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This paper presents a new method for fuel-type identification by combining the joint probability density arbiter and soft-computing techniques. Extensive flame features were extracted both in the time and frequency domains from each flame oscillation signal and formed an original feature data vector. Orthogonal and dimension-reduced feature data were obtained by using the principal component analysis...
A new approach for on-line identification of new coal type as well as existing coal types by combining the principal component analysis (PCA) technique and the joint probability density arbiter is presented. The flame features were extracted in the time domain and frequency domain from each flame oscillation signal and formed an original feature vector. The principal component analysis technique was...
This paper presents a new approach for on-line identification of fuel type by combining the joint probability density arbiter and support vector machine techniques. The flame features are extracted both in the time domain and frequency domain from each flame oscillation signal and form an original feature vector. Orthogonal and dimension-reduced features are obtained by using the principal component...
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