ABSTRACT. Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights for each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed. *Please use the following address for communications: I.N. Tansel*,

transformation neural network (WT-NN) combination with a preprocessor

Abstract: The dynamics of drilling processes presents chaotic and unsteady characteristics, which prevent deterministic description. Vibration signals obtained during the microdrilling process contain rich information reflecting tool and process conditions. Experiments described in this paper show that as drill wear develops and intensifies, the energy distribution of the vibration signal tends to shift towards the low-frequency range. Traditional frequency domain analysis through the fast Fourier transform is not able to capture such transitions with desirable accuracy since the process is highly non-stationary. We propose a new method that combines the discrete wavelet transform with statistical estimations of the signal energy distribution to extract features describing such energy shifts quantitatively. Through a multiresolution transformation, four feature parameters most sensitive to drill wear conditions are extracted. A tool wear index is proposed as a

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which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Sprague-Dawley (SD) rats ’ normal and abnormal pancreatic tissues are determined directly by attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy method. In order to diagnose earlier stage of SD rats pancreatic cancer rate with FT-IR, a novel method of extraction of FT-IR feature using discrete wavelet transformation (DWT) analysis and classification with the probability neural network (PNN) was developed. The differences between normal pancreatic and abnormal samples were identified by PNN based on the indices of 4 feature variants. When error goal was 0.01, the total correct rates of pancreatic early carcinoma and advanced carcinoma were 98 % and 100%, respectively. It was practical to apply PNN on the basis of ATR-FT-IR to identify abnormal tissues. The research result shows the feasibility of establishing the models with FT-IR-DWT-PNN method to identify normal pancreatic tissues, early carcinoma tissues, and advanced carcinoma tissues. 1.