Computational Intelligence in Automotive Applications Episode 1 Part 5 docx
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...
Computational Intelligence in Automotive Applications Episode 1 Part 1 pptx
... 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...
Computational Intelligence in Automotive Applications Episode 1 Part 2 pdf
... 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...
Computational Intelligence in Automotive Applications Episode 1 Part 3 ppt
... (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...
Computational Intelligence in Automotive Applications Episode 1 Part 4 doc
... 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...
Computational Intelligence in Automotive Applications Episode 1 Part 6 doc
... 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....
Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx
... 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...
Computational Intelligence in Automotive Applications Episode 1 Part 8 potx
... 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...
Computational Intelligence in Automotive Applications Episode 2 Part 5 pdf
... 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...
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 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...
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