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:...
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...
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 +...
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...
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...
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...
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...
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...
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...
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...