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During flight, pilots need to receive a large amount of information in a short time and make correct judgments and decisions. The cognitive processes such as perception, judgment and decision-making will be affected by excessive cognitive load and affect flight safety. Firstly, the physiological data of flight cadets during different flight missions are obtained through flight simulation experiments; Then, the characteristics of RESP (respiratory) and ECG(Electrocardiograph) signals are extracted by time-domain and frequency-domain analysis, and the indexes that can reflect the level of cognitive load are selected by statistical methods. Finally, combined with support vector machine, K-nearest neighbor, artificial neural network and other methods, an ensemble learning model is established to evaluate the flight cadets′ cognitive load. Furthermore, it is compared with single algorithms. The results show that the flight cadets′ cognitive load evaluation model established in this paper has a high accuracy rate and can better reflect the flight cadets′ cognitive load level.

Flight simulation training is one of the most important methods in early-stage civil aviation flight training. In this regard, flight simulation competitions are effective tools for evaluating the flight skills of trainees. In this study, a model is developed for evaluating the flight skills of trainees by integrating GBDT (Gradient Boosting Decision Tree), PSO (Particle Swarm Optimization), and CNNs (Convolutional Neural Networks). Flight data from simulations is employed for model training. Initially, performance data and scores are gathered from a simulated flight competition platform. The GBDT algorithm is then applied to filter and identify essential flight parameters from the collected data. Subsequently, the PSO-CNN model is utilized to train on the extracted flight parameters. The proposed GBDT-PSO-CNN model achieves a recognition rate of 93.8% on the test dataset. This assessment system is of significant importance for improving the specific maneuvering skill level of flight trainees. [ABSTRACT FROM AUTHOR]

In this paper, to better evaluate the flight performance of pilot cadets, a flight performance evaluation index system was constructed based on the task of the traffic pattern, the flight training manual, and interviews with instructors. The fuzzy comprehensive evaluation model established by the G1 method was used to evaluate the flight performance of pilot cadets. The flight data of 30 flight cadets were collected to verify the applicability of the fuzzy comprehensive evaluation model. The results showed that the index system established in this paper can meet the requirements of flight performance evaluation. In addition, the fuzzy comprehensive evaluation results were consistent with the evaluation results of experts. Therefore, the system is effective and feasible for the evaluation of pilot cadets' flight performance through the fuzzy comprehensive evaluation model established by the index system and G1 method. [ABSTRACT FROM AUTHOR]

Cognitive load is generated by pilots in the process of information cognition about aircraft control, and it is closely related to flight safety. Cognitive load is the physiological and psychological need that a pilot produces when completing a mission. Therefore, it is meaningful to study the dynamic identification of the cognitive load of the pilot under the complex human-aircraft-environment interaction. In this paper, the airfield traffic pattern flight simulation experiment was designed and used to obtain the ECG physiological and NASA-TLX psychological data. The wavelet transform preprocessing and mathematical statistics analysis were applied on them, respectively. Furthermore, the Pearson correlation analysis method is used to select the characteristic indicators of psycho-physiological data after preprocessing. Based on the psycho-physiological characteristic indicators, the pilot's cognitive load identification model is constructed by combining RNN and LSTM. The results of this study are more accurate compared with the cognitive load identification models established by other methods such as RNN neural network and support vector machine. This research is able to provide a useful reference for preventing and reduction of human error caused by the cognitive load during flight missions. It will be potential to realize intelligent control of aircraft cockpit, improving the flight control behavior and maintaining flight safety., Author(s): Haibo Wang [1]; Naiqi Jiang [1]; Ting Pan [1]; Haiqing Si (corresponding author) [1]; Yao Li [1]; Wenjing Zou [1] 1. Introduction With the rapid development of intelligent aircraft [...]

