North Laser Technology Group Co.

Chengdu, China

North Laser Technology Group Co.

Chengdu, China

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Luo H.,Sichuan University | Luo H.,North Laser Technology Group Co. | Qi L.,Changchun University | Zhu S.-F.,Sichuan University | And 4 more authors.
Rengong Jingti Xuebao/Journal of Synthetic Crystals | Year: 2012

In this paper, the liquid phase synthesis method was used for synthesis of holmium yttrium fluoride barium [molecular formula: Ho3+:BaY2F8, hereinafter referred to as Ho:BYF] polycrystalline raw materials. The laser crystal, barium yttrium fluoride doped with Ho3+ [Ho:BYF] was grown by the Czochralski method with the following parameters: growth speed (0.5-1 mm/h), rotating speed (10 r/min). The result of XRD test shows that the crystal belongs to the monoclinic crystal system and C2/m space group. The crystal absorption and fluorescence spectra were tested, and the absorption coefficient and absorption cross section of Ho3+ on 889 nm were also calculated as follows: respectively 4.84 cm-1 and 1.26×10-21 cm2. The absorption peaks is corresponding to the transition of Ho3+ from the ground state 5I5 to the excited state 5I6, by which 3.9 μm laser output can be carried out.


Zhai D.,Yangzhou University | Wang Z.,Yangzhou University | Zhou X.,Yangzhou University | Xu C.,North Laser Technology Group CO.
Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016 | Year: 2016

Graph learning framework has become a popular method of dimensional reduction. However, the traditional graph construction heavily relies on the selection of parameters, resulting in unstable performance in real-world face recognition applications. To address this, a label information-based weighted regularized sparsity preserving embedding for face recognition is proposed in this paper. Different from the existing L1-graph, we adaptively construct both intrinsic graph and penalty graph with label information-based L1-graph in the graph embedding framework. In order to preserve the local structure, Gaussian kernel distances between the samples are used as weight matrix to weight graph. In addition, the problem of irreversible matrix is alleviated by regularization instead of PCA that loses some discrimination information. At last, an objective function combining globality and locality is created to reduce dimensionality. Meanwhile, Schmidt orthogonalization is used to obtain the orthogonal basis vectors. The experimental results on public face database illustrate that the proposed algorithm has high recognition rate. © 2016 IEEE.

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