A consistent framework was presented to understand and explain SMS characteristics in more detail on the basis of the SV conflict theory, which is expected to be more advantageous in SMS prediction, prevention, and training.

No abstract

Results suggest that visual information may be inefficient and unreliable for body orientation and stabilization in a rotating visual scene, while reprioritizing preferences for different sensory cues was dynamic and asymmetric between individuals. The present findings should facilitate customization of efficient and proper training for astronauts with different sensory prioritization preferences and dynamic characteristics.Chen W, Chao J-G, Zhang Y, Wang J-K, Chen X-W, Tan C. Orientation preferences and motion sickness induced in a virtual reality environment. Aerosp Med Hum Perform. 2017; 88(10):903-910.

The use of deep learning for image segmentation has proven to be an efficient and accurate method, but with the complexity of the network structure, it takes up a lot of computing resources. The consumption of computing resources may be unacceptable during tasks. Aiming at this problem, a fast and light segmentation network (FLSNet) is proposed, which uses the Encoder-Decoder method to extract features. All convolutional layers use depthwise separable convolutions and the channel attention module is linked between Encoder and Decoder. Experiments are performed on the autonomous driving dataset CamVid. The results show that with a slight increase in segmentation accuracy, the model size becomes 8.65% of SegNet, the required computing resources are reduced by a dozen times, and the segmentation speed is increased by about 12%, which show that our network is efficient.

Signal scaling is an essential step in spaceflight simulation. Thus far, the third-order polynomial scaling method has been widely used for signal scaling; however, in this method, parameter tuning is complicated and may induce perceptible distortion during large-range monotonic signal scaling. In the simulation of spacecraft return, specifically, that of re-entry, acceleration and angular velocity signals may vary considerably over short time periods. Motion perception is important for training astronauts in this phase. In this study, two strategies are proposed to solve these problems using the 'scaling scope' parameter. The first strategy is based on the Hermite interpolation polynomial, and the other is based on thirdorder polynomial scaling. Two methods were developed which make use of the stable region of third-order polynomial scaling. The first method maximizes the stable region to prevent signal distortion, and the other restricts the scaling scope in the stable region. Based on the dynamic characteristics of spacecraft in the return phase, the signal scaling strategies proposed in this study are simulated for trainees' perception in a motion-base simulator. Simulations were implemented by utilizing the full curves of spacecraft return phase for the first time, and results show that these methods are more advantageous for parameter tuning and can eliminate signal distortion for all input signals. While these methods have a shortcoming in that the trigger velocity (onset cue) is slowed down, this shortcoming is eliminated by employing the moving cueing algorithm. Both the strategies proposed in this article show good performance and can be applied potentially to the motion simulation of the spacecraft return phase.