The UAV pursuit-evasion strategy based on Deep Deterministic Policy Gradient (DDPG) algorithm is a current research hotspot. However, this algorithm has the defect of low efficiency in sample exploration. To solve this problem, this paper uses the imitation learning (IL) to improve the DDPG exploration strategy. A kind of quasiproportional guidance control law is designed to generate effective learning samples, which are used as the data of the initial experience pool of DDPG algorithm. The UAV pursuit-evasion strategy based on DDPG and imitation learning (IL-DDPG) is proposed, and the algorithm obtains the data from the experience pool for experience playback learning, which improves the exploration efficiency of the algorithm in the initial stage of training and avoids the problem of too many useless exploration in the training process. The simulation results show that the trained pursuit-UAV can flexibly adjust the flight speed and flight attitude to pursuit the evasion-UAV quickly. It also verifies that the improved DDPG algorithm is more effective than the basic DDPG algorithm to improve the training efficiency.

The incomplete inverse spectral and inverse nodal problems for Dirac operator defined on a finite interval with separated boundary conditions are considered. We prove uniqueness theorems for the so-called incomplete inverse spectral problem. Using the obtained result we show that for a unique determination of the operator it is sufficient to specify the nodal points only on a part of the interval slightly exceeding its half.

This work deals with the interior transmission eigenvalue problem for a spherically stratified medium supported in { x : | x | = r = b }, which can be formulated as − y ″ + q ( x ) y = λ ² y with boundary conditions y (0) = 0 = y ′(1) cos( aλ ) − y (1) sin( aλ )/ λ , where a = b / B with B being the size of the wave speed, measured by an integral. We provide a necessary and sufficient condition for the existence issue by giving a new method that allows unique reconstruction of a potential of this Sturm–Liouville problem from the spectrum of the problem and the set of the norming constants corresponding to the real eigenvalues when 0 < a < 1; and from the spectrum together with one additional piece of information when a = 1. The method is based on Mittag-Leffler expansions which can help us to decompose an entire function of exponential type into two functions of more smaller exponential types. This decomposition provides us a well-suited situation for utilizing Levin–Lyubarski interpolation formula to reconstruct the potential for our problem.

With the evolution of communication networks, the Internet of Vehicles (IOV) continues to accelerate the safe and rapid development of autonomous vehicles. Vehicle-to-Pedestrian (V2P) communication is a key technology in autonomous vehicles and a potential solution to realize collaborative intelligence between vehicles and pedestrians. However, the existing V2P communication early warning system does not consider the uncertainty of pedestrian trajectory, and the determination of the collision area is limited to a single point, resulting in an inaccurate system judgment and limited improvement of traffic efficiency. This paper designs a new autonomous-oriented V2P communication network architecture and completes a V2P communication early warning system based on Long Range (LoRa). A V2P anticollision model is established, and a new V2P collision risk early warning method is proposed. In this method, danger index is introduced into the early warning of collision between pedestrian and vehicle. The long short-term memory (LSTM) artificial neural network is used to predict the pedestrian’s trajectory, so as to deduce the pedestrian-vehicle collision risk area when the pedestrian trajectory is uncertain. Meanwhile, the confidence probability is used to judge whether the pedestrian and vehicle are warned. The simulation shows that the V2P collision risk warning method proposed in this paper has good performance, which can accurately warn the pedestrian and vehicle under different vehicle speeds and Global Positioning System (GPS) positioning errors. At the same time, it reflects the characteristics of intelligence brought by using LSTM methods. Using the V2P communication early warning system based on LoRa to verify the experimental results show that when the GPS positioning accuracy is submeter level, the prediction accuracy is greater than 98%. The results of the proposed method show good performance and high detection rate.

The establishment of a complete drug traceability system is essentially important for public drug security and the business of pharmaceutical companies which is aimed at tracking where the drug has gone along the drug supply chain. Traditional centralized server-client technical solutions have been far from satisfaction for their bad performances in data authenticity, privacy, system resilience, and flexibility. In this paper, we propose a drug traceability scheme called MB-BC, which realizes the security and traceability of drug data through a novel multibranched blockchain scheme. Different from the characteristics of transparency of traditional blockchains, MB-BC realizes fine-grained access control of data between all levels in the system, which improves the security and privacy of data. MB-BC has further improved the existing consensus mechanism, strengthened the supervision of pharmaceutical companies, and further improved the safety and robustness of the system. Furthermore, the system combines data access strategies with smart contracts; each branch chain can also issue its smart contract to provide personalized services. Finally, security and performance evaluations show that the solution is advantageous in terms of data security, system robustness, supervisibility, and traceability, as well as efficient in terms of blockchain throughput, data query time, and blockchain consensus consumptions, compared with other typical approaches.