The Experts below are selected from a list of 31755 Experts worldwide ranked by ideXlab platform
Pak Kin Wong - One of the best experts on this subject based on the ideXlab platform.
-
Automotive Engine power performance tuning under numerical and nominal data
Control Engineering Practice, 2012Co-Authors: Pak Kin Wong, Lap Mou TamAbstract:Modern Automotive Engines are controlled by an electronic control unit (ECU), and Engine power performance is significantly affected by the selection of both ECU parameters and Engine components. The Engine performance tuning is usually done by a trial-and-error method. In the current literature, very little research has considered the selection of Engine parts because Engine parts are complicated objects that are usually represented as nominal data. These data are meaningless values in terms of computation. This paper presents a novel multiple-input/output least-squares support vector machine plus one-of-n remapping method for modelling Engine power performance using both numerical (ECU parameters) and nominal data (candidate Engine parts). The Quasi-Newton method, a genetic algorithm and particle swarm optimisation are then applied to the Engine model to determine the optimal Engine setup automatically. A simple binary code synthesis rule is also proposed to optimise the nominal variable. Both experimental and simulation results show that the proposed methodology can successfully yield an optimal Engine setup.
-
signal analysis of Automotive Engine spark ignition system using case based reasoning cbr and case based maintenance cbm
PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MECHANICS AND THE 12TH INTERNATIONAL CONFERENCE ON THE ENHANCEMENT AND PROMOTION OF CO, 2010Co-Authors: H Huang, Chiman Vong, Pak Kin WongAbstract:With the development of modern technology, modern vehicles adopt electronic control system for injection and ignition. In traditional way, whenever there is any malfunctioning in an Automotive Engine, an Automotive mechanic usually performs a diagnosis in the ignition system of the Engine to check any exceptional symptoms. In this paper, we present a case‐based reasoning (CBR) approach to help solve human diagnosis problem. Nevertheless, one drawback of CBR system is that the case library will be expanded gradually after repeatedly running the system, which may cause inaccuracy and longer time for the CBR retrieval. To tackle this problem, case‐based maintenance (CBM) framework is employed so that the case library of the CBR system will be compressed by clustering to produce a set of representative cases. As a result, the performance (in retrieval accuracy and time) of the whole CBR system can be improved.
-
case based reasoning for Automotive Engine performance tune up
PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MECHANICS AND THE 12TH INTERNATIONAL CONFERENCE ON THE ENHANCEMENT AND PROMOTION OF CO, 2010Co-Authors: Chiman Vong, H Huang, Pak Kin WongAbstract:The Automotive Engine performance tune‐up is greatly affected by the calibration of its electronic control unit (ECU). The ECU calibration is traditionally done by trial‐and‐error method. This traditional method consumes a large amount of time and money because of a large number of dynamometer tests. To resolve this problem, case based reasoning (CBR) is employed, so that an existing and effective ECU setup can be adapted to fit another similar class of Engines. The adaptation procedure is done through a more sophisticated step called case‐based adaptation (CBA) [1, 2]. CBA is an effective knowledge management tool, which can interactively learn the expert adaptation knowledge. The paper briefly reviews the methodologies of CBR and CBA. Then the application to ECU calibration is described via a case study. With CBR and CBA, the efficiency of calibrating an ECU can be enhanced. A prototype system has also been developed to verify the usefulness of CBR in ECU calibration.
-
case based adaptation for Automotive Engine electronic control unit calibration
Expert Systems With Applications, 2010Co-Authors: Chiman Vong, Pak Kin WongAbstract:The Automotive Engine performance is greatly affected by the calibration of its electronic control unit (ECU). The method for ECU calibration is traditionally done by trial-and-error. This traditional method consumes a large amount of time and money. To resolve this problem, case-based reasoning (CBR) is employed, so that an existing and effective ECU setup can be adapted to fit another similar class of Engines. The adaptation procedure is done through a more sophisticated step called case-based adaptation (CBA) (Craw, Jarmulak, & Rowe, 2001; Craw, Wiratunga, & Rowe, 2006; Leake, Kinley, & Wilson, 1996, 1997). CBA is an effective knowledge management tool, which can interactively learn the expert adaptation knowledge. The paper briefly reviews the methodologies of CBR and CBA. Then the application to ECU calibration is described via a case study. With CBR and CBA, the efficiency of calibrating an ECU can be enhanced. A prototype system has also been developed to verify the usefulness of CBR in ECU calibration.
