This paper deals with the problem of clustering daily wind speed time series based on two features referred to as W_r and H, representing a measure of the relative daily average wind speed and the Hurst exponent, resp...
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ISBN:
(纸本)9781479999835
This paper deals with the problem of clustering daily wind speed time series based on two features referred to as W_r and H, representing a measure of the relative daily average wind speed and the Hurst exponent, respectively. Daily values of the pairs (W_r, H) are first classified by means of the fuzzy c-means unsupervised clustering algorithm and then results are used to train a supervised MLP neural network classifier. It is shown that associating to a true wind speed time series a time series of classes, allows performing some useful statistics. Further, the problem of predicting 1-step ahead the class of daily wind speed is addressed by introducing NAR sigmoidal neural models into the classification process. The performance of the prediction model is finally assessed.
In the diagnosis and operation of the brain,there are five kinds of main tissues,such as gray matter,white matter,cerebrospinal fluid,scalp and skull,those need to be segmented from the MRI to sequel accurate three-di...
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In the diagnosis and operation of the brain,there are five kinds of main tissues,such as gray matter,white matter,cerebrospinal fluid,scalp and skull,those need to be segmented from the MRI to sequel accurate three-dimensional head *** at this problem,this paper proposes a segmentation method combining fcm & VBM *** the craniocerebral regions were extracted from the original MRI images by the BET algorithm,the brain regions were subdivided to obtain gray matter,white matter and cerebrospinal fluid by fcm ***,the skull,scalp and brain were segmented by VBM segmentation *** then the smooth and morphological treatment of the separated tissues was carried ***,five kinds of tissues were *** with K-means clustering algorithm and morphological segmentation method,it is found that the segmentation algorithm has lower morphological distortion in the case of higher edge gradient.
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