Southeast University

About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Computer science & MIMO. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.

Three-dimensional broadband and broad-angle transformation-optics lens

Abstract: Lenses with superior performance with respect to conventional uniform materials are desirable. The authors show a three-dimensional lens, made of multilayered metamaterials and based on approximate transformation optics, which works in different polarizations at broad viewing angles and with wide bandwidth.

Disturbance/Uncertainty Estimation and Attenuation Techniques in PMSM Drives—A Survey

Abstract: This paper gives a comprehensive overview on disturbance/uncertainty estimation and attenuation (DUEA) techniques in permanent-magnet synchronous motor (PMSM) drives. Various disturbances and uncertainties in PMSM and also other alternating current (ac) motor drives are first reviewed which shows they have different behaviors and appear in different control loops of the system. The existing DUEA and other relevant control methods in handling disturbances and uncertainties widely used in PMSM drives, and their latest developments are then discussed and summarized. It also provides in-depth analysis of the relationship between these advanced control methods in the context of PMSM systems. When dealing with uncertainties, it is shown that DUEA has a different but complementary mechanism to widely used robust control and adaptive control. The similarities and differences in disturbance attenuation of DUEA and other promising methods such as internal model control and output regulation theory have been analyzed in detail. The wide applications of these methods in different ac motor drives (in particular in PMSM drives) are categorized and summarized. Finally, the paper ends with the discussion on future directions in this area.

Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network

Abstract: The paper proposed an improved bacterial chemotaxis optimization (IBCO), which is then integrated into the back propagation (BP) artificial neural network to develop an efficient forecasting model for prediction of various stock indices. Experiments show its better performance than other methods in learning ability and generalization.