Biomedical subjects

Fu-Chao Wu

Publications and source records attributed to Fu-Chao Wu.

4 recordsLinked to original sources

PubMed2006

[A method for auto-extraction of spectral lines based on convolution type of wavelet packet transformation].

The important astrophysical information is hidden in spectral lines of astronomical spectra. The presen paper presents a method for auto-extraction of spectral lines based on convolution type of wavelet packet. This method consists of four main steps: First, the observed spectra are transformed by convolution type of wavelet packet with 4th scale. Then, the noise with coefficients of the 4th scale is eliminated by the local correlation algorithm and threshold in the wavelet packet domain. After that, middle and high frequency coefficients are selected to reconstruct the feature of the spectral lines. Finally, with the reconstructed feature of the spectral lines, spectral lines in observed spectra are searched. The results of our experiments, which include the spectral lines of stars, normal galaxies and active galaxies, show that the method can robustly and accurately extract the spectral lines. The method was applied to extract the SDSS spectral lines and compute the redshifts with those lines. By comparing the redshifts with those given by SDSS, the extraction has proven successful and practical.

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PubMed2006

[Automated recognition of quasars based on adaptive radial basis function neural networks].

Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space. At first, the K-means clustering is used for the initialization, then based on the sum of squares errors and a gradient descent optimization technique, the number of neurons in the hidden layer is adaptively increased to improve the recognition performance; (3) The quasar spectra recognition is effectively carried out by the above trained RBFN. The author's proposed adaptive RBFN is shown to be able to not only overcome the difficulty of selecting the number of neurons in hidden layer of the traditional RBFN algorithm, but also increase the stability and accuracy of recognition of quasars. Besides, the proposed method is particularly useful for automatic voluminous spectra processing produced from a large-scale sky survey project, such as our LAMOST, due to its efficiency.

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PubMed2006

[A method for obtaining redshifts of quasars based on wavelet multi-scaling feature matching].

The LAMOST project, the world's largest sky survey project being implemented in China, is expected to obtain 10(5) quasar spectra. The main objective of the present article is to explore methods that can be used to estimate the redshifts of quasar spectra from LAMOST. Firstly, the features of the broad emission lines are extracted from the quasar spectra to overcome the disadvantage of low signal-to-noise ratio. Then the redshifts of quasar spectra can be estimated by using the multi-scaling feature matching. The experiment with the 15, 715 quasars from the SDSS DR2 shows that the correct rate of redshift estimated by the method is 95.13% within an error range of 0. 02. This method was designed to obtain the redshifts of quasar spectra with relative flux and a low signal-to-noise ratio, which is applicable to the LAMOST data and helps to study quasars and the large-scale structure of the universe etc.

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PubMed2005

[Non-parameter estimation algorithm to determine stellar effective temperature].

The effective temperature of a star is one of the most important parameters, which determine the continuum and spectral lines in the stellar spectrum. A non-parameter estimation algorithm is proposed to estimate the stellar effective temperature in the present paper. Firstly, the spectrum data is processed by principal component analysis(PCA), then, an estimating model based on a Gaussian kernel function is set up using the PCA data and their temperatures. Experiments were carried out to verify the efficiency, and numerical robustness of the algorithm is also tested.

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