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Two-dimensional Harmonic Modelling for Electro-magnetic Solution in Cartesian Coordinates
Yunlu Du,Baocheng Guo,Z. Djelloul-khedda,Fei Peng,Yunkai Huang 한국자기학회 2022 Journal of Magnetics Vol.27 No.3
This paper first presents a general two-dimension (2D) harmonic analytical solution for the magnetic field of electric machines in the Cartesian coordinates. In this solution, the relative permeance is directly considered in Laplace and Poisson’s equations, and the particular solutions in Cartesian coordinates are solved. By applying the complex Fourier separation method, with the boundary and interface conditions, the magnetic field in the inhomogeneous region is solved from system equations. Numerical examples validate the presented method and the obtained results have a satisfactory agreement with the finite element analysis. The proposed model in this paper has a significant value for modelling electric machines, such as linear permanent magnet (PM) machines and axial flux PM machines.
Extracting Attributes of Named Entity from Unstructured Text with Deep Belief Network
Bei Zhong,Jin Liu,Yuanda Du,Yunlu Liaozheng,Jiachen Pu 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5
Entity attribute extraction is a challenging research topic with broad application prospects. Many researchers had proposed rule based or statistic based approaches to deal with the extraction task in a variety of application areas. Recently, deep learning had shown its capacity to model high-level abstractions in data by using multiple processing layers network with complex structures. However there has no research reported to conduct entity attribute extraction with deep learning method. In this paper, we propose a new approach to extract the entities’ attributes from unstructured text corpus that was gathered from Web. The proposed method is an unsupervised machine learning method that extracts the entity attributes utilizing deep belief network (DBN). Experiment results show that, with our method, entity attributes can be extracted accurately and manual intervention can be reduced when compared with tradition methods.