Harbin Engineering University

About: Harbin Engineering University is a education organization based out in Harbin, Heilongjiang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 31149 authors who have published 27940 publications receiving 276787 citations. The organization is also known as: HEU.

Monodisperse Gd2O3:Ln (Ln = Eu3+, Tb3+, Dy3+, Sm3+, Yb3+/Er3+, Yb3+/Tm3+, and Yb3+/Ho3+) nanocrystals with tunable size and multicolor luminescent properties

Abstract: Multicolor and monodisperse Gd2O3:Ln (Ln = Eu3+, Tb3+, Dy3+, Sm3+, Yb3+/Er3+, Yb3+/Tm3+, and Yb3+/Ho3+) nanocrystals (NCs) with narrow size distribution were prepared by a homogeneous precipitation method followed by a subsequent calcination process. X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transformed infrared (FT-IR), thermogravimetric and differential thermal analysis (TG-DTA), photoluminescence (PL) spectra, cathodoluminescence (CL) spectra, and kinetic decays were employed to characterize the samples. The results show that the Gd2O3:Ln NCs can be directly indexed to cubic Gd2O3 phase with high purity. And the possible formation mechanism and the chemical reaction of each step to form spherical Gd2O3:Ln NCs are proposed according to the concerned analysis. Upon ultraviolet and low-voltage electron beams excitation, Gd2O3:Ln (Ln = Eu3+, Tb3+, Dy3+, and Sm3+) NCs exhibit respective bright red (Eu3+, 5D0 → 7F2), green (Tb3+, 5D4 → 7F5), blue (Dy3+, 4F9/2 → 6H13/2) and yellow (Sm3+, 4G5/2 → 6H7/2) down-conversion (DC) emissions. Under 980 nm NIR irradiation, Gd2O3:Ln (Ln = Yb3+/Er3+, Yb3+/Tm3+, and Yb3+/Ho3+) exhibit characteristic up-conversion (UC) emissions of Er3+, Tm3+, and Ho3+, respectively.

A novel virtual sample generation method based on Gaussian distribution

Abstract: Traditional machine learning algorithms are not with satisfying generalization ability on noisy, imbalanced, and small sample training set. In this work, a novel virtual sample generation (VSG) method based on Gaussian distribution is proposed. Firstly, the method determines the mean and the standard error of Gaussian distribution. Then, virtual samples can be generated by such Gaussian distribution. Finally, a new training set is constructed by adding the virtual samples to the original training set. This work has shown that training on the new training set is equivalent to a form of regularization regarding small sample problems, or cost-sensitive learning regarding imbalanced sample problems. Experiments show that given a suitable number of virtual sample replicates, the generalization ability of the classifiers on the new training sets can be better than that on the original training sets.

A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants.

Abstract: The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applications, mainly because adopting a single fault diagnosis method may reduce fault diagnosis accuracy. In addition, some of the proposed solutions rely heavily on fault examples, which cannot fully cover future possible fault modes in nuclear plant operation. This paper presents the results of a research in hybrid fault diagnosis techniques that utilizes support vector machine (SVM) and improved particle swarm optimization (PSO) to perform further diagnosis on the basis of qualitative reasoning by knowledge-based preliminary diagnosis and sample data provided by an on-line simulation model. Further, SVM has relatively good classification ability with small samples compared to other machine learning methodologies. However, there are some challenges in the selection of hyper-parameters in SVM that warrants the adoption of intelligent optimization algorithms. Hence, the major contribution of this paper is to propose a hybrid fault diagnosis method with a comprehensive and reasonable design. Also, improved PSO combined with a variety of search strategies are achieved and compared with other current optimization algorithms. Simulation tests are used to verify the accuracy and interpretability of research findings presented in this paper, which would be beneficial for intelligent execution of nuclear power plant operation.