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 & Computer science. The organization has 31149 authors who have published 27940 publications receiving 276787 citations. The organization is also known as: HEU.

Ceramic Superstrate-Based Decoupling Method for Two Closely Packed Antennas With Cross-Polarization Suppression

Abstract: A ceramic superstrate-based decoupling method (CSDM) is proposed to reduce the mutual coupling between two closely packed dipole antennas while maintaining cross-polarization suppression. Compared with other superstrate-based methods, this proposed method can effectively reduce the mutual coupling between the antennas without using any periodic structures on the superstrate. The ceramic superstrate is a 2 mm thin slab with a relative dielectric constant of 20.5 and is suspended over the antennas coupled in H-plane with the spacing of only 0.28 wavelength at 3.5 GHz. It is demonstrated by both simulation and measurement that the isolation between two dipole antennas can be improved from 10 to more than 25 dB within the operation band while their reflection coefficients remain to be below −10 dB after the ceramic superstrate is introduced. The radiation patterns of the antenna maintain stable at different operation frequencies within the band of interest and the peak gain has increased by around 1.5 dB. Meanwhile, the total efficiency is enhanced by about 15% and the envelope correlation coefficient (ECC) between the two antennas is reduced from 0.7 to 0.4.

Oligothiophene dye-sensitized solar cells

Abstract: We scrutinize the energetic and kinetic interplays in oligothiophene dye-sensitized solar cells via analyzing electrical impedance as well as transient emission and absorption spectroscopies.

Data-Driven Performance-Prescribed Reinforcement Learning Control of an Unmanned Surface Vehicle.

Abstract: An unmanned surface vehicle (USV) under complicated marine environments can hardly be modeled well such that model-based optimal control approaches become infeasible. In this article, a self-learning-based model-free solution only using input-output signals of the USV is innovatively provided. To this end, a data-driven performance-prescribed reinforcement learning control (DPRLC) scheme is created to pursue control optimality and prescribed tracking accuracy simultaneously. By devising state transformation with prescribed performance, constrained tracking errors are substantially converted into constraint-free stabilization of tracking errors with unknown dynamics. Reinforcement learning paradigm using neural network-based actor-critic learning framework is further deployed to directly optimize controller synthesis deduced from the Bellman error formulation such that transformed tracking errors evolve a data-driven optimal controller. Theoretical analysis eventually ensures that the entire DPRLC scheme can guarantee prescribed tracking accuracy, subject to optimal cost. Both simulations and virtual-reality experiments demonstrate the remarkable effectiveness and superiority of the proposed DPRLC scheme.