Wireless sensor networks (WSN) are widely used in various applications, such as environmental monitoring, healthcare, event detection, agriculture, disaster management, and so on. Due to their small size, sensors are limited power sources and are often deployed in special environments where frequent battery replacement is not feasible. Therefore, it is important to reduce the energy consumption of sensors and extend the network lifetime. An effective way to achieve this is clustering. This paper proposes a dual cluster-head energy-efficient algorithm (DCK-LEACH), which is based on K-means and Canopy optimization. Considering that the K-means algorithm is sensitive to the location of the initial clustering centers, this paper uses both the dynamic Canopy algorithm and K-means algorithm for clustering. For cluster-head election, this algorithm uses a hierarchy to minimize the cluster-head burden and balance the network load. The primary cluster-head is selected by two objectives: the node’s residual energy and the distance from the node to the clustering center. The vice cluster-head is selected by the residual energy of the node, and the distance from the nodes to the base station. Simulator results show that DCK-LEACH significantly prolongs the energy-critical node lifetime and the network lifetime compared with existing protocols.

Instructional videos are often a key component of online learning, and their quality significantly influences online learning outcomes and student satisfaction. However, instructional video evaluation is time-consuming. To solve this problem, this study developed an automatic evaluation method for instructional videos. This method first establishes a metric to evaluate instructional videos based on two aspects: video features and watching experience. An automatic scoring method for each indicator was developed based on video and clickstream data. Finally, all the scores were input into the evaluation model to obtain the evaluation result. Our experimental results showed that 85% of the evaluation results using our proposed model are consistent with manual quality evaluation. Therefore, our method can perform automatic evaluation of instructional videos while achieving acceptable accuracy, which is helpful in reducing the workload associated with manual evaluation and improving the quality of online teaching.

The tandem-rotor helicopters have high use value both in military and civilian fields. However, few studies have been conducted in order to develop controllers for tandem-rotor helicopters. Because the coupling between control channels is complicated and application context are usually with complex disturbance, so the robustness and adaptive ability of its control system is crucial. The brilliant performance of L ₁ adaptive control theory has been verified in the design of control system of fixed wing. In this paper, we build the mathematical model of tandem-rotor helicopter with stability augmentation system based on LQR method and design the control structure based on L ₁ adaptive control theory. We add artificial disturbance to the model to simulate the situation of parameter uncertainty and external interference, to verify the controller's tolerance of disturbance. The simulation results show that the L ₁ adaptive theory can be applied to design the control system of tandem-rotor helicopters and it will improve the deficiency of traditional adaptive controller and has good robustness and transient characteristics.