With the development of UAV technology, the task allocation problem of multiple UAVs is remarkable, but most of these existing heuristic methods are easy to fall into the problem of local optimization. In view of this limitation, deep transfer reinforcement learning is applied to the task allocation problem of multiple unmanned aerial vehicles, which provides a new idea about solving this kind of problem. The deep migration reinforcement learning algorithm based on QMIX is designed. The algorithm first compares the target task with the source task in the strategy base to find the task with the highest similarity, and then migrates the network parameters obtained from the source task after training, stored in the strategy base, so as to accelerate the convergence of the QMIX algorithm. Simulation results show that the proposed algorithm is significantly better than the traditional heuristic method of allocation in terms of efficiency and has the same running time.

In this paper, the secrecy performance in internet of things (IoTs) networks with cooperative eavesdroppers has been investigated, where simultaneous wireless information and power transfer (SWIPT) nodes perform as relay nodes. The secrecy energy efficiency (SEE) maximization problem for single relay node has been introduced to extend the life-time for IoT nodes without power supply. The optimization problem is firstly transformed into two sub-problems, and then each sub-problem is solved by bisection method. Furthermore, the model has been extended into multiple relay nodes and a novel relay selection algorithm for SEE maximization has been proposed. Finally, the advantage of the proposed algorithm has been verified from simulations.

Drones are widely used in a number of key fields and are having a profound impact on all walks of life. Working out how to improve drone safety through fault detection is key to ensuring the smooth execution of tasks. At present, most research focuses on fault detection at the component level as it is not possible to locate faults quickly from the global system state of a UAV. Moreover, most methods are offline detection methods, which cannot achieve real-time monitoring of UAV faults. To remedy this, this paper proposes a fault detection method based on a fault mode database and runtime verification. Firstly, a large body of historical fault information is analyzed to generate a summary of fault modes, including fault modes at the system level. The key safety properties of UAVs during operation are further studied in terms of system-level fault modes. Next, a monitor generation algorithm and code instrumentation framework are designed to monitor whether a certain safety attribute is violated during the operation of a UAV in real time. The experimental results show that the fault detection method proposed in this paper can detect abnormal situations in a timely and accurate manner.

Existing software intelligent defect classification approaches don’t consider radar characters and prior statistics information. Thus when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15%~20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defecs effectively to improve the identifying adequacy of the defects in radar software.

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