By Dušan Krokavec, Anna Filasová, Vratislav Hladký (auth.), Ladislav Madarász, Jozef Živčák (eds.)
This quantity covers the state-of-the paintings of the study and improvement in numerous elements of computational intelligence and offers a few point of view instructions of improvement. other than the normal engineering parts that comprise theoretical wisdom, purposes, designs and initiatives, the booklet comprises the realm of use of computational intelligence in biomedical engineering. „Aspects of Computational Intelligence: thought and purposes” is a compilation of conscientiously chosen prolonged papers written at the foundation of unique contributions offered on the 15th IEEE overseas convention on Intelligence Engineering structures 2011, INES 2011 held at June 23.-26. 2011 in AquaCity Poprad, Slovakia.
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Additional resources for Aspects of Computational Intelligence: Theory and Applications: Revised and Selected Papers of the 15th IEEE International Conference on Intelligent Engineering Systems 2011, INES 2011
The conclusions are pointed out in Section 5. 2 3D Crane System Models The following nonlinear state-space equations of the process in 3D crane systems are obtained if no disturbance are considered and zero initial conditions are considered for all state variables except x1 [25, 26]: Lead-Lag Controller-Based Iterative Learning Control Algorithms 27 x1 = x2 , x 2 = −T1 x2 − Tsy sgn( x2 ) − μ1 cos( x5 )[−T3 x10 − Tsz sgn( x10 )] + k1u1 + k3 μ1 cos( x5 )u3 , x3 = x4 , x 4 = −T2 x4 − Tsx sgn( x4 ) − μ 2 sin( x5 ) sin( x7 )[−T3 x10 − Tsz sgn( x10 )] + k 2u 2 + k3 μ 2 sin( x5 ) sin( x7 )u3 , x5 = x6 , x 6 = −[T1 x2 − Tsy sgn( x2 )] sin( x5 ) / x9 + sin( x3 ) cos( x5 ) x82 / x9 + cos( x5 ) cos( x7 )[k1 sin( x5 )u1 − k 2 cos( x5 ) sin( x7 )u2 − k3 μ 2 sin( x5 ) ⋅ cos( x5 ) sin 2 ( x7 )u3 + k3 μ1 sin( x5 ) cos( x5 )u3 ] / x92 + μ 2 sin( x5 ) cos( x5 ) ⋅ sin 2 ( x7 )[−T3 x10 − Tsz sgn( x10 )] / x9 + cos( x5 ) sin( x7 )[T2 x4 + Tsx sgn( x4 )] / x9 − μ1 sin( x5 ) cos( x5 )[−T3 x10 − Tsz sgn( x10 )] / x9 − 2 x6 x10 / x9 , x 7 = x8 , x8 = k 2 sin( x7 ) cos( x7 )u2 /[ x92 sin 2 ( x5 )] − k3 μ1μ 2 sin 2 ( x7 ) cos( x7 ) ⋅ u3 /[ x92 sin( x5 )] + μ 2 sin( x7 ) cos( x7 )[−T3 x10 − Tsz sgn( x10 )] / x9 − 2 x8 x10 / x9 + cos( x7 )[T2 x4 + Tsx sgn( x4 )] /[ x9 sin( x5 )], x9 = x10 , , (1) x10 = cos( x5 )[T1 x2 + Tsy sgn( x2 )] + x x sin ( x5 ) − k1 sin( x5 ) cos( x5 ) 2 8 9 2 ⋅ cos( x7 )u1 − k 2 sin 2 ( x5 ) sin( x7 ) cos( x7 )u 2 + k3 sin( x5 ) cos( x7 )[−μ 2 ⋅ sin 2 ( x5 ) sin 2 ( x7 ) − μ1 cos 2 ( x5 ) − 1]u3 + μ 2 sin 2 ( x5 ) sin 2 ( x7 )[−T3 x10 − Tsz sgn( x10 )] + sin( x5 ) sin( x7 )[T2 x4 + Tsx sgn( x4 )] + μ1[−T3 x10 − Tsz ⋅ sgn( x10 )] + μ1 sin 2 ( x5 )[−T3 x10 − Tsz sgn( x10 )] + x62 x9 − T3 x10 − Tsz sgn( x10 ), where the state variables are x1 – the distance of the cart from the centre of the rail, x10 – the initial condition for x1 , x2 – the speed of the cart on the direction of x1 , x3 – the distance of the rail with the cart from the centre of the construction frame, x4 – the speed of the rail with the cart on the direction of x3 , x5 – the acute angle between the lift-line of the payload and the rail, x6 – the angular speed that corresponds to x5 , x7 – the acute angle between the lift-line of the payload and the vertical line, x8 – the angular speed that corresponds to x7 , x9 – the length of the lift-line, and x10 – the speed of the lift-line.
7. Detecting the centre of a line using Gabor filter From the above figure it can be seen that the zero crossing of phase signal is the appropriate feature to find the line centre. Based on the detected lines centres, it is necessary to fit a straight line. In order to eliminate the effected of the outlier data points an M-estimator is used. An other way is, to calculate the centre of “gravity” of the gray values of the individual pixels: Algorithms for Pitch Distance Determination rcentre = 45 g i r i, j i, j j g i i, j j where r is the distance from the origin of the coordinate system and gi,j-s are the gray scales values of the different pixels.
Fig. 3. Pitch distance definitions 42 G. Hermann Whereas the first two versions given in the picture can be measured directly, the third one requires more processing. The difficulty is generated by line thickness variation and the straightness error of the edges. When a bright line on a black background is viewed on sample-level, column by column, theoretically the pattern obtained is an ideal step-shaped waveform. In real-life, this waveform is not that ideal, but the line will be a bit more blurred.