Biomedical subjects

Fu-chao Wu

Publications and source records attributed to Fu-chao Wu.

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

[Spectra classification based on generalized discriminant analysis].

A kernel based generalized discriminant analysis (GDA) technique is proposed for the classification of stars, galaxies, and quasars. GDA combines the LDA algorithm with kernel trick, and samples are projected by nonlinear mapping onto the feature space F with high dimensions, and then LDA is conducted in F. Also, it could be inferred that GDA which combines the extension of Fisher's criterion with kernel trick is complementary to kernel Fisher discriminant framework. LDA, GDA, PCA and KPCA were experimentally compared with these three different kinds of spectra. Among these four techniques, GDA obtains the best result, followed by LDA, and PCA is the worst. Although KPCA is also a kernel based technique, its performance is not satisfactory if the selected number of the principal components is small, and in some cases, it appears even worse than LDA, a non-kernel based technique.

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 wavelet-transform-based method for the automatic detection of late-type stars].

The LAMOST project, the world largest sky survey project, urgently needs an automatic late-type stars detection system. However, to our knowledge, no effective methods for automatic late-type stars detection have been reported in the literature up to now. The present study work is intended to explore possible ways to deal with this issue. Here, by "late-type stars" we mean those stars with strong molecule absorption bands, including oxygen-rich M, L and T type stars and carbon-rich C stars. Based on experimental results, the authors find that after a wavelet transform with 5 scales on the late-type stars spectra, their frequency spectrum of the transformed coefficient on the 5th scale consistently manifests a unimodal distribution, and the energy of frequency spectrum is largely concentrated on a small neighborhood centered around the unique peak. However, for the spectra of other celestial bodies, the corresponding frequency spectrum is of multimodal and the energy of frequency spectrum is dispersible. Based on such a finding, the authors presented a wavelet-transform-based automatic late-type stars detection method. The proposed method is shown by extensive experiments to be practical and of good robustness.

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↗