Computational Intelligence in Automotive Applications Episode 1 Part 2 pdf

Computational Intelligence in Automotive Applications Episode 1 Part 2 pdf

Computational Intelligence in Automotive Applications Episode 1 Part 2 pdf

... F09-F10) 32 70 Pupil-diameter features only (F09-F10) 2 61 Driving-performance features (F 01- F08) 8 60 PleaserefertoTable2forthefeatureindices 0 2 4 6 8 10 12 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 Feature ... the features 12 Y. Zhang et al. Rule 1/ 1: ( 41. 4/4.6, lift 2. 2) F10 > 3. 526 F 21 <= 0.0635 -> class High [0.8 71] Rule 1/ 2:...

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Computational Intelligence in Automotive Applications Episode 1 Part 1 pptx

Computational Intelligence in Automotive Applications Episode 1 Part 1 pptx

... 11 9 On Learning Machines for Engine Control G´erard Bloch, Fabien Lauer, and Guillaume Colin 12 5 1 Introduction 12 5 1. 1 CommonFeaturesin EngineControl 12 5 1. 2 NeuralNetworksinEngineControl 12 6 1. 3 ... Networks in Automotive Applications Danil Prokhorov 10 1 1 Models 10 1 2 VirtualSensors 10 3 3 Controllers 10 6 4 TrainingNN 11 1 5 RNN: AMotivatingExample 11...

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Computational Intelligence in Automotive Applications Episode 1 Part 3 ppt

Computational Intelligence in Automotive Applications Episode 1 Part 3 ppt

... (s) 1 394 (two intervals: 18 0 + 21 4 ) 516 910 2 90 (one interval) 21 0 300 3 0 24 0 24 0 4 15 5 (one interval) 17 5 330 5 16 0 (one interval) 393 553 6 18 0 (one interval) 370 550 7 310 (two intervals: 15 0 ... 310 (two intervals: 15 0 + 16 0) 6 31 9 41 8 8 42 (two intervals: 390 + 4 52) 765 1, 607 9 21 0 (two intervals: 75 + 13 5) 25 5 465 10 673 (two intervals: 310 +...

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Computational Intelligence in Automotive Applications Episode 1 Part 4 doc

Computational Intelligence in Automotive Applications Episode 1 Part 4 doc

... on Artificial Intel ligence, Montreal, Canada, 19 95. 11 . I. Guyon and A. Elisseeff. An introduction to feature selection. Journal of Machine Learning Research, 3 :11 57– 11 82, 20 03. 12 . I. Guyon, ... as: ⎛ ⎝ X b Y b Z b ⎞ ⎠ = λ ⎛ ⎝ h 11 h 12 h 13 h 21 h 32 h 33 h 31 h 32 1 ⎞ ⎠ ⎛ ⎝ X a Y a Z a ⎞ ⎠ (4) The perspective camera coordinates can be mapped to pixel coordina...

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Computational Intelligence in Automotive Applications Episode 1 Part 5 docx

Computational Intelligence in Automotive Applications Episode 1 Part 5 docx

... they are related by the following equations: u 2 = h 11 u 1 + h 12 v 1 + h 13 h 31 u 1 + h 32 v 1 + h 33 ,v 2 = h 21 u 1 + h 22 v 1 + h 23 h 31 u 1 + h 32 v 1 + h 33 (17 ) The matrix H formed from ... Machine Intelligence, pp. 918 – 923 , July 20 03. 31. D.V. Prokhorov. Neural Networks in Automotive Applications. Computational Intelligence...

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Computational Intelligence in Automotive Applications Episode 1 Part 6 doc

Computational Intelligence in Automotive Applications Episode 1 Part 6 doc

... location in (right) 12 7 0.83 Tool in (2, 3) and shaft in (2) 13 0 0.83 Tool in (1, 4) and location in (right) 13 3 0.83 Tool in (4) 18 2 0. 81 Tool not in (4) and shaft in (2) 18 3 0. 81 Tool not in (1) and ... Location 6.00 92 1 1 Right 6.008 4 2 Right 6.00 61 4 2 Right 6.0067 1 2 Left 6.0076 4 1 Right 6.00 82 2 2 Left 6.0075 3 1 Right 6.0077...

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Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx

Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx

... Applications 11 7 0 50 10 0 15 0 20 0 25 0 300 350 400 1 −0.5 0 0.5 1 0 50 10 0 15 0 20 0 25 0 300 350 400 1 −0.5 0 0.5 1 Testing Training Fig. 11 . The RNN results after training. The segment from 0 to 20 0 is ... et al.: On Learning Machines for Engine Control, Studies in Computational Intelligence (SCI) 13 2, 12 5 14 4 (20 08) www.springerlink.com c  Springer...

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Computational Intelligence in Automotive Applications Episode 1 Part 8 potx

Computational Intelligence in Automotive Applications Episode 1 Part 8 potx

... with the inputs of the experimental data as shown in Fig. 10 for N =3. 0.4 0.6 0.8 1 0 10 20 30 0.4 0.6 0.8 1 0 10 20 30 0.4 0.6 0.8 1 0 10 20 30 0.4 0.6 0.8 1 0 10 20 30 0.4 0.6 0.8 1 0 10 20 30 0.4 ... 2 71 27 6, 20 02. 24 . I. Kolmanovsky. Support vector machine-based determination of gasoline direct-injected engine admissible operating envelope. SAE Technic...

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Computational Intelligence in Automotive Applications Episode 2 Part 1 docx

Computational Intelligence in Automotive Applications Episode 2 Part 1 docx

... 2) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 Time [s] AFR [/] (Case 3) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 21 Time [s] AFR [/] (Case 4) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 Time ... - (Case 2) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 Time [s] AFR [/] (Case 3) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19...

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Computational Intelligence in Automotive Applications Episode 2 Part 2 ppt

Computational Intelligence in Automotive Applications Episode 2 Part 2 ppt

... rad/s 314 rad/s 2 61 rad/s 10 4 rad/s 15 7 rad/s 20 9 rad/s (a) engine efficiency map 0 0.5 1 1.5 2 2.5 0 0.5 1 1.5 2 2.5 Mechanical Power[kW ] Electrical Power[kW] Alternator Map ( 14 V- 2kW ) 52 rad/s 10 4 ... Murphey: Intelligent Vehicle Power Management: An Overview, Studies in Computational Intelligence (SCI) 13 2, 16 9 19 0 (20 08) www.springerlink.com c  S...

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