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.

Cultural firework algorithm and its application for digital filters design

Abstract: The substance of the digital filter design is a multi-parameter optimisation problem. This paper presents a joint objective function to design finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters, and a cultural firework (CF) algorithm is proposed to implement filter designs. The design of the filter is transformed into the constrained optimisation problem, and the cultural firework algorithm is used to search optimal value of filter design parameters in the parameter space with parallel search. The proposed cultural firework algorithm is a multi-dimensional search algorithm for optimisation of real numbers, which uses mechanisms of cultural evolution to update the locations of cultural sparks. Computer simulations have showed that FIR and IIR digital filters based on the CF algorithm are superior to previous filters based on particle swarm optimisation (PSO), quantum-behaved particle swarm optimisation (QPSO) and adaptive quantum-behaved particle swarm optimisation (AQPSO) in the convergence speed and optimisation results. The effectiveness and superiority of the CF are also demonstrated by computer simulations.

Mesoscopic titania solar cells with the tris(1,10-phenanthroline)cobalt redox shuttle: uniped versus biped organic dyes

Abstract: The combination of some non-iodine one-electron outer-sphere redox shuttles with high-absorption-coefficient organic D–π–A dyes has very recently brought forth a new opportunity towards efficiency enhancement of dye-sensitized solar cells (DSCs). Thereby a comprehensive understanding on the structure–property relationship of metal-free organic photosensitizers will play a pivotal role in further development of high-efficiency devices iodine-free. Herein, we scrutinize some general influences of the cyanoacrylic-acid-anchoring-group number of organic dyes upon the optoelectronic features of mesoscopic titania solar cells based on the tris(1,10-phenanthroline)cobalt(II/III) redox shuttle, by employing three pairs of uniped and biped dyes with thiophene, 3,4-ethylenedioxythiophene and 4,4-dihexyl-4H-cyclopenta[2,1-b:3,4-b′]dithiophene as the π-conjugated spacers. It is found that regardless of the spacer selection, a biped dye confers an improved light absorption coefficient of a stained titania film in comparison with its uniped analogue, which is highly desirable for cells with a one-electron outer-sphere redox shuttle, because in this type of cells a very thin titania film is always needed to satisfy the mass transport and electron collection requirements. However, our electrical impedance analyses have revealed that the alteration from a uniped chromophore to its biped congener evokes not only a downward displacement of the titania conduction band edge but also an acceleration of interfacial charge recombination of the titania electrons with cobalt(III) ions, leading to an attenuated open-circuit photovoltage, highlighting the future necessity to design a sterically bulky organic dye featuring superior light-harvesting capacity.

Dynamic Spectrum Interaction of UAV Flight Formation Communication With Priority: A Deep Reinforcement Learning Approach

Abstract: The formation flights of multiple unmanned aerial vehicles (UAV) can improve the success probability of single-machine. Dynamic spectrum interaction solves the problem of the ordered communication of multiple UAVs with limited bandwidth via spectrum interaction between UAVs. By introducing reinforcement learning algorithm, UAVs can continuously obtain the optimal strategy by continuously interacting with the environment. In this paper, two types of UAV formation communication methods are studied. One method allows for information sharing between two UAVs in the same time slot. The other method is the adoption of a dynamic time slot allocation scheme to complete the alternate use of time slots by the UAV to realize information sharing. The quality of experience (QoE) is introduced to evaluate the results of UAV sharing, and the M/G/1 queuing model is used for priority and to evaluate the packet loss of UAV. In terms of algorithms, a combination of deep reinforcement learning (DRL) and the long-short-term memory (LSTM) network is adopted to accelerate the convergence speed of the algorithm. The experimental results show that, compared with the Q-learning and deep Q-network (DQN) methods, the proposed method achieves faster convergence and better performance with respect to the throughput rate.