Digital image transmission plays a very significant role in information transmission, so it is very important to protect the security of image transmission. Based on the analysis of existing image encryption algorithms, this article proposes a new digital image encryption algorithm based on the splicing model and 1D secondary chaotic system. Step one is the algorithm of this article divides the plain image into four sub-parts by using quaternary coding, and these four sub-parts can be coded separately. Only by acquiring all the sub-parts at one time can the attacker recover the useful plain image. Therefore, the algorithm has high security. Additionally, the image encryption scheme in this article used a 1D quadratic chaotic system, which makes the key space big enough to resist exhaustive attacks. The experimental data show that the image encryption algorithm has high security and a good encryption effect.

Inducing civil aviation aircraft to bumpiness, atmospheric turbulence is a typical risk that seriously threatens flight safety. The Eddy Dissipation Rate (EDR) value, as an aircraft-independent turbulence severity indicator, is estimated by a vertical wind-based or aircraft vertical acceleration-based algorithm. Based on the flight data of civil aviation aircraft, the vertical turbulence component is obtained as the input of both algorithms. A new method of computing vertical acceleration response in turbulence is put forward through the Unsteady Vortex Lattice Method (UVLM). The lifting surface of the target aircraft is assumed to be a combination of wing and horizontal tail in a turbulent flight scenario. Vortex rings are assigned on the mean camber surface, forming a non-planar UVLM, to further improve the accuracy. Moreover, the neighboring vortex lattices are placed as close as possible to the structural edge of control surfaces. Thereby, a complete algorithm for estimating vertical acceleration and in situ EDR value from Quick Access Recorder (QAR) flight data is proposed. Experiments show that the aerodynamic performance is computed accurately by non-planar UVLM. The acceleration response by non-planar UVLM is able to track the recorded acceleration data with higher accuracy than that of the linear model. Different acceleration responses at different locations are also obtained. Furthermore, because the adverse effects of aircraft maneuvers are separated from turbulence-induced aircraft bumpiness, the new acceleration-based EDR algorithm shows better accuracy and stability.

Pilot fatigue detection based on physiological signals is practical for aviation safety. Current methods face challenges in balancing the high computational cost of deep learning models with robust accuracy, especially when integrating short-term multimodal physiological signals. To address these challenges, this paper proposes a framework for fast, accurate, and robust pilot fatigue detection by fusing features from electroencephalogram (EEG) and electrocardiogram (ECG) signals. The primary novelty of this work lies in a streamlined selection and classification strategy that overcomes the intrinsic limitations of Heart Rate Variability (HRV) analysis in short (2-s) segments while maintaining competitive accuracy at a drastically lower training cost. Specifically, by utilizing statistical ECG features, which are then integrated with EEG markers through a two-stage ANOVA-SVM feature selection process. The optimized, low-dimensional feature set is then classified using an XGBoost model. Evaluated on data from 32 pilots, the framework demonstrated robust generalization with an accuracy of 88.42% in rigorous cross-subject cross-validation, significantly outperforming our previous EEG-only ASFT-Transformer. While standard cross-clip validation yielded a higher accuracy of 98.36%, the cross-subject metric highlights the model's potential utility for unseen individuals. Crucially, the framework achieves this performance with an average training time of only 39.3 s, a drastic reduction compared to mainstream deep learning models. By striking a balance between accuracy, generalization, and efficiency, this study presents a promising and feasible approach for objective pilot fatigue management.

Objective evaluation of pilot fatigue is crucial for enhancing aviation safety. Although electroencephalography (EEG) is regarded as an effective tool for recognizing pilot fatigue, the direct application of deep learning models to raw EEG signals faces significant challenges due to issues such as massive data volume, excessively long training time, and model overfitting. Moreover, existing feature-based methods often suffer from data redundancy due to the lack of effective feature and channel selections, which compromises the model’s recognition efficiency and accuracy. To address these issues, this paper proposes a framework, named ASFT-Transformer, for fast and accurate detection of pilot fatigue. This framework first extracts time-domain and frequency-domain features from the four EEG frequency bands. Subsequently, it introduces a feature and channel selection strategy based on one-way analysis of variance and support vector machine (ANOVA-SVM) to identify the most fatigue-relevant features and pivotal EEG channels. Finally, the FT-Transformer (Feature Tokenizer + Transformer) model is employed for classification based on the selected features, transforming the fatigue recognition problem into a tabular data classification task. EEG data is collected from 32 pilots before and after actual simulator training to validate the proposed method. The results show that ASFT-Transformer achieved average accuracies of 97.24% and 87.72% based on cross-clip data partitioning and cross-subject data partitioning, which were significantly superior to several mainstream machine learning and deep learning models. Under the two types of cross-validation, the proposed feature and channel selection strategy not only improved the average accuracy by 2.45% and 8.07%, respectively, but also drastically reduced the average training time from above 1 h to under 10 min. This study offers civil aviation authorities and airline operators a tool to manage pilot fatigue objectively and effectively, thereby contributing to flight safety.

Air-data, recorded in the flight data of civil aviation aircraft, can be disturbed by wind disturbance, thus leading to measurement error. A two-stage air-data estimation in wind disturbance was studied to obtain accurate true airspeed, angle of attack, and sideslip angle based on flight data. To separate the prevailing wind from the wind disturbance, the first stage involved the preliminary air-data optimization by the immune clone algorithm (ICA) and prevailing wind optimization by the Gauss-Newton algorithm. In the second stage, a new filtering system combining air-data and the von Kármán turbulence model was built with the initial value provided by the first stage. A weighted adaptive extended Kalman filtering (WAEKF) algorithm was proposed, in which an exponential weighting in the innovation covariance matrix was used to reduce the estimation error further. Simulation results indicate that the optimized initial value provided by the first stage is fundamental to ensuring the convergence rate and stability. The WAEKF algorithm with initial value (WAEKF-INIT) can improve the estimation accuracy and alleviate the effects of uncertain measuring noise. A further test with flight data shows that weighted adaptive filtering is capable of reducing the estimation error further in uncertain disturbance.