Fu-chao Wu

Northeast Institute of Geography and Agroecology, Beijing, Beijing Shi, China

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Publications (23)5.46 Total impact

  • [show abstract] [hide abstract]
    ABSTRACT: The authors present a new method called two class PCA for decomposing the mixed spectra, namely, for subtracting the host galaxy contamination from each SN spectrum. The authors improved the quality of reconstructed galaxy spectrum and computational efficiency, and these improvements were realized because we used both the PCA eigen spectra of galaxy templates library and SN templates library to model the mixed spectrum. The method includes mainly three steps described as follows. The first step is calculating two class PCA eigen spectra of galaxy templates and SN templates respectively. The second step is determining all reconstructed coefficients by the SVD matrix decomposition or orthogonal transformation. And the third step is computing a reconstructed galaxy spectrum and subtracting it from each mixed spectrum. Experiments show that this method can obtain an accurate decomposition of a mixed synthetic spectrum, and is a method with low time-consumption to get the reliable SN spectrum without galaxy contamination and can be used for spectral analysis of large amount of spectra. The time consumption using our method is much lower than that using chi2-template fitting for a spectrum.
    Guang pu xue yu guang pu fen xi = Guang pu 06/2010; 30(6):1707-11. · 0.29 Impact Factor
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    ABSTRACT: Supernova (SN) is one of the most intense astronomical phenomena among the known stellar activities, but compared with several billion astronomical objects which people have probed, the number of supernova the authors have observed is very small. Therefore, the authors need to find faster and higher-efficiency approaches to searching supernova. In the present paper, we present a novel automated method, which can be successfully used to reduce the range of searching for 1a supernova candidates in a huge number of galaxy spectra. The theoretical basis of the method is clustering and outlier picking, by introducing and measuring local outlier factors of data samples, description of statistic characters of SN emerges in low dimension space. Firstly, eigenvectors of Peter's 1a supernova templates are acquired through PCA projection, and the description of la supernova's statistic characters is calculated. Secondly, in all data set, the local outlier factor (LOF) of each galaxy is calculated including those SN and their host galaxy spectra, and all LOFs are arranged in descending order. Finally, spectra with the largest first one percent of all LOFs should be the reduced 1a SN candidates. Experiments show that this method is a robust and correct range reducing method, which can get rid of the galaxy spectra without supernova component automatically in a flood of galaxy spectra. It is a highly efficient approach to getting the reliable candidates in a spectroscopy survey for follow-up photometric observation.
    Guang pu xue yu guang pu fen xi = Guang pu 12/2009; 29(12):3420-3. · 0.29 Impact Factor
  • Jian-nan Zhang, Fu-chao Wu, A-li Luo
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    ABSTRACT: The three fundamental parameters of stellar atmosphere, i.e. the effective temperature, the surface gravity, and the metallic, determine the continuum and spectral lines in the stellar spectrum. With the development of the modern telescopes such as SDSS, LAMOST projects, the great voluminous spectra demand to explore automatic celestial spectral analysis methods. It is most significant for Galaxy research to develop automatic methods determining the fundamental parameters from stellar spectra data. Two non-linear regression algorithms, kernel least squared regression (KLSR) and kernel PCA regression (KPCR), are proposed for estimating the three parameters in the present paper. The linear regression models, LSR and PCR, are extended to non-linear regression by using a kernel function for the stellar parameter estimation from spectra. Extensive experiments on low resolution spectra data show: (1) KLSR and KPCR methods realize the regression from spectrum to the effective temperature and gravity. KLSR is sensitive to the noise while KPCR is robust than the former. (2) For the effective temperature estimation, the two algorithms perform similarly; and for the gravity and metallic estimation, the KPCR is superior to the KLSR and the NPR (Non-parameter regression); (3) KLSR and KPCR methods are simple and efficient for the stellar spectral parameter estimation.
    Guang pu xue yu guang pu fen xi = Guang pu 05/2009; 29(4):1131-6. · 0.29 Impact Factor
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    ABSTRACT: Given a set of low-redshift spectra of active galactic nuclei, the wave bands of spectra in the rest frame were intercepted according to the different features of emission lines of broad-line AGNs and narrow-line AGNs, and an adaptive boosting (Adaboost) method was developed to carry out the classification experiments of feature fusion. As a result, the wave band of Halpha and [N II] was confirmed to be the main discriminative feature between broad-line AGNs and narrow-line AGNs. Then based on the wave band of Halpha and [N II], the Adaboost method was used for the spectral classification. In this method, the "weak classifiers" were increased constantly during training until a scheduled error rate or a maximum cycle times was met, then the classification judgment of the consequent collective classifier was determined by the votes of respective judgments of these "weak classifiers". The Adaboost method needs not to adjust parameters in advance and the results of "weak classifiers" are only required to be better than random guessing, so its algorithm is very simple. As proved by the experiments, the adaboost method achieves good performance in the classification just based on the wave band of Halpha and [N II] so that it could be applied effectively to the automatic classification of large amount of AGN spectra from the large-scale spetral surveys.