-
case based reasoning for Automotive Engine electronic control unit calibration
International Conference on Information and Automation, 2009Co-Authors: Chiman Vong, Pak Kin Wong, H HuangAbstract:The Automotive Engine performance is greatly affected by the calibration of ECU, which controls fuel injection and ignition advance over different timing. Fine tuning an Engine giving maximum performance is equivalent to calibrating the ECU of that Engine. However, the method for ECU calibration is traditionally done in a trial-and-error way. Every trial means an adjustment to the fuel and ignition maps and then run on a dynamometer to verify the Engine performance. This traditional method expenses a large amount of time and money. In order to resolve this problem, Case-based Reasoning (CBR) from artificial intelligence field is employed so that the maps of a fully calibrated ECU can be adapted to fit another similar class of Engines. This paper briefly reviews the methodology of CBR. Then the application of CBR to ECU calibration is described. By applying CBR, the efficiency of calibrating an Automotive ECU becomes higher. Furthermore, expert and novice Automotive Engineers may use this system as an assistant when calibrating an ECU. A prototype system has been developed to verify the usefulness of CBR in ECU calibration.
Su Shiung Lam - One of the best experts on this subject based on the ideXlab platform.
-
production of hydrogen and light hydrocarbons as a potential gaseous fuel from microwave heated pyrolysis of waste Automotive Engine oil
International Journal of Hydrogen Energy, 2012Co-Authors: Alan D Russell, Su Shiung Lam, Chern Leing Lee, Su Ki Lam, Howard A ChaseAbstract:Abstract Waste Automotive Engine oil was pyrolyzed in a continuous stirred bed reactor using microwave energy as the heat source; the yield and characteristics of the incondensable gaseous products are discussed. The recovered gases (41 wt% yield) were found to contain substantial concentrations of light aliphatic hydrocarbons (up to 86 vol.%) that could potentially be used as a chemical feedstock or a fuel source to power the process, or to be reformed to produce hydrogen for use as a second-generation fuel. Examination of the composition of the gases also showed the formation of H2 (up to 19 vol.%) and CO that could also be used as a valuable syngas (with a H2 + CO content of up to 35 vol.%). The high yield of gaseous hydrocarbons can be attributed to the unique heating mode and chemical environment present during microwave-heated pyrolysis. The use of a microwave-heated bed of particulate-carbon showed advantages in transforming waste oil into valuable gases. Hence an environmentally unfriendly waste material can be transformed into a useful resource and serves as an alternative source of hydrogen or hydrocarbon energy. The recovery of valuable gases shows advantage over traditional destructive approaches and suggests excellent potential for recycling problematic waste oil.
-
microwave heated pyrolysis of waste Automotive Engine oil influence of operation parameters on the yield composition and fuel properties of pyrolysis oil
Fuel, 2012Co-Authors: Alan D Russell, Su Shiung Lam, Chern Leing Lee, Howard A ChaseAbstract:Abstract The pyrolysis of waste Automotive Engine oil was investigated using microwave energy as the heat source, and the yield and characteristics of the pyrolysis oils (i.e. elemental analysis, hydrocarbon composition, and potential fuel properties) are presented and discussed. The microwave-heated pyrolysis generated an 88 wt.% yield of condensable pyrolysis oil with fuel properties (e.g. density, calorific value) comparable to traditional liquid transportation fuels derived from fossil fuel. Examination of the composition of the oils showed the formation of light aliphatic and aromatic hydrocarbons that could also be used as a chemical feedstock. The oil product showed significantly high recovery (90%) of the energy present in the waste oil, and is also relatively contaminant free with low levels of sulphur, oxygen, and toxic PAH compounds. The high yield of pyrolysis oil can be attributed to the unique heating mode and chemical environment present during microwave-heated pyrolysis. This study extends existing findings on the effects of pyrolysis process conditions on the overall yield and formation of the recovered oils, by demonstrating that feed injection rate, flow rate of purge-gas, and heating source influence the concentration and the molecular nature of the different hydrocarbons formed in the pyrolysis oils. The microwave-heated pyrolysis can be performed in a continuous operation, and the apparatus described which is fitted with magnetrons capable of delivering 5 kW of microwave power is capable of treating waste oil at a feed rate of 5 kg/h with a positive energy ratio of 8 (energy content of hydrocarbon products/electrical energy supplied for microwave heating) and a net energy output of 179,390 kJ/h. Our results indicate that microwave-heated pyrolysis shows exceptional promise as a means for recycling and treating problematic waste oil.
