Computational Intelligence in Automotive Applications Episode 1 Part 6 doc

Computational Intelligence in Automotive Applications Episode 1 Part 6 doc

Computational Intelligence in Automotive Applications Episode 1 Part 6 doc

... Location 6. 0092 1 1 Right 6. 008 4 2 Right 6. 00 61 4 2 Right 6. 0 067 1 2 Left 6. 00 76 4 1 Right 6. 0082 2 2 Left 6. 0075 3 1 Right 6. 0077 3 2 Right 6. 00 61 2 1 Left 6. 0 063 1 1 Right 6. 0 063 1 2 Right improve ... 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 in (right) 210 0.84 Tool in (1,...

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Computational Intelligence in Automotive Applications Episode 1 Part 4 doc

Computational Intelligence in Automotive Applications Episode 1 Part 4 doc

... transcriptions in real time. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP88), pp. 61 1 – 61 4 , New York City, USA, April 11 14 , 19 88. 28. K. ... controls 74% Eating a meal/snack 51% Using a cell phone 41% Tending to children 34% Reading a map/publication 19 % Grooming 11 % Prepared for work Activities drivers engage in w...

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Computational Intelligence in Automotive Applications Episode 1 Part 5 docx

Computational Intelligence in Automotive Applications Episode 1 Part 5 docx

... Machine Intelligence, pp. 918 –923, July 2003. 31. D.V. Prokhorov. Neural Networks in Automotive Applications. Computational Intelligence in Automotive Applications, Studies in Computational Intelligence, ... 10 (6) :15 31 15 36, 19 99. 40. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86...

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Computational Intelligence in Automotive Applications Episode 2 Part 6 doc

Computational Intelligence in Automotive Applications Episode 2 Part 6 doc

... Objects-of-Interest table to generate a set of goal paths for the vehicle that meets the control values specified in the table. GP 113 GP 114 GP 115 GP 1 16 GP 117 Vehicle’s Goal Paths - Fig. 14 . Elemental ... al. 2 Dynamic Trajectories built from Goal Paths. GP 113 GP 114 GP 117 GP 1 16 GP 115 Fig. 15 . Primitive/Trajectory control module pre-calculates (at 10 0× real-time) t...

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Computational Intelligence in Automotive Applications Episode 1 Part 1 pptx

Computational Intelligence in Automotive Applications Episode 1 Part 1 pptx

... 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 6 6 VerificationandValidation (V &V) 11 8 References ... . . . . . . . . . 15 5 6 ExperimentalSet-Up 15 5 6 .1 Training andTestData 15 6 7 Results 16 2 7 .1 FRNNM:AFRPrediction 16 2 7.2 IRNNM:AFRContro...

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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 > 3.5 26 F 21 <= 0. 063 5 -> class High [0.8 71] Rule 1/ 2: (13 .9...

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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 + 214 ) 5 16 910 2 90 (one interval) 210 300 3 0 240 240 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 ... 550 7 310 (two intervals: 15 0 + 16 0) 6 31 9 41 8 842 (two intervals: 390 + 452) 765 1, 60 7 9 210 (two intervals: 75 + 13 5) 255 465 10 67 3 (two intervals: 310...

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Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx

Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx

... vol. 17 , no. 6, pp. 16 06 16 16, 20 06. 10 2. H. Jaeger and H. Haas, “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunications,” Science, vol. 308, no. 566 7, ... et al.: On Learning Machines for Engine Control, Studies in Computational Intelligence (SCI) 13 2, 12 5 14 4 (2008) www.springerlink.com c  Springer-Verlag Berlin Heidelbe...

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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 ... and Control in Spark Ignition Automotive Engines, Studies in Computational Intelligence (SCI) 13 2, 14 5 16 8 (2008) www.springerlink.com c  Spr...

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Computational Intelligence in Automotive Applications Episode 2 Part 1 docx

Computational Intelligence in Automotive Applications Episode 2 Part 1 docx

... 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 1) 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19 20 Time [s] measured predicted 5 5.5 6 6.5 7 7.5 8 12 13 14 15 16 17 18 19...

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