Afaq Khattak^1,*, Pak-wai Chan², Feng Chen³, Abdulrazak H. Almaliki⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1565-1584, 2025, DOI:10.32604/cmes.2025.059914 - 27 January 2025

Abstract Intense wind shear (I-WS) near airport runways presents a critical challenge to aviation safety, necessitating accurate and timely classification to mitigate risks during takeoff and landing. This study proposes the application of advanced Residual Network (ResNet) architectures including ResNet34 and ResNet50 for classifying I-WS and Non-Intense Wind Shear (NI-WS) events using Doppler Light Detection and Ranging (LiDAR) data from Hong Kong International Airport (HKIA). Unlike conventional models such as feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), ResNet provides a distinct advantage in addressing key challenges such as capturing intricate… More >

Kunigal Shivakumar

The International Conference on Computational & Experimental Engineering and Sciences, Vol.20, No.1, pp. 19-20, 2011, DOI:10.3970/icces.2011.020.019

Abstract The vision of the Center for Aviation Safety (CAS) is to establish a strong aerospace engineering research and education program by bringing together highly competent and dedicated faculty members at North Carolina A&T State University (A&T) through the NASA funding to address the challenges in composite aircraft structures, fire safety, hazards of airflow, and the alternate fuels. The research goals of CAS will be accomplished through an understanding of science and engineering to developing new materials, tools, models, and technologies that support the safe operation of aerospace vehicles. Research goal is being accomplished through three More >