11timing belt drives in automotive applications
Tài liệu Using Simulink and Stateflow in Automotive Applicationsl doc
... Simulink and Stateflow today The applications and models described in this booklet include the following examples using Simulink alone: I Engine Model engine.mdl — open-loop simulation enginewc.mdl ... Moskwa, "Automotive Engine Modeling for Real-Time Control Using Matlab/Simulink," 1995 SAE Intl Cong paper 950417 USING S IMULINK AND S TATEFLOW IN A UTOMOTIVE A PPLICATIONS 17 II ANTI-LOCK BRAKING ... to rad/s Proportional Gain Ki T z -1 Integral Gain N limit output Throttle Setting Discrete Time Integrator integrator input enable integration controller output prevent windup Figure 1.5: Speed...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_1 pot
... hand, engaging in thinking (the so-called minds-off-road phenomenon) is difficult to detect Since the cognitive workload level is internal to the driver, it can only be inferred based on the information ... use Cover design: Deblik, Berlin, Germany Printed on acid-free paper springer.com Computational Intelligence in Automotive Applications Preface What is computational intelligence (CI)? Traditionally, ... the in- cylinder air mass in which open loop neural estimators are combined with a dynamic polytopic observer, and (2) modeling an in- cylinder residual gas fraction by a linear programming support...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_5 pptx
... and 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, ... Models in the Automotive Industry, Studies in Computational Intelligence (SCI) 132, 79–88 (2008) c Springer-Verlag Berlin Heidelberg 2008 www.springerlink.com 80 M Steinbrecher et al underlying ... scheme for forming a strong classifier using a linear combination of a number of weak classifiers based on individual features [36, 37] Every weak classifier is individually trained on a single feature...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_6 pptx
... Madsen Predicting Parts Demand in the Automotive Industry – An Application of Probabilistic Graphical Models In Proceedings of International Joint Conference on Uncertainty in Artificial Intelligence ... group of intelligent methods is concerned with discovering interesting information in large data sets This discipline is generally referred to as Knowledge Discovery or Data Mining In the automotive ... 0.83 Tool in (2,4) and location in (left) Shaft in (2) and location in (right) Tool in (2,3) and shaft in (2) Tool in (1,4) and location in (right) Tool in (4) Tool not in (4) and shaft in (2) Tool...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_7 pot
... with off-line training which assumes that the plant (its model in this case) can be reset to any specified state at any time On-line training can be done in a straightforward way by maintaining two ... “Toward effective combination of off-line and on-line training in ADP framework,” in Proceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL), Symposium ... based on a gradient obtained for every instance in the training set, hence the term instantaneous gradient In batch mode, the index i is no longer applicable to individual instances, and it becomes...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_10 ppt
... Es (k))∆t (7) By incorporating Es (k), the current energy storage in the battery, into λ dynamically, we are able to avoid draining or overcharging the battery during the driving cycle The dynamically ... using the driving cycles described in the SAE J1711 standard The operating points of the engine, EM, and battery were either close to the optimal curve or in the optimal range [46] 3.2 An Intelligent ... controller T line1 is the function representing the threshold shown in Fig 7a, and T line2 is the function representing the threshold shown in Fig 7b Lin et al used the Urban Dynamometer Driving Schedule...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_11 doc
... system by integrating the individual diagnostic elements of design for testability, on-board diagnostics, automatic testing, manual troubleshooting, training, maintenance aiding, technical information, ... modeled using a realistic engine model in CRAMAS, and are injected via the GUI in CRAMAS host PC Faults F6–F9 injected through the CRAMAS include: An Integrated Diagnostic Process for Automotive ... can be manually injected in the prototype AIS The leakage fault is injected by adjusting the hole size in the pipe, while the dirty air filter fault is injected by blocking the opening of the pipe...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_12 doc
... the automotive industry to increasingly use zinc coated steel in auto body construction One of the major concerns associated with welding coated steel is the mushrooming effect (the increase in ... Manufacturing: Intelligent Resistance Welding, Studies in Computational Intelligence (SCI) 132, 219–235 (2008) c Springer-Verlag Berlin Heidelberg 2008 www.springerlink.com 220 M El-Banna et al causing ... architecture during training The weighted voting follows the optimum voting rules for binary classifiers [44] (3) Dynamic fusion: Dynamic fusion architecture, combining ECOC and dynamic inference algorithm...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_14 ppt
... conservative), and intent (stopping at intersection, turning right, asserting right-of-way, followingmotion-vector, moving-randomly, etc.) Additionally, this module’s world model contains road and intersection ... the beginning of its cycle, reads its inputs, does some planning, writes its outputs and starts the cycle again Processes communicate using the Neutral Message Language (NML) in a non-blocking mode, ... horizon is the line determined by the points where the line of sight of the cameras stops intersecting the ground plane Once the algorithm is running, the algorithm randomly samples pixels in the current...
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Computational Intelligence in Automotive Applications by Danil Prokhorov_15 pdf
... example accomplishments in this area include: determining the high impact areas according to the AGV industry, partnering with an AGV vendor to demonstrate pallets visualization using LADAR towards ... performance test methods and data, and infrastructure technology needed by US manufacturing industry and government agencies in developing and applying intelligent control technology to mobility ... deeper into these projects with even more autonomous capabilities Broader applications to robots supporting humans in manufacturing, construction, and farming are expected once major key intelligent...
