The development of hydrogen sensors with high detection accuracy, fast response times, long calibration periods, and good stability has become the focus of the space station environmental control and life support subsystem. We analyze the current research status of different types of hydrogen sensors, including catalyst combustion type, heat conduction type, semiconductor type, fiber optic type, etc. The response signals of most hydrogen sensors are affected by temperature and humidity, resulting in cross-sensitivity. Reducing the cross-sensitivity of temperature, humidity, and other interfering factors to achieve accurate hydrogen measurement in different environments is a challenge that limits the development of hydrogen sensors. Several hydrogen sensors that are currently commercially available have a narrow operating temperature range, most of them can only measure at room temperature, and high-temperature environments require a higher accuracy and lifetime of the sensor than required at room temperature. Many new hydrogen-sensitive materials were developed to improve the performance of the sensors. The excellent performance of fiber-optic hydrogen sensors is beneficial to temperature compensation and distributed multiparameter measurement, as well as to the research and development of intelligent sensing systems, in the context of the Internet of Things. The signal detection and demodulation techniques of fiber-optic sensors are the focus of future hydrogen sensor research.

An exoskeleton is a kind of intelligent wearable device with bioelectronics and biomechanics. To realize its effective assistance to the human body, an exoskeleton needs to recognize the real time movement pattern of the human body in order to make corresponding movements at the right time. However, it is of great difficulty for an exoskeleton to fully identify human motion patterns, which are mainly manifested as incomplete acquisition of lower limb motion information, poor feature extraction ability, and complicated steps. Aiming at the above consideration, the motion mechanisms of human lower limbs have been analyzed in this paper, and a set of wearable bioelectronics devices are introduced based on an electromyography (EMG) sensor and inertial measurement unit (IMU), which help to obtain biological and kinematic information of the lower limb. Then, the Dual Stream convolutional neural network (CNN)-ReliefF was presented to extract features from the fusion sensors’ data, which were input into four different classifiers to obtain the recognition accuracy of human motion patterns. Compared with a single sensor (EMG or IMU) and single stream CNN or manual designed feature extraction methods, the feature extraction based on Dual Stream CNN-ReliefF shows better performance in terms of visualization performance and recognition accuracy. This method was used to extract features from EMG and IMU data of six subjects and input these features into four different classifiers. The motion pattern recognition accuracy of each subject under the four classifiers is above 97%, with the highest average recognition accuracy reaching 99.12%. It can be concluded that the wearable bioelectronics device and Dual Stream CNN-ReliefF feature extraction method proposed in this paper enhanced an exoskeleton’s ability to capture human movement patterns, thus providing optimal assistance to the human body at the appropriate time. Therefore, it can provide a novel approach for improving the human-machine interaction of exoskeletons.

Studies on hydrogen leakage have mainly focused on the influences of location and geometrical configuration on the distribution of the hydrogen in various spaces. The present study developed a simplified model for the leakage diffusion of hydrogen in an enclosed cuboid space with two vents, at the top and at the bottom, respectively. The effect of different leakage positions on the diffusion of the hydrogen was analyzed. The results showed that when hydrogen diffused vertically from the bottom to the top of the space, the farther the leakage position was from the vent on the side wall, the more hydrogen accumulated. When the hydrogen leaked in the vertical direction from the floor, the distance between the leakage position and the bottom vent had little effect on the horizontal diffusion speed of the hydrogen at the top of the space. The diffusion speed for the leakage in the horizontal direction was faster than that in the vertical direction. When the hydrogen leaked in the horizontal direction from the side wall, the height of the leakage had little effect on the horizontal diffusion speed of the hydrogen at the top of the space. Stratification occurred for models set up in the present study whenever the envelope of 1% mole fraction, or 4% mole fraction, of hydrogen extended to the whole ceiling.

For the sake of accomplishing the rapidity, safety and consistency of obstacle avoidance for a large-scale unmanned aerial vehicle (UAV) swarm in a dynamic and unknown 3D environment, this paper proposes a flocking control algorithm that mimics the behavior of starlings. By analyzing the orderly and rapid obstacle avoidance behavior of a starling flock, a motion model inspired by a flock of starlings is built, which contains three kinds of motion patterns, including the collective pattern, evasion pattern and local-following pattern. Then, the behavior patterns of the flock of starlings are mapped on a fixed-wing UAV swarm to improve the ability of obstacle avoidance. The key contribution of this paper is collective and collision-free motion planning for UAV swarms in unknown 3D environments with dynamic obstacles. Numerous simulations are conducted in different scenarios and the results demonstrate that the proposed algorithm improves the speed, order and safety of the UAV swarm when avoiding obstacles.