Computational Intelligence in Automotive Applications Episode 1 Part 5 docx

... Computer Vision and Machine Learning for Enhancing Pedestrian Safety 69 0 1 2 3 4 5 0 .5 1 1 .5 2 2 .5 3 3 .5 4 4 .5 decision boundary −2 1 0 1 2 −2 1. 5 1 −0 .5 0 0 .5 1 1 .5 2 (a) (b) Fig. 8. Illustration ... Machine Intelligence, pp. 918 –923, July 2003. 31. D.V. Prokhorov. Neural Networks in Automotive Applications. Computational Intelligence in Automo...

Ngày tải lên: 07/08/2014, 09:23

... 11 8 References 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: A...

Ngày tải lên: 07/08/2014, 09:23

... 2.0) F 01 <= -1. 56 3 F 15 <= 0. 017 4 -> class High [0.788] Rule 1/ 8: (11 .5/ 3 .1, lift 1. 8) F 15 > 0. 017 4 F 21 > 0.06 35 F28 <= 1. 25 -> class High [0.699] Rule 1/ 9: ( 45. 2, ... lift 1. 2) F09 > 21. 2443 F 15 > 0.0 056 F 15 <= 0. 017 4 F 21 > 0.06 35 -> class Low [0.979] Rule 1/ 10: (92 .1/ 1 .5, lift 1. 2) F 01...

Ngày tải lên: 07/08/2014, 09:23

... (s) 1 394 (two intervals: 18 0 + 214 ) 51 6 910 2 90 (one interval) 210 300 3 0 240 240 4 15 5 (one interval) 17 5 330 5 16 0 (one interval) 393 55 3 6 18 0 (one interval) 370 55 0 7 310 (two intervals: 15 0 ... 55 0 7 310 (two intervals: 15 0 + 16 0) 6 31 9 41 8 842 (two intervals: 390 + 452 ) 7 65 1, 607 9 210 (two intervals: 75 + 13 5) 255 4 65 10 673 (two inte...

Ngày tải lên: 07/08/2014, 09:23

... ra9 57 .39 steeringError 57 .32 aheadLaneBearing rd5 ra9 55 .33 aheadLeneBearing 51 . 85 crossLaneVelocity 50 .55 Stat3 of steeringError crossLaneVelocity distToLeftLaneEdge aheadLaneBearing 38.07 crossLaneVelocity rv9 ... 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 dri...

Ngày tải lên: 07/08/2014, 09:23

... 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, 2) 236 0. 85 Tool in (2,4) 240 0. 81 Tool in (1, 4) 244 0. 81 Location in ... constrain the manufacturing process to a particular subset of in uence variables. Neural Networks in Automotive Applications 10 5 1. 5 1. 6 1. 7 1....

Ngày tải lên: 07/08/2014, 09:23

... Automotive Applications 11 7 0 50 10 0 15 0 200 250 300 350 400 1 −0 .5 0 0 .5 1 0 50 10 0 15 0 200 250 300 350 400 1 −0 .5 0 0 .5 1 Testing Training Fig. 11 . The RNN results after training. The segment from ... 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...

Ngày tải lên: 07/08/2014, 09:23

... 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 0.6 0.8 1 0 10 20 30 N e = 10 00 rp m OF =0 ◦ CA/m OF =0. 41 ◦ CA/m OF =1. 16 ◦ CA/m N e = ... and Control in Spark Ignition Automotive Engines, Studies in Computational Intelligence (SCI) 13 2, 14 5 16 8 (2008) www.springerlink.com c...

Ngày tải lên: 07/08/2014, 09:23

... Welding, Proceedings of 2006 IEEE World Congress of Computational Intelligence, 2006 IEEE International Conference on Fuzzy Systems, Vancouver, 15 70 15 77, 2006. 16 . D. Dickinson, J. Franklin, ... International Joint Conf. on Neural Networks I, pp. 54 5 55 0 San Diego, 19 90. 10 . N. T. Williams and J. D. Parker, Review of resistance spot welding of steel sheets Part 2 – Factors...

Ngày tải lên: 07/08/2014, 09:23

... 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 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...

Ngày tải lên: 07/08/2014, 09:23