Biomedical subjects

A-Li Luo

Publications and source records attributed to A-Li Luo.

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.

English Abstract↗

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.

English Abstract↗

PubMed2005

[An automated measurement of the galaxies spectra of LAMOST].

To measure redshifts of the marge amount of galaxies' spectra automatically is the main goal of the data processing for the LAMOST project (Large Sky Area Multi-Object Optical Fiber Spectroscopic Telescope). A method called PCAZ can be applied to measure the very small redshifts (generally z < 0.2) due to the restriction of the wavelength range of the templates that are composed to make orthogonal templates. In the present article, the authors break the restriction by improving PCAZ method according to the characteristic of LAMOST spectra. Applying this new method to the SDSS data, more than 90% of the results are correct. The maximum limitation for redshift measurement of this new method depends on the wavelength range of the templates and the S/N of the blue parts of the spectra. According to the spectral feature of LAMOST, the authors can measure the galaxies with z < 0.8 correctly. From the experiment the authors concluded: first, this method can be used to measure the redshift of LAMOST spectra; second, the authors need to compose self-contained templates of various galaxies (UV-IR) to measure the survey redshift; finally, the S/N of the blue end of the spectra influences the measurement of the large redshift.

Algorithms↗

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.

English Abstract↗