In this paper, by introducing the Software Defined Networking (SDN) controller, we conduct traffic scheduling under dual connectivity (DC) pattern to support handover management in heterogeneous aeronautical network. Firstly, we construct a stochastic optimization problem of handover overhead with queue backlog, under the condition of ensuring user fairness and limited resource constraints. Then, we utilize the Lyapunov optimization theory to transform the stochastic optimization problem into a deterministic optimization problem, and propose the feasible and optimal strategies. Finally, we verify the feasibility and effectiveness of those strategies on handover management by simulations in two different aeronautical scenarios.

In this paper, we explore the effect of dual connectivity (DC) on delay-aware resource control with statistical Quality of Service (QoS) provisioning in heterogeneous aeronautical networks. Firstly, the Software Defined Networking (SDN) controller is introduced to support the DC for aircraft by centralized management of different base stations. In the light of the delay analysis of user traffic, the delay-aware resource control of SDN controller is established as a multi-objective stochastic optimization problem to guarantee the statistical QoS provisioning. Secondly, the multi-objective stochastic optimization problem is transformed into a weighted single-objective optimization and relaxed by the Lagrangian relaxation. Then, the Lyapunov optimization theory is utilized to solve the relaxed stochastic optimization for optimal resource control. Finally, the performance of resource control under DC mode is verified in two aeronautical scenarios.

Currently, the existing datalink selection algorithms in heterogeneous aeronautical network unilaterally focus on the user side or network side and lack the two sides’ cooperation, which seriously affects the utilization of available datalinks. Firstly, to reflect the user‐side requirements and network‐side load, we propose a user‐centric datalink selection scheme combing the user‐side multiattribute utility and the network‐side access rate for single‐user selection. It can be established as a multiobjective optimization problem and solved by the selected optimal datalink. Secondly, for multiuser datalink selection, in order to guarantee the access requests of multiple users, the central control mode of Software Define Networking (SDN) is used to assist the user to perform datalink selection on the network side. By constructing multiuser multiobjective optimization problem, a priority distinction selection (PDS) algorithm is proposed to maximize the number of users accessing their optimal datalinks and minimize the modification of the users’ access request. Finally, the proposed user‐side datalink selection scheme and network‐side assisted selection algorithm are simulated to verify the feasibility and effectiveness.

Current sensors always play a very important role in the power industry. For example, current sensors can provide the key information for measurement, control and relay protection. However, when the economic further develops and the level of current increases year by year, it is very difficult to meet the demand for current sensors based on conventional technology which is still the main stream. Novel current sensors always are pursued. A research focusing on the current sensor is the technology of fiber optic current sensor, because there is high resistance to electromagnetic interference in fiber optic sensors. Fiber Bragg grating (FBG) sensors have been applied in many fields and have gained great achievements. It is of great help to the current measurement if FBG current sensors are realized. In this paper, a novel FBG high-current sensor is developed based on magnetic coupling. The principle is described, such as the magnetic coupling, the structure of the sensor and the sensing data processing. Experiments are carried out, and the results show at least 60 pm wavelength shift with the change of 100A and 2563 pm wavelength shift when the current is 750A and a good repeatability. The results are in agreement with the principle and indicate that the proposed sensor is capable of measuring both dc and ac current.

Spatio-temporal information is crucial to human action recognition. Inspired by the primary visual cortex of the human brain, a new end-to-end action recognition method based on deep learning was proposed to extract pure spatiotemporal information in videos. It is named moving human focus inference model that can capture long-term temporal information with low time costs and eliminates the interference of dynamic background. It obtains the temporal and spatial information in the video from the temporal pathway with the focus block and spatial pathway, respectively. Then the temporal and spatial information are merged as spatio-temporal information by fusion region. Finally, the softmax layer is used to recognize the actions, followed by the bayes inference block that is used to correct the recognition results through the methods of maximum posterior probability inference. The model is evaluated on two challenge datasets: UCF101 and HMDB51. The experimental results show that the approach has better accuracy and generalization ability compared to the state-of-the-art methods.