Mr. Kezhi Zhang Profile

Kezhi Zhang

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Proceedings Article | 12 November 2024 Open Access

Horizontal scale study of low-level wind shear based on F-factor

Qi Wang, Hong Sun, Kezhi Zhang, Qing Wang

Proceedings Volume 13395, 133952P (2024) https://doi.org/10.1117/12.3049231

KEYWORDS: Wind speed, Statistical analysis, Safety, Radar, LIDAR, Hazard analysis, Data acquisition, Calibration, Signal processing, Nose

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Low-level wind shear is an important weather phenomenon that affects flight safety, and understanding the horizontal scale of low-level wind shear is highly beneficial for wind shear-related research and flight training. Considering the lack of statistics and research in this area in China, this paper proposes a calibration method for the horizontal scale of low-level wind shear based on the F-factor. First, the vertical wind speed is calculated from various flight parameters recorded in the Quick Access Recorder (QAR) data, and then the F-factor is obtained. Second, the wind shear danger period for triggering reactive wind shear warning segments is determined based on the threshold value (±0.105) proposed by the FAA. Third, the horizontal scale of low-level wind shear is estimated by calculating the distance that the aircraft flies during the danger period of the wind shear. Finally, the QAR data from 147 flights triggering reactive wind shear warnings are statistically analyzed, and the mean value of the common horizontal scale of low-altitude wind shear was estimated to be approximately 517 m, with a standard deviation of approximately 135 m.

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Proceedings Article | 9 January 2024 Paper

Naming conventions-based multi-label and multi-task learning for fine-grained classification

Qinbang Zhou, Kezhi Zhang, Feng Yue, Zhaoliang Zhang, Hui Yu

Proceedings Volume 12969, 129691D (2024) https://doi.org/10.1117/12.3014589

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This paper proposes a fine-grained image classification architecture using multi-task learning. The structure of the fine-grained classification network uses ResNest as the feature extraction layer of the multi-task hard parameter sharing mode with the fine-grained category label regression branch based on multi-hot naming conventions and classification branch based on cross-entropy loss with one-hot encoding. The coupling between the two branches enables multi-task classification through hyperparameter weighting. Subsequently, comparison and ablation experiments were performed on the public datasets of Stanford Cars, CUB-200-2011 and FGVC-Aircraft. The experimental result shows multi-label regression, multi-task learning and label smoothing can effectively improve the generalization of the model and increase the inter-class distance of the previous layer at the network output terminal, and reduces the intra-class distance.

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