Biomedical subjects

Yong-heng Zhao

Publications and source records attributed to Yong-heng Zhao.

11 recordsLinked to original sources

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.

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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.

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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.

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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

[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↗

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

[Diagnosis and treatment of lung aspergillosis after liver transplantation].

OBJECTIVE: To assess the diagnosis and treatment of invasive lung aspergillosis after liver transplantation. METHODS: Routine sputum culture was performed. Itraconazole and fluconazole were used to prevent fungal infection prophylactically. Amphyotericin B was only used on aspergillosis. In 54 patients receiving, liver transplantation, 3 patients with lung aspergillosis were reviewed. RESULTS: Of the 3 patients 2 died and 1 recovered. CONCLUSIONS: Over-immunosuppression is a main risk factor for aspergillosis. Amphotericin B is still the best choice for the treatment of aspergillosis and its gradual, interrupted, low concentration administration, cooperated with itraconazole can ease the side effects.

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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↗

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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