A wideband compact magnetoelectric (ME) dipole antenna is investigated for millimeter-wave applications. First, an aperture coupled ME dipole is proposed with wideband and low profile. Next, transverse slots are added to miniaturize the antenna. The radiation performance of the higher-order mode is also improved. The antenna is finally miniaturized to 2.5×3.3 mm 2 ( 0.27 λ 0 ×0.35 λ 0 , where λ 0 is the wavelength in free space at center frequency) when it is used in the array environment. A bandwidth of 48.8% (24.3-40 GHz) for SWR <; 2 can be achieved, with unidirectional radiation performance over the operating band. By combining the proposed compact antenna with an eight-way substrate integrated coaxial line (SICL) feed network, a 1 ×8 linear array is designed, fabricated, and measured. Good beam scanning capability is also verified by active simulation. With the advantages of wide bandwidth, compact size, promising radiation pattern and wide-angle beam scanning potential, the proposed antenna would be attractive for millimeter-wave devices and antenna in package (AiP) applications.
Battery energy manage system is the key technology in the research of electrical vehicle. Signal batteries parameters like voltage and temperature should be acquired accurately and in time in order to show the working state and SOC of battery. Based on the distributed battery signal getting system and virtual instrument LabVIEW, the battery energy management system is developed. Virtual instrument can provide direct signal of battery, and show the changing of state accurately and in time.
In this short paper, we study platooning control of drones using only the information from a camera attached to each drone. For this, we adopt real-time objection detection based on a deep learning model called YOLO (you only look once). The YOLO object detector continuously estimates the relative position of the drone in front, by which each drone is controlled by a PD (Proportional-Derivative) feedback controller for platooning. The effectiveness of the proposed system is shown by indoor experiments with three drones.
Energy revolution is the guideline for the construction of modern intelligent power systems. All types of increasing new energy, as well as the requirement of fast demand responses will bring frequent and sizable voltage fluctuations. But existing voltage optimization method lacks accurate grid model and real-time computing capability, and can only make decisions offline. In response to the above problems, this paper proposes an intelligent optimization method for reactive voltage control based on deep reinforcement learning by transforming the voltage optimization problem of the power system into a Markov decision process. The optimization goal is to minimize the voltage deviation of each node, the system network loss, and maximize the reactive power reserve of each equipment. During offline learning, the agent continuously interacts with the power system environment built by the simulation platform to complete the network training. This method for voltage regulation of discrete reactive power compensation devices based on the Deep Q Network (DQN) algorithm does not rely on predictive data, and can achieve online optimization, effectively solving the multi-objective complex combination optimization of discrete reactive power compensation devices problems, improving the ability of the grid to respond to disturbances by massively connected new energy. Finally, through the IEEE test, in the case of new energy, load fluctuations and N-1 fault, voltage of each node, network loss and the reactive reserve of each device can be controlled rapidly, which verifies the validity of the above algorithm.
In the above article the figure 2, 13, 14 and 17 have been corrected after online publication.The authors apologize for any inconvenience caused.Figure 2. Equiaxed-microstructure of the solution-treated SA508-ІV.Figure 13.Microstructures of the SA508-IV steel deformed at 950 C and strain rates of (a) 0.001 s À1 , (b) 0.01 s À1 , (c) 0.1 s À1 and (d) 1 s À1 .
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