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Computational Intelligence in Automotive Applications pot
... date [19] Machine learning has found increasing applicability in fields as varied as banking, medicine, marketing, condition monitoring, computer vision, and robotics [20] Machine learning technology ... feature vectors) = Test = Train Segmentlevel training Subjectlevel training Fig Allocation of time windows or segments to training and testing sets for two training objectives Table The test ... can be increased by using more training data AdaBoost has been validated in a large number of classification applications See5 incorporates AdaBoost as a training option We utilized boosting with...
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Computational Intelligence in Automotive Applications Episode 1 Part 1 pptx
... hand, engaging in thinking (the so-called minds-off-road phenomenon) is difficult to detect Since the cognitive workload level is internal to the driver, it can only be inferred based on the information ... use Cover design: Deblik, Berlin, Germany Printed on acid-free paper springer.com Computational Intelligence in Automotive Applications Preface What is computational intelligence (CI)? Traditionally, ... the in- cylinder air mass in which open loop neural estimators are combined with a dynamic polytopic observer, and (2) modeling an in- cylinder residual gas fraction by a linear programming support...
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Computational Intelligence in Automotive Applications Episode 1 Part 2 pdf
... feature vectors) = Test = Train Segmentlevel training Subjectlevel training Fig Allocation of time windows or segments to training and testing sets for two training objectives Table The test ... can be increased by using more training data AdaBoost has been validated in a large number of classification applications See5 incorporates AdaBoost as a training option We utilized boosting with ... driver-dependent training strategy is adopted to evaluate and address this issue In driver-dependent training strategy, the data from a single subject was divided into disjoint training and test sets...
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Computational Intelligence in Automotive Applications Episode 1 Part 3 ppt
... collecting and constructing a database of naturalistic driving data in the driving simulator We concentrate on the machine learning aspect; making the database usable as the basis for learning driving/driver ... Eye closure duration Blink freq Nodding freq Face position Fixed gaze DIL In In In In In In Out [0.0, 1.0] [1.0–30.0] [1.0–30.0] [0.0–8.0] [0.0–1.0] [0.0–0.5] [0.0–1.0] 3 5 Linguistic terms Small, ... time (s) 10 394 (two intervals: 90 (one interval) 155 (one interval) 160 (one interval) 180 (one interval) 310 (two intervals: 842 (two intervals: 210 (two intervals: 673 (two intervals: 180 + 214)...
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Computational Intelligence in Automotive Applications Episode 1 Part 4 doc
... by filling gaps and providing more complete information about the surroundings Looking in the vehicle at driver’s state is as important as looking out in surroundings in order to convey warnings ... determining the driving maneuvers Different combinations of sensor variables are identified for different driving maneuvers For example, steering wheel angle is important for determining the turning ... well as the steering error entropy, that may indicate driver inattention during a common driving task, such as looking in the “blind spot.” Experimental Setup We designed the following procedure...
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Computational Intelligence in Automotive Applications Episode 1 Part 5 docx
... and 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, ... Models in the Automotive Industry, Studies in Computational Intelligence (SCI) 132, 79–88 (2008) c Springer-Verlag Berlin Heidelberg 2008 www.springerlink.com 80 M Steinbrecher et al underlying ... scheme for forming a strong classifier using a linear combination of a number of weak classifiers based on individual features [36, 37] Every weak classifier is individually trained on a single feature...
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Computational Intelligence in Automotive Applications Episode 1 Part 6 doc
... Madsen Predicting Parts Demand in the Automotive Industry – An Application of Probabilistic Graphical Models In Proceedings of International Joint Conference on Uncertainty in Artificial Intelligence ... group of intelligent methods is concerned with discovering interesting information in large data sets This discipline is generally referred to as Knowledge Discovery or Data Mining In the automotive ... 0.83 Tool in (2,4) and location in (left) Shaft in (2) and location in (right) Tool in (2,3) and shaft in (2) Tool in (1,4) and location in (right) Tool in (4) Tool not in (4) and shaft in (2) Tool...
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Computational Intelligence in Automotive Applications Episode 1 Part 7 pptx
... with off-line training which assumes that the plant (its model in this case) can be reset to any specified state at any time On-line training can be done in a straightforward way by maintaining two ... “Toward effective combination of off-line and on-line training in ADP framework,” in Proceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL), Symposium ... based on a gradient obtained for every instance in the training set, hence the term instantaneous gradient In batch mode, the index i is no longer applicable to individual instances, and it becomes...
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Computational Intelligence in Automotive Applications Episode 1 Part 8 potx
... the engine supervisor of Fig by determining the air mass set points Computing the Manifold Pressure Set Points To obtain the desired torque of a SI engine, the air mass trapped in the cylinder ... Colin, and Y Chamaillard Combining experimental data and physical simulation models in support vector learning In L Iliadis and K Margaritis, editors, Proc of the 10th Int Conf on Engineering Applications ... emissions and increasing engine fuel economy As a result of this stringent legislation, the automotive engine technology has experienced continuous improvements in many areas In the field of Engine Control...
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Computational Intelligence in Automotive Applications Episode 2 Part 1 docx
... implementation Artificial Intelligence in Engineering 13: 55–68 24 Inagaki H, Ohata A, Inoue T (1990) An adaptive fuel injection control with internal model in automotive engines In: IECON ’90, 16th ... could occur They include injection actuation, lack of synchronization between injection and intake valve timing, unsteadiness of gas flowing through the measuring section and mixing in the pipe with ... most informative data In the field of Internal Combustion Engine (ICE), these techniques search in the independent variables domain (i.e the engine operating conditions) for those experimental input–output...
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