Howard A Chase - One of the best experts on this subject based on the ideXlab platform.
-
production of hydrogen and light hydrocarbons as a potential gaseous fuel from microwave heated pyrolysis of waste Automotive Engine oil
International Journal of Hydrogen Energy, 2012Co-Authors: Alan D Russell, Su Shiung Lam, Chern Leing Lee, Su Ki Lam, Howard A ChaseAbstract:Abstract Waste Automotive Engine oil was pyrolyzed in a continuous stirred bed reactor using microwave energy as the heat source; the yield and characteristics of the incondensable gaseous products are discussed. The recovered gases (41 wt% yield) were found to contain substantial concentrations of light aliphatic hydrocarbons (up to 86 vol.%) that could potentially be used as a chemical feedstock or a fuel source to power the process, or to be reformed to produce hydrogen for use as a second-generation fuel. Examination of the composition of the gases also showed the formation of H2 (up to 19 vol.%) and CO that could also be used as a valuable syngas (with a H2 + CO content of up to 35 vol.%). The high yield of gaseous hydrocarbons can be attributed to the unique heating mode and chemical environment present during microwave-heated pyrolysis. The use of a microwave-heated bed of particulate-carbon showed advantages in transforming waste oil into valuable gases. Hence an environmentally unfriendly waste material can be transformed into a useful resource and serves as an alternative source of hydrogen or hydrocarbon energy. The recovery of valuable gases shows advantage over traditional destructive approaches and suggests excellent potential for recycling problematic waste oil.
-
microwave heated pyrolysis of waste Automotive Engine oil influence of operation parameters on the yield composition and fuel properties of pyrolysis oil
Fuel, 2012Co-Authors: Alan D Russell, Su Shiung Lam, Chern Leing Lee, Howard A ChaseAbstract:Abstract The pyrolysis of waste Automotive Engine oil was investigated using microwave energy as the heat source, and the yield and characteristics of the pyrolysis oils (i.e. elemental analysis, hydrocarbon composition, and potential fuel properties) are presented and discussed. The microwave-heated pyrolysis generated an 88 wt.% yield of condensable pyrolysis oil with fuel properties (e.g. density, calorific value) comparable to traditional liquid transportation fuels derived from fossil fuel. Examination of the composition of the oils showed the formation of light aliphatic and aromatic hydrocarbons that could also be used as a chemical feedstock. The oil product showed significantly high recovery (90%) of the energy present in the waste oil, and is also relatively contaminant free with low levels of sulphur, oxygen, and toxic PAH compounds. The high yield of pyrolysis oil can be attributed to the unique heating mode and chemical environment present during microwave-heated pyrolysis. This study extends existing findings on the effects of pyrolysis process conditions on the overall yield and formation of the recovered oils, by demonstrating that feed injection rate, flow rate of purge-gas, and heating source influence the concentration and the molecular nature of the different hydrocarbons formed in the pyrolysis oils. The microwave-heated pyrolysis can be performed in a continuous operation, and the apparatus described which is fitted with magnetrons capable of delivering 5 kW of microwave power is capable of treating waste oil at a feed rate of 5 kg/h with a positive energy ratio of 8 (energy content of hydrocarbon products/electrical energy supplied for microwave heating) and a net energy output of 179,390 kJ/h. Our results indicate that microwave-heated pyrolysis shows exceptional promise as a means for recycling and treating problematic waste oil.
