Computational Intelligence in Automotive Applications Episode 2 Part 1 docx

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...

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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 26 1 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) 1 32, 16 9 19 0 (20 08) www.springerlink.com c  S...

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Computational Intelligence in Automotive Applications Episode 2 Part 3 ppsx

Computational Intelligence in Automotive Applications Episode 2 Part 3 ppsx

... 0 1 0 0 0 1 F5 1 0 0 0 0 0 0 0 F6 0 0 0 1 1 1 1 0 F7 0 0 0 1 0 0 0 0 F8 0 0 0 0 0 0 0 1 F9 0 1 0 1 0 0 0 1 less fuel injection ( 10 %), added engine friction ( +10 %), air/fuel sensor fault ( 10 %), ... faults in the CRAMAS engine model Table 2 . Diagnostic matrix of the engine system Fault\test R1 R2 hR2lR3R4R5R6hR6l F0 0 0 0 0 0 0 0 0 F1 0 1 0 0 0 0 0 0 F2 0 0 1...

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Computational Intelligence in Automotive Applications Episode 2 Part 4 pptx

Computational Intelligence in Automotive Applications Episode 2 Part 4 pptx

... 8 .2 ± 2. 5 12 . 8 ± 2. 11 4 .1 ± 2. 12 1 .1 ± 3.733 .1 ± 3 . 21 6.3 ± 2. 3 data via classification MPLS Tandem (serial) 15 .87 ± 2. 49 ( 12 . 8 KB) fusion Fusion center (parallel) 14 . 81 ± 3.46 Majority voting 12 . 06 ... in air intake system (F2) 10 .11 +0.76 −0 .20 −0. 72 11 .22 +0.75 Blockage of air filter (F3) −75.55 +6. 42 +1. 37 +0.75 −44 .20 +6.38 Throttle angle sensor...

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Computational Intelligence in Automotive Applications Episode 2 Part 5 pdf

Computational Intelligence in Automotive Applications Episode 2 Part 5 pdf

... Welding, Proceedings of 20 06 IEEE World Congress of Computational Intelligence, 20 06 IEEE International Conference on Fuzzy Systems, Vancouver, 15 70 15 77, 20 06. 16 . D. Dickinson, J. Franklin, ... with ∗ Corresponding author, roger.bostelman@nist.gov J. Albus et al.: Intelligent Control of Mobility Systems, Studies in Computational Intelligence (SCI) 1 32, 23 7 27 4 (20...

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Computational Intelligence in Automotive Applications Episode 2 Part 6 doc

Computational Intelligence in Automotive Applications Episode 2 Part 6 doc

... (right) 25 4 J. Albus et al. 2 Dynamic Trajectories built from Goal Paths. GP 113 GP 114 GP 117 GP 116 GP 115 Fig. 15 . Primitive/Trajectory control module pre-calculates (at 10 0× real-time) the set of ... 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 116 GP 117 Vehicle’...

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Computational Intelligence in Automotive Applications Episode 2 Part 7 potx

Computational Intelligence in Automotive Applications Episode 2 Part 7 potx

... 18 5 controller, 10 6, 10 8, 11 0, 1 12 in engine control, 12 6 models, 10 3, 11 6, 12 8 Neuro-fuzzy inference system, 22 2 Nodding, 20 , 29 Observer, 10 3, 12 7 , 1 32 13 5, 13 7, 14 7, 14 9, 19 5 polytopic, 12 5 , 13 4, 13 6 Output ... Neural Network (RNN), 10 1, 1 02, 10 5, 10 9, 11 1, 11 6, 14 6, 14 9 15 1, 15 3, 15 5, 16 5 Remaining useful life, 19...

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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. 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 6 6...

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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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