Bo Jia

Quick access recorder (QAR) data have been used to evaluate pilot performance for decades. However, traditional evaluation methods suffer from the inability to consider multiple parameters simultaneously, and most of them need to select features manually in advance. To study the relationship between QAR data and pilot performance, this paper puts forward one-dimensional convolutional neural networks (1-D CNN) which consider QAR metrics in an integrated manner. This paper obtained indicators describing the operational status of an aircraft first. Then, the correlation between indicators and pilot performance (skill levels) was studied. Inspired by the fact that CNN can extract both local and global features, this paper has developed an approach to achieve the state-of-the-art result in pilot performance evaluation, which was tested on operating data of Boeing 737. The results prove that other methods do not work well, while the 1-D CNN improves the prediction accuracy of 5 pilot skill levels. Besides, when it is used on a binary classification problem, the result improves to 78.18%. Finally, the indicators were grouped into 5 common factors by factor analysis and fed into 1-D CNN in different combinations. Each common factor plays a different role in pilot performance evaluation, which can provide advice for the future.

The main factor affecting the accuracy of transfer alignment is: flexural deformation error. Due to the impact of waves and other external forces, the hull will deform to varying degrees, affecting the accuracy of information such as the output speed and attitude. When the deflection deformation reaches a certain level, which will affect the accuracy of transfer alignment. Therefore, deflection deformation is researched in this paper.

Workload assessment faces two major issues. That is, how to learn effective fatigue characteristics and how to find the potential state of the workload. This paper proposes a solution to assess the brain fatigue workload of pilots through an instantaneous spectral entropy feature and an infinitely warped model. The instantaneous characteristics of electroencephalography (EEG) signals are extracted by Hilbert transform, and Euclidean norm weighted permutation entropy is proposed. The infinitely warped model is a new automatic learning model for detecting arbitrary shapes of EEG data. In addition, we propose a rapid learning framework to learn mental fatigue by integrating Treelet transform and infinitely warped models. Compared to other state-of-the-art methods, our approach is better able to handle complex data in complex shapes. The experimental results show that this method can more effectively assess the brain fatigue of pilots.

Silicon (Si) nanomembranes (NMs) enable conformal covering on complicated surfaces for novel applications. We adopt classical fibers as flexible/curved substrates and wrap them with freestanding ultrathin Si-NMs with a thickness of ∼20 nm. Intrinsic defects in single-crystalline Si-NMs provide a flow path for hydrofluoric acid (HF) to release the NM with a consecutive area of ∼0.25 cm². Such Si-NMs with ultralow flexural rigidities are transferred onto a single-mode fiber (SMF) and functionalized into bendable photodetectors, which detects the leaked light when the fiber is bent. Our demonstration exemplifies optoelectronic applications in flexible photodetector for Si-NMs in a three-dimensional (3D) geometry.

Extreme learning machine (ELM) has achieved wide attention due to faster learning speed compared with conventional neural network models like support vector machine (SVM) and back-propagation (BP) networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM) based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm.