Biomedical subjects

A-li Luo

Publications and source records attributed to A-li Luo.

7 recordsLinked to original sources

PubMed2006

[A novel method for the determination of redshifts of normal galaxies by non-linear dimensionality reduction].

It is difficult to determine the redshifts of normal galaxies (NG) from their spectra because of their common weak absorption property. In the present work, a novel method is proposed to effectively deal with this issue. The proposed method is composed of the following three parts: At first, the wavelet transform coefficients at the fourth scaling are experimentally found to be appropriate and used as our features to represent the absorption information from NG absorption lines, break points, and absorption bands. Then, the features are mapped by a non-linear method, LLE (locally linear embedding), onto an one-dimensional manifold in the 3D space; Finally, the NG redshifts are obtained by the nearest neighborhood technique from the redshift distribution on the manifold. Besides, the proposed method is compared with widely used PCA method in the literature with SDSS database, and is shown to be more accurate for the redshifts determination.

English Abstract↗

PubMed2006

[A novel method for continuum normalization of astronomical spectrum signals].

A combined method of wavelet transform and spline fitting is presented for continuum fitting with the strong spectral lines taken out beforehand. Some comparisons between normal methods and the method presented are made by the experiments on actual spectra And the experimental results show that our method is superior to others. Besides, an effective continuum revision formula is derived to avoid the appearance of negative continua obtained by some methods such as polynomial fitting and so on.

English Abstract↗

PubMed2005

[Using neural networks based template matching method to obtain redshifts of normal galaxies].

Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is proposed in this paper, which consists of the following three main steps: (1) From the template of normal galaxy, the two sets of samples are simulated, one with the redshift of 0.0-0.3, the other of 0.3-0.5, then the PCA is used to extract the main components, and train samples are projected to the main component subspace to obtain characteristic spectra. (2) The characteristic spectra are used to train a Probabilistic Neural Network to obtain a Bayes classifier. (3) An unknown real NG spectrum is first inputted to this Bayes classifier to determine the possible range of redshift, then the template matching is invoked to locate the redshift value within the estimated range. Compared with the traditional template matching technique with an unconstrained range, our proposed method not only halves the computational load, but also increases the estimation accuracy. As a result, the proposed method is particularly useful for automatic spectrum processing produced from a large-scale sky survey project.

Algorithms↗

PubMed2005

[A method for redshift determination of quasars based on cross correlation].

This paper presents a novel method for redshift determination of quasars. Firstly, a group of redshifts were determined using the emission line info extracted from the observed spectrum; Secondly, the template was redshifted according to the candidates, and the correlation between the observed spectrum and the redshifted template was measured. Finally, the redshift candidate corresponding to the highest correlation was chosen as the redshift. Compared with the existing methods based on spectral line matching, the proposed method has a lower dependence on the quality of spectral line extraction. Experiments show that this method is robust and superior to the methods based on spectral linematching.

Algorithms↗

PubMed2005

[Mean shift based auto-extraction of spectral lines for non-emission-line objects].

The mean shift algorithm is used. At first, the property that mean shift vectors always point toward local maxima of the density is used to get the pseudo continuum; secondly, mean shift filtering is a goodedge preserving smoothing, which canadaptively reduce the amount of smoothing near feature spectral lines, so the authors use mean shift filtering in noise reduction after the noramalization of continuum spectra; finally, the authors extract feature spectral lines by setting local thresholds. The experiments on both stars and normal galaxies show that our method can extract spectral lines accurately, which is helpful to the parameter measure and the automatic classification of spectra based on spectral lines.

English Abstract↗

PubMed2005

[A novel spectral classifier based on coherence measure].

Classification and discovery of new types of celestial bodies from voluminous celestial spectra are two important issues in astronomy, and these two issues are treated separately in the literature to our knowledge. In the present paper, a novel coherence measure is introduced which can effectively measure the coherence of a new spectrum of unknown type with the training sampleslocated within its neighbourhood, then a novel classifier is designed based on this coherence measure. The proposed classifier is capable of carrying out spectral classification and knowledge discovery simultaneously. In particular, it can effectively deal with the situation where different types of training spectra exist within the neighbourhood of a new spectrum, and the traditional k-nearest neighbour method usually fails to reach a correct classification. The satisfactory performance for classification and knowledge discovery has been obtained by the proposed novel classifier over active galactic nucleus (AGNs) and active galaxies (AGs) data.

English Abstract↗

PubMed2005

[Density estimation based model matching method for redshift determination].

The present paper proposes a model matching method based on density estimation for redshift determination, in whichthe problem of redshift determination is translated into the problem of searching for the point of maximum density within a data set. At first, the mean shift-based method for auto-extraction of spectral lines is used to get feature spectrallines. Secondly, according tothe redshift formula, the authors use the feature wavelength array and the spectral template to get a data set. Finally, the authors findthe point of maximum density within the data set, then the average of the data in epsilon-neighbor of the point is regarded as the redshift estimation. The information of feature wavelength and spectral line type is used in this method so that it can deal with every kind of spectra. Experiments show that our method is stable and the correct identification rate is high.

English Abstract↗