By Fu Lee Wang, Hepu Deng, Jingsheng Lei
The 2010 overseas convention on synthetic Intelligence and Computational Intelligence (AICI 2010) was once held October 23–24, 2010 in Sanya, China. The AICI 2010 acquired 1,216 submissions from 20 nations and areas. After rigorous stories, a hundred and five high quality papers have been chosen for booklet within the AICI 2010 lawsuits. The reputation expense was once 8%. the purpose of AICI 2010 used to be to collect researchers operating in lots of diversified parts of man-made intelligence and computational intelligence to foster the trade of latest rules and advertise overseas collaborations. as well as the big variety of submitted papers and invited periods, there have been numerous the world over recognized keynote audio system. On behalf of the Organizing Committee, we thank Hainan Province Institute of desktop and Qiongzhou college for its sponsorship and logistics help. We additionally thank the participants of the Organizing Committee and this system Committee for his or her labor. we're very thankful to the keynote audio system, invited consultation organizers, consultation chairs, reviewers, and scholar helpers. final yet no longer least, we thank the entire authors and contributors for his or her nice contributions that made this convention attainable.
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Extra resources for Artificial Intelligence and Computational Intelligence, Part I
If the “Zoom- Ratio” equals to 1, the “Zoom In” operation is disabled; and when we have a zr of W / Ws , it is just the “Fit-in” case, the “Zoom Out” operation is disabled. Different from the “Fit-In” operation where we extract every W / Ws lines and pixels, we extract every zr lines from the original images and every zr pixels from each of the extracted line from the beginning in the “Zoom-In/Out” operation, the extraction process stops when 14 Y. Liu et al. Input Open the image in binary Lines=0 Need Decoding?
Paper  analyzes the satisfiability of a proposition. The calculus of satisfiability degree is proposed, which expresses uncertainty, extends the concepts of satisfactory and contradictory propositions in propositional logic and describes how satisfactory is based on the proportion of interpretations that make the proposition true. In this paper we want to provide a new sight of model checking to deal with the uncertain situation. In the real world, may sometimes the system can not satisfy the property totally, but it can support partially.
Towards principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies 43(5/6), 907–928 (1995) 9. : Ontologies. IEEE Intelligent Systems, 18–19 (January/February 1999) 10. , pp. 20–25 (January/February 1999) 11. : Formalizing the Modeling Process of Physical Systems in MBD. , Lei, J. ) AICI 2009. LNCS, vol. 5855, pp. 685–695. cn Abstract. Classical model checking is not capable of solving the situation of uncertain systems or fault-tolerant systems which occur in the real world commonly.
Artificial Intelligence and Computational Intelligence, Part I by Fu Lee Wang, Hepu Deng, Jingsheng Lei