Soft Computing in Industrial Applications: Recent Trends by Pieter J. Mosterman, Elisabeth M. O’Brien (auth.), Ashraf

By Pieter J. Mosterman, Elisabeth M. O’Brien (auth.), Ashraf Saad, Keshav Dahal, Muhammad Sarfraz, Rajkumar Roy (eds.)

Soft Computing admits approximate reasoning, imprecision, uncertainty and partial fact so that it will mimic facets of the outstanding human power of constructing judgements in real-life and ambiguous environments. "Soft Computing in business functions" features a selection of papers that have been offered on the 11th online international convention on gentle Computing in commercial functions, held in September-October 2006. This conscientiously edited ebook offers a accomplished assessment of the hot advances within the commercial functions of soppy computing and covers a variety of program parts, together with facts research and knowledge mining, special effects, clever keep watch over, platforms, development acceptance, classifiers, in addition to modeling optimization. The ebook is geared toward researchers and practitioners who're engaged in constructing and utilizing clever platforms rules to fixing real-world difficulties. it's also compatible as wider interpreting for technology and engineering postgraduate students.

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3. (a) User hand image. (b) Back-projected image (color cue). (c) Motion gesture. (d) Image differencing (motion cue). As a result of the color cue we have an intensity image pk, representing the skin color probability at frame k, and a second BW image used as a motion indicator фk, obtained from the motion cue at frame k. At frame k, Ik is the fused intensity image [16] according to (1). 3 for best performance), and α is a motion assessment variable which increases and decreases for large small amounts of motion.

Kennedy, R. Eberhart, Particle swarm optimization, Proc. IEEE Intl. Conf. Neural Networks, 4, Nov/Dec 1995, pp. 1942 –1948. 19. R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, Proc. the Sixth Intl. Symposium on Micro Machine and Human Science, MHS '95, 4-6 Oct 1995, pp. 39 -43. 20. Y. Shi, R. Eberhart, A modified particle swarm optimizer, The 1998 IEEE Intl. Conf. , IEEE World Congress on Computational Intelligence, 4-9 May 1998, pp. 69 – 73. 21. C. Eberhart, Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization, Proceedings of the 2000 Congress on Evolutionary Computation, Vol.

The recognition system is tested by generating the test objects by translating, rotating, and scaling and adding noise to the model objects contained in a database of size 60. The test objects were randomly rotated, scaled and translated. Sixty test objects were used for each of the experiments for testing similarity transformation, 16 test objects were used for noisy objects with similarity transformations, and 60 test objects were used for occluded objects. The salt & pepper noise of density 10% is added to the objects for generating the noisy test objects.

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