Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
KPCA denoising and the pre-image problem revisited
Digital Signal Processing
Multiscale texture classification using dual-tree complex wavelet transform
Pattern Recognition Letters
Concepts for novelty detection and handling based on a case-based reasoning process scheme
Engineering Applications of Artificial Intelligence
A Study and Application on Machine Learning of Artificial Intellligence
JCAI '09 Proceedings of the 2009 International Joint Conference on Artificial Intelligence
Machine learning for event selection in high energy physics
Engineering Applications of Artificial Intelligence
The global kernel k-means algorithm for clustering in feature space
IEEE Transactions on Neural Networks
Case-based adaptation for automotive engine electronic control unit calibration
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
CBR methodology application in an expert system for aided design ship's engine room automation
Expert Systems with Applications: An International Journal
A cluster-based wavelet feature extraction method and its application
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Full length article: Emerging applications of wavelets: A review
Physical Communication
Embedded holonic fault diagnosis of complex transportation systems
Engineering Applications of Artificial Intelligence
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Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.