-
microwave pyrolysis a novel process for recycling waste Automotive Engine oil
Energy, 2010Co-Authors: Alan D Russell, Howard A ChaseAbstract:Used Automotive Engine oil was treated using a microwave-induced pyrolysis process, with the intention of assessing the suitability of the process in recovering valuable products from this otherwise difficult to dispose of waste. The resulting pyrolysis gases were condensed into liquid oil; the yield and composition of the recovered oil and remaining incondensable gases were determined, and these were compared with those arising from fresh oil. Process temperature was shown to have a significant effect on the overall yield and formation of the recovered oils. The recovered liquid and gaseous pyrolysis products contained various light hydrocarbons which could be used as a valuable fuel and as an industrial feedstock. Our results indicate that microwave pyrolysis shows extreme promise as a means for disposing of problematic waste oil. The recovery of commercially valuable products shows advantage over traditional, more destructive disposal methods, and suggests excellent potential for scaling the process to the commercial level.
Chiman Vong - One of the best experts on this subject based on the ideXlab platform.
-
signal analysis of Automotive Engine spark ignition system using case based reasoning cbr and case based maintenance cbm
PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MECHANICS AND THE 12TH INTERNATIONAL CONFERENCE ON THE ENHANCEMENT AND PROMOTION OF CO, 2010Co-Authors: H Huang, Chiman Vong, Pak Kin WongAbstract:With the development of modern technology, modern vehicles adopt electronic control system for injection and ignition. In traditional way, whenever there is any malfunctioning in an Automotive Engine, an Automotive mechanic usually performs a diagnosis in the ignition system of the Engine to check any exceptional symptoms. In this paper, we present a case‐based reasoning (CBR) approach to help solve human diagnosis problem. Nevertheless, one drawback of CBR system is that the case library will be expanded gradually after repeatedly running the system, which may cause inaccuracy and longer time for the CBR retrieval. To tackle this problem, case‐based maintenance (CBM) framework is employed so that the case library of the CBR system will be compressed by clustering to produce a set of representative cases. As a result, the performance (in retrieval accuracy and time) of the whole CBR system can be improved.
-
case based reasoning for Automotive Engine performance tune up
PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL MECHANICS AND THE 12TH INTERNATIONAL CONFERENCE ON THE ENHANCEMENT AND PROMOTION OF CO, 2010Co-Authors: Chiman Vong, H Huang, Pak Kin WongAbstract:The Automotive Engine performance tune‐up is greatly affected by the calibration of its electronic control unit (ECU). The ECU calibration is traditionally done by trial‐and‐error method. This traditional method consumes a large amount of time and money because of a large number of dynamometer tests. To resolve this problem, case based reasoning (CBR) is employed, so that an existing and effective ECU setup can be adapted to fit another similar class of Engines. The adaptation procedure is done through a more sophisticated step called case‐based adaptation (CBA) [1, 2]. CBA is an effective knowledge management tool, which can interactively learn the expert adaptation knowledge. The paper briefly reviews the methodologies of CBR and CBA. Then the application to ECU calibration is described via a case study. With CBR and CBA, the efficiency of calibrating an ECU can be enhanced. A prototype system has also been developed to verify the usefulness of CBR in ECU calibration.
-
case based adaptation for Automotive Engine electronic control unit calibration
Expert Systems With Applications, 2010Co-Authors: Chiman Vong, Pak Kin WongAbstract:The Automotive Engine performance is greatly affected by the calibration of its electronic control unit (ECU). The method for ECU calibration is traditionally done by trial-and-error. This traditional method consumes a large amount of time and money. To resolve this problem, case-based reasoning (CBR) is employed, so that an existing and effective ECU setup can be adapted to fit another similar class of Engines. The adaptation procedure is done through a more sophisticated step called case-based adaptation (CBA) (Craw, Jarmulak, & Rowe, 2001; Craw, Wiratunga, & Rowe, 2006; Leake, Kinley, & Wilson, 1996, 1997). CBA is an effective knowledge management tool, which can interactively learn the expert adaptation knowledge. The paper briefly reviews the methodologies of CBR and CBA. Then the application to ECU calibration is described via a case study. With CBR and CBA, the efficiency of calibrating an ECU can be enhanced. A prototype system has also been developed to verify the usefulness of CBR in ECU calibration.
