Optical fiber sensor and assembly method for measuring the tensile strain of a nickel-based directionally solidified superalloy in a high-temperature environment.

Nickel-based superalloys are widely used in key hot-end components such as aero engines and industrial gas turbines due to their excellent comprehensive properties. Real-time monitoring of engine blades and other structures in high-temperature environments can promptly discover possible internal damage to the structure. Optical fiber sensing technology has unique advantages that traditional electrical sensors do not have, such as anti-electromagnetic interference, small size, light weight, and corrosion resistance. The technology is gradually replacing traditional methods and becoming an important means of structural health monitoring. We propose an optical fiber sensor and assembly method that can be used to measure the strain of a nickel-based directionally solidified superalloy in a high-temperature environment more accurately. The proposed technology is simple to manufacture and also has low cost and a high survival rate, which is of great significance for high-temperature strain measurements in aerospace and other fields.

A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning.

Despite the emergence of various human-robot collaboration frameworks, most are not sufficiently flexible to adapt to users with different habits. In this article, a Multimodal Reinforcement Learning Human-Robot Collaboration (MRLC) framework is proposed. It integrates reinforcement learning into human-robot collaboration and continuously adapts to the user's habits in the process of collaboration with the user to achieve the effect of human-robot cointegration. With the user's multimodal features as states, the MRLC framework collects the user's speech through natural language processing and employs it to determine the reward of the actions made by the robot. Our experiments demonstrate that the MRLC framework can adapt to the user's habits after repeated learning and better understand the user's intention compared to traditional solutions.

A Massage Area Positioning Algorithm for Intelligent Massage System.

A growing number of studies have been conducted over the past few years on the positioning of daily massage robots. However, most methods used for research have low interactivity, and a systematic method should be designed for accurate and intelligent positioning, thus compromising usability and user experience. In this study, a massage positioning algorithm with online learning capabilities is presented. The algorithm has the following main innovations: (1) autonomous massage localization can be achieved by gaining insights into natural human-machine interaction behavior and (2) online learning of user massage habits can be achieved by integrating recursive Bayesian ideas. As revealed by the experimental results, combining natural human-computer interaction and online learning with massage positioning is capable of helping people get rid of positioning aids, reducing their psychological and cognitive load, and achieving a more desirable positioning effect. Furthermore, the results of the analysis of user evaluations further verify the effectiveness of the algorithm.