[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: Data transformation is a kind of data preprocessing [1, 3, 5] and an important procedure for mathematical modelling. Mathematical model estimated based on a training data set results better if the data set has been properly preprocessed before passed to the modelling procedure. In the paper, different preprocessing methods on automotive engine data are examined. The preprocessed data sets using different preprocessing methods are passed to neural networks for models estimation. The generalizations of these estimated models could be verified by applying test sets, which determine the effects of different preprocessing methods. The results of preprocessing methods for automotive engine data are shown in the paper.
[Show abstract][Hide abstract] ABSTRACT: Adaptation knowledge is usually the most important and difficult stage in building a case-based intelligent system. Existing intelligent systems for hydraulic system design use production rules as its source of knowledge. However, this leads to problems of knowledge acquisition for case adaptation and knowledge base maintenance. This paper describes the application of CBR to hydraulic circuit design for production machines, which helps acquiring adaptation knowledge and solving problems by reusing this acquired knowledge (experience). A technique Case-Based Adaptation (CBA) is implemented in the adaptation stage of CBR so that adaptation becomes much easier. The details of CBA and its implementation are discussed. A prototype system has been developed to verify the usefulness of CBR in hydraulic power machine design.