    Guang pu xue yu guang pu fen xi = Guang pu 03/2008; 28(2):472-7. · 0.29 Impact Factor
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    ABSTRACT: Celestial spectra should be preprocessed before automated classification to eliminate the disturbance of noise, observa-tion environment, and flux aberrance. In the present work, the authors studied the spectrum flux standardization problem. By analyzing the disturbing factors and their characteristics, the authors put forward a theoretical model for spectra flux, and corre-spondingly give several flux standardizing methods. The rationality/correctness of the model, and the satisfactory performance of the proposed methods have been obtained by the experiments over normal galaxies (NGs) and quasi-stellar object (Qso). Furthermore, the authors theoretically analyze, compare and evaluate them. In particular, this work indicated that the conventional method is worse than the proposed one. And the investigation is also particularly significant for other automatic spectrum processing study, e. g. redshift determination, effective temperature, metallic estimation, etc.
    Guang pu xue yu guang pu fen xi = Guang pu 08/2007; 27(7):1448-51. · 0.29 Impact Factor
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    ABSTRACT: A kernel based covering algorithm, called the kernel covering algorithm (KCA), is proposed for the classification of celestial spectra. This algorithm is a combination of kernel trick with the covering algorithm, and is used to extract the support vectors in feature space. The experiments show that the classification result based on KCA is a little less than that based on SVM. However, KCA only involves the distance computation without the need to solve the quadratic programming problem. Also, KCA is insensitive to the width of gauss window. Although KCA has a comparable classification performance with the covering algorithm, it changes the distance between samples in feature space by the nonlinear mapping such that the distribution of samples is more adaptable to classify. Therefore, the number of KCA's resulting support vectors is significantly smaller than that of the covering algorithm.
    Guang pu xue yu guang pu fen xi = Guang pu 04/2007; 27(3):602-5. · 0.29 Impact Factor
  • Source
    Xiao-Ming DENG, Fu-Chao WU, Yi-Hong WU
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    ABSTRACT: AbstractCentral catadioptric cameras are widely used in virtual reality and robot navigation, and the camera calibration is a prerequisite for these applications. In this paper, we propose an easy calibration method for central catadioptric cameras with a 2D calibration pattern. Firstly, the bounding ellipse of the catadioptric image and field of view (FOV) are used to obtain the initial estimation of the intrinsic parameters. Then, the explicit relationship between the central catadioptric and the pinhole model is used to initialize the extrinsic parameters. Finally, the intrinsic and extrinsic parameters are refined by nonlinear optimization. The proposed method does not need any fitting of partial visible conic, and the projected images of 2D calibration pattern can easily cover the whole image, so our method is easy and robust. Experiments with simulated data as well as real images show the satisfactory performance of our proposed calibration method.
    Acta Automatica Sinica. 01/2007;
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    ABSTRACT: By means of a batch of low-redshift spectral data of AGNs taken from the SDSS, an automated K-nearest neighbor method is developed to classify AGNs into two types: broad-line and narrow-line AGNs. According to the different characteristics of emission lines of broad-line and narrow-line AGNs, the spectral wavebands containing the Hβ, [OIII], Hα and [NII] emission lines are used separately or in combination in the classification. experiment. The results show that the best results are obtained when only the wavebands of H and [NII] are used, and that for a training set of size 1000 and a testing set of 3313, we can achieve a speed of 32.89 single classifications per second. It is demonstrated that, where the typical spectral features are sufficiently exploited, the automated classification method is feasible for the spectra of AGNs in largescale spectral surveys and provides a fast and straightforward alternative to classification schemes based on using the FWHM values of emission lines or the line strength ratio diagnostic diagrams.
    Chinese Astronomy and Astrophysics 01/2007; 31(4):352-362.
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    ABSTRACT: 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.
    Guang pu xue yu guang pu fen xi = Guang pu 11/2006; 26(10):1960-4. · 0.29 Impact Factor
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    ABSTRACT: The LAMOST project, the world's largest sky survey project being implemented in China, is expected to obtain 10(5) quasar spectra. The main objective of the present article is to explore methods that can be used to estimate the redshifts of quasar spectra from LAMOST. Firstly, the features of the broad emission lines are extracted from the quasar spectra to overcome the disadvantage of low signal-to-noise ratio. Then the redshifts of quasar spectra can be estimated by using the multi-scaling feature matching. The experiment with the 15, 715 quasars from the SDSS DR2 shows that the correct rate of redshift estimated by the method is 95.13% within an error range of 0. 02. This method was designed to obtain the redshifts of quasar spectra with relative flux and a low signal-to-noise ratio, which is applicable to the LAMOST data and helps to study quasars and the large-scale structure of the universe etc.
