Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment.

Identification of flight accidents causative factors base on SHELLO and improved entropy gray correlation method.

In order to effectively identify the key causative factors of civil aviation flight accidents, and establish a forward-looking effective prevention mechanism for flight accidents. Firstly, Corrected SHELLO model is established to classify the causes of civil aviation accidents in China (2015-2019) based on the integration of SHELL analysis model and Reason organization system concept. Secondly, in view of the randomness and uncertainty gray characteristics of the flight accidents inducing factors, the improved entropy gray correlation algorithm is established for the purpose of importance recognition, which combined with the characteristics of the data sample of inducement classification.

Effects of full windshield head-up display on visual attention allocation.

This study explores the impact of full windshield head-up display (FHUD) cues on the visual attention allocation of drivers under different scenarios. Forty-eight participants with driving experience were randomly divided into two groups and asked to drive on a preset route in various virtual driving scenarios created in advance. The full windshield highlights situational cues related to the driving task, such as lane lines, safe vehicle distance warnings, navigation guidance and pedestrian cues.

Flight safety assessment based on an integrated human reliability quantification approach.

Human error is an important risk factor for flight safety. Although the human error assessment and reduction technique (HEART) is an available tool for human reliability derivation, it has not been applied in flight safety assessment. The traditional HEART suffers from imprecise calculation of the assessed proportion of affect (APOA) because it heavily depends on a single expert's judgment.

Bird-Strike Resistance of Composite Laminates with Different Materials.

To obtain some basic laws for bird-strike resistance of composite materials in aeronautical application, the high-velocity impact behaviors of composite laminates with different materials were studied by numerical methods. The smoothed particle hydrodynamics (SPH) and finite element method (FEM) coupling models were validated from various perspectives, and the numerical results were comparatively investigated. Results show that the different composite materials have relatively little effect on projectile deformations during the bird impact.

Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot's Workload Condition.

To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot's real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension.