Biomedical subjects

Zhan-yi Hu

Publications and source records attributed to Zhan-yi Hu.

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.

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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.

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PubMed2004

[Automated classification of celestial spectra based on support vector machines].

The main objective of an automatic recognition system of celestial objects via their spectra is to classify celestial spectra and estimate physical parameters automatically. This paper proposes a new automatic classification method based on support vector machines to separate non-active objects from active objects via their spectra. With low SNR and unknown red-shift value, it is difficult to extract true spectral lines, and as a result, active objects can not be determined by finding strong spectral lines and the spectral classification between non-active and active objects becomes difficult. The proposed method in this paper combines the principal component analysis with support vector machines, and can automatically recognize the spectra of active objects with unknown red-shift values from non-active objects. It finds its applicability in the automatic processing of voluminous observed data from large sky surveys in astronomy.

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PubMed2003

[A PCA based efficient stellar spectra classification method].

Stellar spectra classification is an indispensable part of any workable automated recognition system of celestial bodies. This paper introduces an efficient method of automated classification of stellar spectra based on the principal component analysis (PCA). The method consists of two parts. In the first part, the eigen-matrix is built by a standard PCA technique where only the first two eigenvectors are selected due to their predominance. More specifically, the first two eigenvalues are found to always represent more than 95% of the total sum of all the eigenvalues, and much larger than others in our all experiments. The principal component space of stellar (V1, V2) then is constructed from the first two eigenvectors. In the second part, namely classification part, an unknown spectrum X is first mapped to a 2D space with the two coordinates defined respectively as (V1T X, V2T X), then the nearest neighbor approach in this 2D space is employed to determine the spectral type as well as luminosity class of the input spectrum. The experimental results show that our new method can achieve comparable performance with that by the standard MK spectral types classification criterion, which is regarded as a benchmark in astronomy field. Thanks to its high efficiency, our new method appears promising especially for the processing of spectra in large quantities collected from large survey projects, such as LAMOST project in our country.

English Abstract↗

PubMed2003

[A new automatic quasars recognition technique based on PCA and Hough transform].

The main purpose of quasar recognition is to determine the observed quasar spectrum's redshift value. Previously the template of quasar rest frame in the literature was basically constructed based on astronomers' hypotheses. Due to the inaccuracy of such a template, it is hard to determine the redshift value by matching the observed quasar spectrum with the template directly. This paper's main contributions are two-fold: Firstly, the template in our paper is constructed by the principal component analysis (PCA) method from some selected spectra with known redshift values, hence the obtained template is more realistic. Secondly, a 2D standard Hough transform, rather than a 1D Hough transform, is used. This is because although only redshift needs to be determined in our system, based on our observations, the magnitude of emission peak is also changed, hence a new parameter, namely scale parameter, is also introduced to the Hough transform to enhance the reliability of the recognition. The experiments show that the proposed technique is workable and the correct recognition rate can reach about as high as 90%.

English Abstract↗

PubMed2002

[Describing language of spectra and rough set].

It is the traditional way to analyze spectra by experiences in astronomical field. And until now there has never been a suitable theoretical frame to describe spectra, which is may be owing to small spectra datasets that astronomers can get by low-level instruments. With the high-speed development of telescopes, especially on behalf of LAMOST, a large telescope which can collect more than 20,000 spectra in an observing night, spectra datasets are becoming larger and larger very fast. Facing these voluminous datasets, the traditional spectra-processing way simply depending on experiences becomes unfit. In this paper, we develop a brand-new language--describing language of spectra (DLS) to describe spectra of celestial bodies by defining BE (Basic element). And based on DLS, we introduce the method of RSDA (Rough set and data analysis), which is a technique of data mining. By RSDA we extract some rules of stellar spectra, and this experiment can be regarded as an application of DLS.

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PubMed2002

[A pseudo-triangle technique for redshift identification of celestial spectra].

In this paper, we present a novel technique for redshift identification. Redshift is a key parameter of celestial spectra. In the literature, there are few reports on redshift identification due to either no many people working on the problem or perhaps industrial confidentiality. Our technique is a pseudo-triangle technique. It consists of the following three major steps: in the first step, the three wavelengths corresponding to the three highest intensity values of an unknown spectrum are selected to construct a pseudo-triangle, and the largest angle of this triangle is calculated which is independent of redshift value. In the second step, the obtained angle is used as an index to retrieve the corresponding three model wavelengths via a pre-calculated look-up-table, which is composed of all the combinations of all the feature wavelengths of the model spectrum. And finally, based on the three corresponding wavelengths, the corresponding redshift value is derived. The main characteristic of our technique is its simplicity and efficiency, which is demonstrated by experiments on simulated data as well as on real celestial spectra. It is shown that the correct identification rate can reach as high as 86%. Taking into account the high noisy nature of celestial spectra, such a result is considered a good one.

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