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End date: 1999Subject: search.filters.subject.Neural network

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    Spatial-based feature extraction for estimating radial wind speed in Doppler radar spectra
    (1998-12-01)
    Thitimajshima, Punya
    ;
    Rangsanseri, Yuttapong
    The power density spectra of wind profiler are usually contaminated by persistent ground clutter. The radial wind speed estimated in such spectral data can produce a considerable error if each spectrum is processed independently. For the clutter-elimination purpose, we propose an algorithm for estimating radial wind speed that takes into account a spatial relationship within range-gated spectra. The candidates for the atmospheric echo in each spectrum are firstly determined by detecting all local power density maxima in the smoothed and normalized spectra. The spatial relationship is then exploited by linking those local maxima across gates, with respect to a continuity criterion. A number of features can be extracted from each candidate and a neural network is subsequently used to identify the local maximum most likely reflecting the radial atmospheric velocity.
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    Classification of UV-Vis spectroscopic data using principal component analysis and neural network techniques
    (1998-01-01)
    Benjathapanun, N.
    ;
    Boyle, W. J.O.
    ;
    Grattan, K. T.V.
    This paper presents a comparative study of the use of principal component analysis (PCA) and neural network methods to determine the nature of species present in multicomponent mixtures from ultraviolet-visible (UV-Vis) absorption spectral data. With the use of the PCA technique, absorption spectra with a 316-dimensional space are reduced to six principal components and then classified using the K nearest-neighbor method. By contrast, with the neural network technique, absorption spectra are transformed to represent the spectral shape information by binary encoding segments of the second derivative of the absorption spectra and then classifying using a back propagation neural network algorithm. It is found that the neural network method offers better performance with a higher accuracy than use of the PCA method. © 1998 Elsevier Science Ltd. All rights reserved.
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    Intelligent UV-Vis Spectrometry for Water and Environment Monitoring using GUI Software and Neural Networks
    (1996-01-01)
    Benjathapanun, N.
    ;
    Boyle, W. J.O.
    ;
    Grattan, K. T.V.
    This paper describes an Intelligent UV-Vis Spectrometry system using GUI software and neural network techniques for the identification and estimation of UV absorbing chemical species in water.