-
case based reasoning for Automotive Engine electronic control unit calibration
International Conference on Information and Automation, 2009Co-Authors: Chiman Vong, Pak Kin Wong, H HuangAbstract:The Automotive Engine performance is greatly affected by the calibration of ECU, which controls fuel injection and ignition advance over different timing. Fine tuning an Engine giving maximum performance is equivalent to calibrating the ECU of that Engine. However, the method for ECU calibration is traditionally done in a trial-and-error way. Every trial means an adjustment to the fuel and ignition maps and then run on a dynamometer to verify the Engine performance. This traditional method expenses a large amount of time and money. In order to resolve this problem, Case-based Reasoning (CBR) from artificial intelligence field is employed so that the maps of a fully calibrated ECU can be adapted to fit another similar class of Engines. This paper briefly reviews the methodology of CBR. Then the application of CBR to ECU calibration is described. By applying CBR, the efficiency of calibrating an Automotive ECU becomes higher. Furthermore, expert and novice Automotive Engineers may use this system as an assistant when calibrating an ECU. A prototype system has been developed to verify the usefulness of CBR in ECU calibration.
-
prediction of Automotive Engine power and torque using least squares support vector machines and bayesian inference
Engineering Applications of Artificial Intelligence, 2006Co-Authors: Chiman Vong, Pak Kin WongAbstract:Automotive Engine power and torque are significantly affected with effective tune-up. Current practice of Engine tune-up relies on the experience of the Automotive Engineer. The Engine tune-up is usually done by trial-and-error method, and then the vehicle Engine is run on the dynamometer to show the actual Engine output power and torque. Obviously, the current practice costs a large amount of time and money, and may even fail to tune up the Engine optimally because a formal power and torque model of the Engine has not been determined yet. With an emerging technique, least squares support vector machines (LS-SVM), the approximated power and torque model of a vehicle Engine can be determined by training the sample data acquired from the dynamometer. The number of dynamometer tests for an Engine tune-up can therefore be reduced because the estimated Engine power and torque functions can replace the dynamometer tests to a certain extent. Besides, Bayesian framework is also applied to infer the hyperparameters used in LS-SVM so as to eliminate the work of cross-validation, and this leads to a significant reduction in training time. In this paper, the construction, validation and accuracy of the functions are discussed. The study shows that the predicted results using the estimated model from LS-SVM are good agreement with the actual test results. To illustrate the significance of the LS-SVM methodology, the results are also compared with that regressed using a multilayer feed forward neural networks.
Lee Albert Feldkamp - One of the best experts on this subject based on the ideXlab platform.
-
rbf network feedforward compensation of load disturbance in idle speed control
IEEE Control Systems Magazine, 1996Co-Authors: Dimitry Gorinevsky, Lee Albert FeldkampAbstract:Automotive Engine idle speed control is a disturbance rejection problem. An Engine at idle is typically well away from its most favorable region of operation and exhibits significant nonlinearities. Control of such a system is complicated by delays of both physical (time between induction and power strokes) and computational origin. In the model used, there are two control variables, and these differ in both their range of effectiveness and their temporal characteristics: spark advance is fast-acting but limited in its effect, while throttle has a large range but a slower effect which results both from the dynamics of filling the intake manifold and from the induction-power delay. The spark variable also has a maximum effective value, i.e., a value beyond which it has an effect opposite to that expected. This article describes a nonlinear adaptive feedforward controller for compensation of external load disturbances in the idle speed control of an Automotive Engine. The controller is based on a radial basis function (RBF) network approximation of certain input-output mappings describing the system. An underlying assumption used in the controller design is that the external Engine load is known to the controller. In particular, that might be achieved by putting an appropriate torque sensor in the powertrain or using other available information.
-
Automotive Engine idle speed control with recurrent neural networks
American Control Conference, 1993Co-Authors: Gintaras Vince Puskorius, Lee Albert FeldkampAbstract:This paper describes the development of recurrent neural network controllers for an Automotive Engine idle speed control (ISC) problem. Engine ISC is a difficult problem because of troublesome characteristics such as severe process nonlinearities, variable time delays, time-varying process dynamics and unobservable system states and disturbances. We demonstrate that recurrent neural network controllers can be trained to handle these difficulties gracefully while achieving good regulator performance for a representative model of 4-cylinder, 1.6 liter Engine. Empirical results clearly illustrate that neural network controllers with relatively large amounts of internal feedback provide more robust performance for the ISC problem than do neural network controllers that are static or contain limited internal recurrent connections.