    Guang pu xue yu guang pu fen xi = Guang pu 10/2006; 26(9):1738-41. · 0.29 Impact Factor
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    ABSTRACT: 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.
    Guang pu xue yu guang pu fen xi = Guang pu 03/2006; 26(2):377-81. · 0.29 Impact Factor
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    ABSTRACT: 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.
    Guang pu xue yu guang pu fen xi = Guang pu 03/2006; 26(2):372-6. · 0.29 Impact Factor
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    ABSTRACT: It is well known that without any priori knowledge on the scene, camera motion and camera intrinsic parameters, the only constraint between a pair of images is the so-called epipolar constraint, or equivalently its fundamental matrix. For each fundamental matrix, at most two independent constraints on the cameras’ intrinsic parameters are available via the Kruppa equations. Given N images, N(N−1)/2 fundamental matrices appear, and N(N−1) Kruppa constraints are available. However, to our knowledge, a formal proof of how many independent Kruppa constraints exist out of these N(N−1) ones is unavailable in the literature. In this paper, we prove that given N images captured by a pinhole camera with varying parameters and under general motion, the number of independent Kruppa constraints is (5N−9) (N > 2), and it is less than that of independent constraints from the absolute quadric by only one. This one-constraint-less property of the Kruppa equations is their inherent deficiency and is independent of camera motion. This deficiency is due to their failure of automatic enforcement of the rank-three-ness on the absolute quadric.
    Journal of Computer Science and Technology 02/2006; 21(2):209-217. · 0.48 Impact Factor
  • Xin Xu, Fu-chao Wu, Zhan-yi Hu, A-li Luo
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    ABSTRACT: 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.
    Guang pu xue yu guang pu fen xi = Guang pu 02/2006; 26(1):182-6. · 0.29 Impact Factor
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    ABSTRACT: 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.
    Guang pu xue yu guang pu fen xi = Guang pu 01/2006; 25(12):2088-91. · 0.29 Impact Factor
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    ABSTRACT: The physical parameters of stellar atmosphere, e.g. the effective temperature, surface gravity and chemical abundance, are the main factors for the differences in stellar spectra, and the automatic measurement of these parameters is an important content in the automatic processing of the immense amount of spectral data provided by LAMOST and other patrol telescopes. Aiming at the estimation of the physical parameters for every star in large samples of stellar spectral data, a variable window-width algorithm is proposed in this article. It consists of the following three steps: (1) A PCA (principal component analysis) treatment of historical stellar spectral data is carried out to obtain a low-dimensional characteristic data of the spectra. (2) Establish the correlation between the characteristic data and the physical parameters using a non-parametric estimator with variable window-width. (3) By means of this estimator, the three physical parameters of the star are directly calculated. As shown by results of experiments, in comparison with the fixed window-width estimator and other algorithms reported in literature, our algorithm is more accurate and robust.
    Chinese Astronomy and Astrophysics 01/2006; 30(2):176-186.
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    ABSTRACT: This paper presents a fast neural network method of radial basis function with dynamic decay adjustment (RBFN-DDA) to classify Quasi-Stellar Objects (QSOs) and galaxies automatically. The classification process is mainly comprised of three parts: (1) the dimensions of the normalized input spectra is reduced by the Principal Component Analysis (PCA); (2) the network is built from scratch: the number of required hidden units is determined during training and the individual radii of the Gaussians are adjusted dynamically until corresponding criterions are satisfied; (3) The trained network is used for the classification of the real spectra of QSOs and galaxies. The method of RBFN-DDA having constructive and fast training process solves the difficulty of selecting appropriate number of neurons before training in many methods of neural networks and achieves lower error rates of spectral classification. Besides, due to its efficiency, the proposed method would be particularly useful for the fast and automatic processing of voluminous spectra to be produced from the large-scale sky survey project.
    Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II; 01/2006
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    ABSTRACT: 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.
    Guang pu xue yu guang pu fen xi = Guang pu 12/2005; 25(11):1884-8. · 0.29 Impact Factor
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    ABSTRACT: 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.
    Guang pu xue yu guang pu fen xi = Guang pu 12/2005; 25(11):1889-92. · 0.29 Impact Factor
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    ABSTRACT: 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.
    Guang pu xue yu guang pu fen xi = Guang pu 12/2005; 25(11):1895-8. · 0.29 Impact Factor

Publication Stats

11 Citations
5.46 Total Impact Points


  • 2005–2010
    • Northeast Institute of Geography and Agroecology
      • Institute of Automation
      Beijing, Beijing Shi, China
  • 2006–2007
    • Chinese Academy of Sciences
      • Institute of Automation
      Peping, Beijing, China