Current Issue : April - June Volume : 2020 Issue Number : 2 Articles : 5 Articles
For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a twolayer\ntask and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task\nresources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and\nthe large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment\nalgorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus\nrules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in\nreal time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs\nand targets is large....
The urban intersection signal decision-making in traditional control methods are mostly\nbased on the vehicle information within an intersection area. The far vehicles that have not\nreached the intersection area are not taken into account, which results in incomplete information\nand even incorrectness in decision-making. This paper presents an intersection signal control\nmechanism assisted by far vehicle information. Using the aid of real-time information collection\nfor far vehicles through vehicular ad hoc networks (VANETs), we can consider them together and\ncalculate the accumulative waiting time for each intersection traffic flow at a future moment to\nmake the optimal signal decision. Simulation results show that, under three different traffic flow\nenvironmentsâ??same even traffic flows, same uneven traffic flows, and different traffic flowsâ??the\ntwo proposed implementation schemes based on the mechanism (fixed phase and period timing\nimprovement scheme, and dynamic phase and period control scheme) show good performances, in\nwhich the average waiting time and the ratio of long-waiting vehicles are both less than the results of\nthe traditional signal timing scheme. Especially, in the second scheme, the waiting time was reduced\nby an average of 38.6% and the ratio of long-waiting vehicles was reduced by an average of 7.67%....
This paper designs a vertically polarized, horizontal, omnidirectional vehicle antenna for the mobile communication band,\ncovering the available frequency bands of the wireless sensor network and 5G. The antenna is composed of semi-Tmonopole and\nsemicone monopole, which are placed vertically on the metal plate, especially suitable for being mounted on top of a car. T-branch\nmainly works at low frequency, and cone branch mainly works at high frequency. The cone branch adopts tapered structure in\norder to improve the impedance matching of antenna and increase the bandwidth of antenna. The antenna can be miniaturized by\ncutting the antenna in half. The operating frequencies of the antenna are 770 MHzâ??1000MHz and 1.7 GHzâ??3.78 GHz which can\ncover multiple wireless system bands, including GSM, LTE, and 5G....
In this report, a method for estimating pulse power performance according to pulse duration\nis proposed. This approach can be used for power control logic in an environmentally friendly power\ngeneration system such as electric vehicles and an energy storage system (ESS). Although there have\nbeen studies on pulse power capability, we are unaware of any publications on the estimation of the\nmagnitude of pulse power according to the power usage time, and the verification of the estimation\nresult. Therefore, we propose a method to predict power performance according to the pulse duration\nof batteries and supercapacitors that are used in eco-friendly power generation systems. The proposed\nmethod is systematically presented using both a lithium-ion battery module with a nominal voltage\nof 44 V, 11 Ah, and a supercapacitor module with a maximum voltage of 36 V and a capacitance of\n30 F....
Traffic oscillations often occur in road traffic, they make traffic flow unstable, unsafe and inefficient. Emerging connected and\nautonomous vehicle (CAV) technologies are potential solutions to mitigating the traffic oscillations for the advantages that CAVs\nare controllable and cooperative. In order to study a control strategy and the effectiveness of CAVs in mitigating traffic oscillations\nand improving traffic flow and analyse the characteristics of homogeneous traffic flow made up of CAVs and heterogeneous traffic\nflow made up of CAVs and RVs when traffic oscillations appear in traffic flow. Firstly, the formation and propagation of traffic\noscillations in a platoon of RVs are simulated and analysed. Then, a car-following control model is built to control the longitudinal\nmotion of CAVs, and real-time information of preceding CAV is used in the model and this can make the motion of CAVs more\ncooperative. The model reflects an idea named â??slow-inâ? and â??fast-out,â? and this idea is helpful to mitigate traffic oscillations. Then,\nnumerical simulations of homogeneous traffic flow of a platoon of CAVs and simulations of heterogeneous traffic flow containing\nCAVs and RVs are conducted, and different penetration rates (0, 0.2, 0.4, 0.6, 0.8, and 1) of CAVs are considered in the simulations of\nheterogeneous traffic flow. The characteristics and evolution of traffic flow are analysed and some indexes reflecting traffic efficiency\nand stability are calculated and analysed. Simulation results show that there are smaller velocity fluctuation, less stopping time and\nshorter length of road occupied when vehicle platoon contains CAVs (penetration rates are from 0.2 to 1) compared to the platoon\ncontaining only RVs (without CAVs). As for the heterogeneous traffic flow containing CAVs and RVs, these three indexes decrease\nwith the increase of penetration rates (from 0.2 to 1) of CAVs. These results indicate that CAVs with the car-following control model\nin vehicle platoon are beneficial for mitigating traffic oscillations and improving traffic flow....
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