Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming and impractical. Although the cross-dataset fatigue detection model does not need to be retrained, no one has studied this problem previously. Therefore, this study will focus on the design of the cross-dataset fatigue detection model.Methods: This study proposes a regression method for EEG-based cross-dataset fatigue detection. This method is similar to self-supervised learning and can be divided into two steps: pre-training and the domain-specific adaptive step. To extract specific features for different datasets, a pretext task is proposed to distinguish data on different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. Moreover, the maximum mean discrepancy (MMD) is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. In addition, the attention mechanism is introduced to extract continuous information on spatial features, and the gated recurrent unit (GRU) is used to capture time series information.Results: The accuracy and root mean square error (RMSE) achieved by the proposed method are 59.10% and 0.27, respectively, which significantly outperforms state-of-the-art domain adaptation methods.Discussion: In addition, this study discusses the effect of labeled samples. When the number of labeled samples is 10% of the total number, the accuracy of the proposed model can reach 66.21%. This study fills a vacancy in the field of fatigue detection. In addition, the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research practices.
As a key part of the solid rocket motor, the nozzle always withstands a series of problems such as high-temperature, high-pressure, and chemical erosion and faces more severer environmental challenges. So its Stability plays a vital role in the normal operation of the engine. This paper takes the engine nozzle throat lining as the research object, uses the ANSYS to establish a carbon/carbon composite throat lining model in the ACP module, and analyzes the stress and displacement of the throat lining in a high-temperature and high-pressure environment. The experimental results show that under the high-temperature condition of 3500°C, the maximum stress value is 2734.8MPa, and more than 96% of the area is below 1000MPa, lower than the prescribed stress threshold. The superiority in the performance of the throat lining prepared for carbon/carbon materials is verified, and the structural integrity of the throat lining can still be guaranteed under high-temperature conditions.
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