
Hui Zhu; Nikos E. Mastorakis; Xiaodong Zhuang
This paper provides a way of skin detection in outdoor image based on multiple color space. The clustering is good in the color space YCgCr. Firstly, skin colors are projected in the color space CgCr and the fitting of distribution is carried through in order to wipe off a part of non skin color and gain the intersected image as the result of the first detection. Experimental results indicate that this fitting of distribution can have a good effect on reducing a mass of processing pixels of non skin color. Secondly, skin colors extracted in the first detection are projected in the color space GB in order to further wipe off part of the remaining non skin colors that are not reduced in the first detection by the fitting of distribution. Lastly, the relationships among the three components of every pixel of skin colors and non skin colors in the color space HSL are observed and the percentage of pixels corresponding to a certain relationship is calculated, so part of the non skin color can be further reduced based on the differences we find according to the observed relationship. Experimental results indicate that this algorithm has a good recognition effect and small amount of computation, it can be used in skin color detection in simple environment.
international conference on mathematics and computers in sciences and in industry | 2016
Mingyue Ding; Jinxiao Huang; Jiazhen Li; Nikos E. Mastorakis; Xiaodong Zhuang
With the intelligent development of the power system, load identification becomes an important task. A noninvasive household load identification method is proposed in this paper, which can be used to detect the load switching moments and identify the types of loads. A calculation method of feature value is proposed based on active power. A template method is proposed to detect the jumping of the feature curve, which can determine the load switching moments robustly and accurately. The types of loads can be identified by a threshold value method. The results show that the method can accurately determine the load switching moments and identify the types of loads, and it also has good robustness.
arXiv: Computer Vision and Pattern Recognition | 2016
Xiaodong Zhuang; Nikos E. Mastorakis
WSEAS Transactions on Computers archive | 2009
Xiaodong Zhuang; Nikos E. Mastorakis
MATEC Web of Conferences | 2018
Xiaodong Zhuang; Nikos Mastorakis
MATEC Web of Conferences | 2018
Zhenyan Fan; Qianqian Chen; Guiqi Sun; Nikos Mastorakis; Xiaodong Zhuang
international conference on mathematics and computers in sciences and in industry | 2017
Guiqi Sun; Zhenyan Fan; Nikos E. Mastorakis; Stavros D. Kaminaris; Xiaodong Zhuang
international conference on mathematics and computers in sciences and in industry | 2017
Xiaodong Zhuang; Nikos E. Mastorakis; Stavros D. Kaminaris; Jieru Chi; Hanping Wang
ITM Web of Conferences | 2017
Xiaodong Zhuang; Nikos Mastorakis; Mingyue Ding
2017 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) | 2017
Mingyue Ding; Guiqi Sun; Nikos E. Mastorakis; Xiaodong Zhuang