Soil organic matter (SOM) content is one of themost important indicators of soil quality and hence theproductive capacity of soils. Northeast China (NEC) is themost important region in grain production in China. Inthis study,we assessed the spatiotemporal change of croplandSOM content in NEC using sampling data of 2005 andsurvey data of 1985. We also analysed the driving forcesbehind the SOM content change. Our results showed thatSOM content decreased in 39% of all the cropland in NEC,while increase in SOM content was only detected on 16%of the cropland. SOM remained unchanged in nearly half(i.e. 45%) of the cropland. Our results also revealed thatcropping intensity and fertilizer application were the twomost important factors driving SOM change. Overall, resultsfrom this research provided novel details of the spatiotemporalpatterns of cropland SOM content change inNEC which was not revealed in earlier assessments. Thedatasets presented here can be used not only as baselinesfor the calibration of process-based carbon budget models,but also to identify regional soil quality hotspots andto guide spatial-explicit soil management practices.

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Based on the present semi-empirical model of numerically predicting aerodynamic noise from wind turbine, effects of .several important parameters on aerodynamic noise from wind turbine are studied in the paper. To better simulate vortex-shedding noise near the tip of blade, tip loss correction is added in the present semi-model at the domain of blade tip. Using the noise predicting model developed in the paper, four different important parameters including blade airfoil, tip pitch angle, rotor angular speed and tail edge bluntness are mainly discussed in the paper, where the pattern of noise level with the frequency and A-weighted total sound power level and sound pressure level are also analyzed in detail. These influential patterns of the different aerodynamic parameters can provide technical references for the design and manufacture of low noise wind turbine, which helps to the study on the reduction of aerodynamic noise from wind turbine. [ABSTRACT FROM AUTHOR]

An advanced time approach of Ffowcs Williams-Hawkings (FW-H) acoustic analogy is developed, and the integral equations and integral solution of FW-H acoustic analogy are derived. Compared with the retarded time approach, the transcendental equation need not to be solved in the advanced time approach, on the other hand, computational cost can be saved using the approach due to no demand of pre-storing lots of aerodynamic data. To further validate the efficiency of the advanced time approach for predicting noise, unsteady flow fields are firstly simulated for air around square cylinder and NACA0012 airfoil, then unsteady calculations are used as input for FW-H equations, and numerical predictions are made for noise induced by vortex shedding of square cylinder and NACA0012 airfoil using the advanced time approach. Finally, the retarded time approach and the advanced time approach are compared. [ABSTRACT FROM AUTHOR]

The lattice Boltzmann method (LBM) is studied and extended into computational aeroacoustics (CAA), and a non-equilibrium extrapolation scheme for LBM is applied to treating wall boundary and an absorbing boundary condition is used to prevent wave reflection at the far field boundary. Firstly, LBM code is validated by simulating the lid-driven cavity flow. Then, several benchmark problems in CAA are numerically studied and comparisons between LBM and conventional four-order dispersion-relation-preserving (DRP) scheme are also performed. The ability of LBM to simulate these problems is tested, which helps to further study aerodynamic noise from complex bodies. It is demonstrated that the calculated results by LBM for benchmark problems are in good agreement with the exact solution although the standard LBM has two-order accuracy in space and time, and LBM is an effective and feasible method for aero-acoustical calculations. [ABSTRACT FROM AUTHOR]

Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the excessive cognitive load will induce incorrect operations and even lead to flight accidents. Accordingly, the research on cognitive load is crucial to reduce errors and even accidents caused by human factors. By using physiological acquisition systems such as eye movement, ECG, and respiration, multi-source physiological signals of flight cadets performing different flight tasks during the flight simulation experiment are obtained. Based on the characteristic indexes extracted from multi-source physiological data, the CGAN-DBN model is established by combining the conditional generative adversarial networks (CGAN) model with the deep belief network (DBN) model to identify the flight cadets' cognitive load. The research results show that the flight cadets' cognitive load identification based on the CGAN-DBN model established has high accuracy. And it can effectively identify the cognitive load of flight cadets. The research paper has important practical significance to reduce the flight accidents caused by the high cognitive load of pilots.