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In the current research on the fatigue state of controllers using radiotelephony communication, most of them only consider the changes of voice in the time domain or frequency domain, while ignore that fatigue will affect the time domain and frequency domain at the same time. The voice of radiotelephony communication in the three fatigue states is converted into speech spectrum images that can reflect the characteristics of both the time domain and frequency domain, and the grayscale co-existence matrix is used to extract the typical feature parameters in four dimensions. The changes of the characteristic parameters of the controllers in different states are compared, and the controller fatigue detection model is constructed to detect the input features. The results show that the detection accuracy of using the spectrogram features combined with the traditional features as the input features is the highest(95.49%), which is 4% higher than that of the traditional features alone. The change of controllers fatigue state can intuitively reflected on the spectrogram and haves an impact on its eigenvalues, and the better detection results can be obtained by using this influence to detect the controllers fatigue state.

In this paper, a data-driven framework aimed at investigating how weather factors affect the spatio-temporal patterns of air traffic flow in the terminal maneuvering area (TMA) is presented. The framework mainly consists of three core modules, namely, trajectory structure characterization, flow pattern recognition, and association rule mining. To fully characterize trajectory structure, abnormal trajectories and typical operations are sequentially extracted based on a deep autoencoder network with two specially designed loss functions. Then, using these extracted elements as basic components to further construct and cluster per-hour-level descriptions of airspace structure, the spatio-temporal patterns of air traffic flow can be recognized. Finally, the association rule mining technique is applied to find sets of weather factors that often appear together with each flow pattern. Experimental analysis is demonstrated on two months of arrival flight trajectories at Hong Kong International Airport (HKIA). The results clearly show that the proposed framework effectively captures spatial anomalies, fine-grained trajectory structures, and representative flow patterns. More importantly, it also reveals that those flow patterns with non-conforming behaviors result from complex interactions of various weather factors. The findings provide valuable insights into the causal relationships between weather factors and changes in flow patterns, greatly enhancing the situational awareness of TMA. [ABSTRACT FROM AUTHOR]