Objective. Brain-controlled robotic arms have shown broad application prospects with the development of robotics, science and information decoding. However, disadvantages, such as poor flexibility restrict its wide application. Approach. In order to alleviate these drawbacks, this study proposed a robotic arm asynchronous control system based on steady-state visual evoked potential (SSVEP) in an augmented reality (AR) environment. In the AR environment, the participants were able to concurrently see the robot arm and visual stimulation interface through the AR device. Therefore, there was no need to switch attention frequently between the visual stimulation interface and the robotic arm. This study proposed a multi-template algorithm based on canonical correlation analysis and task-related component analysis to identify 12 targets. An optimization strategy based on dynamic window was adopted to adjust the duration of visual stimulation adaptively. Main results. Experimental results of this study found that the high-frequency SSVEP-based brain–computer interface (BCI) realized the switch of the system state, which controlled the robotic arm asynchronously. The average accuracy of the offline experiment was 94.97 % , whereas the average information translate rate was 67.37 ± 14.27 bits·min−1. The online results from ten healthy subjects showed that the average selection time of a single online command was 2.04 s, which effectively reduced the visual fatigue of the subjects. Each subject could quickly complete the puzzle task. Significance. The experimental results demonstrated the feasibility and potential of this human-computer interaction strategy and provided new ideas for BCI-controlled robots.

No abstract

The clustering coefficients have been extensively investigated for analyzing the local structural properties of complex networks. In this paper, the clustering coefficients for triangle and square structures, namely [Formula: see text] and [Formula: see text], are introduced to measure the local structure properties for different degree-mixing pattern networks. Firstly, a network model with tunable assortative coefficients is introduced. Secondly, the comparison results between the local clustering coefficients [Formula: see text] and [Formula: see text] are reported, one can find that the square structures would increase as the degree [Formula: see text] of nodes increasing in disassortative networks. At the same time, the Pearson coefficient [Formula: see text] between the clustering coefficients [Formula: see text] and [Formula: see text] is calculated for networks with different assortative coefficients. The Pearson coefficient [Formula: see text] changes from [Formula: see text] to 0.98 as the assortative coefficient [Formula: see text] increasing from [Formula: see text] to 0.45, which suggests that the triangle and square structures have the same growth trend in assortative networks whereas the opposite one in disassortative networks. Finally, we analyze the clustering coefficients [Formula: see text] and [Formula: see text] for networks with tunable assortative coefficients and find that the clustering coefficient [Formula: see text] increases from 0.0038 to 0.5952 while the clustering coefficient [Formula: see text] increases from 0.00039 to 0.005, indicating that the number of cliquishness of the disassortative networks is larger than that of assortative networks.

No abstract

The fluctuation of product quality is closely related to the degradation of the equipment in multi-stage manufacturing systems. This paper proposes a critical measure approach for a quality-reliability coupled network of multi-stage manufacturing systems via network controllability. The impact of component degradation will be transmitted, expanded, and accumulated in multiple manufacturing stages, leading to quality flaws or even shutdowns of the entire system. An important measurement method via controllability analysis is provided by quantifying the impact of attacking the quality-reliability coupled network. By quantifying the control ability of the fault source node on the key quality attribute node, the weakness that affects the processing accuracy of a production line is identified. Case studies of real production lines are applied to verify the effectiveness, and comparative results show the method can guide the quality-reliability improvement of manufacturing systems.