By Yongquan Zhou, Yanlian Du, Zhengxin Huang (auth.), De-Shuang Huang, Martin McGinnity, Laurent Heutte, Xiao-Ping Zhang (eds.)
The foreign convention on clever Computing (ICIC) was once shaped to supply an annual discussion board devoted to the rising and demanding subject matters in synthetic intel- gence, laptop studying, development acceptance, photo processing, bioinformatics, and computational biology. It goals to assemble researchers and practitioners from either academia and to proportion principles, difficulties, and options regarding the m- tifaceted points of clever computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the sixth - ternational convention on clever Computing. It equipped upon the good fortune of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005, that have been held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This 12 months, the convention centred in general at the theories and methodologies in addition to the rising purposes of clever computing. Its objective was once to unify the image of up to date clever computing suggestions as an necessary idea that highlights the developments in complex computational intelligence and bridges theoretical study with purposes. accordingly, the subject for this convention used to be “Advanced clever Computing know-how and Applications.” Papers targeting this subject matter have been solicited, addressing theories, methodologies, and purposes in technology and technology.
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Extra resources for Advanced Intelligent Computing Theories and Applications: 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings
Thus, we proposed a combination of AdaBoost and Random Forests to improve the performance of Face Detection. 4 Experimental Results and Analysis The training sample set comes from the MIT-CBCL database, which includes 2429 face images and 4554 non-face images, and the database contains face images of variable quality, different facial expressions and a wide range of lighting conditions. Each image of the set has scaled and cropped to a size 64×64 pixels. 1429 face images and 3000 non-face images of MIT-CBCL database are used as training samples.
In the following figures the X-axis denotes the time and the Y-axis denotes the output voltage by ECRH. According to the experiments, Table 1 gives out the detailed data for comparison. Fig. 5. Tracing control using CMAC Fig. 6. Tracing control with PID 14 P. Du and X. Luo Table 1. 75 193 268 420 From the comparisons above it can be seen that the intelligent method proposed in this paper can trace the voltage much more quickly and the overshoot is much smaller so the control system in this paper is more stable and has faster speed.
Table 1. 652s 30 11 12 From Table 1, we can see AdBoostRF can achieve lower classification errors than AdaBoost. 14% than AdaBoost, even higher than AdaBoostSVM. In addition, the number of false detection is decreased to 11 compared with AdaBoost, even lower than AdaBoostSVM. From these data, we can easily find that there is a great improvement in detection performance after we use AdBoostRF. From Figure 3, we can also know AdBoostRF has the best detection performance among three algorithms. Fig.