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 >

Yajing Zhang¹, Chaoyang Song², Zhiguo Li^2,*

Energy Engineering, Vol.120, No.2, pp. 501-510, 2023, DOI:10.32604/ee.2022.021423 - 28 November 2022

Abstract To overcome the problems of natural decreases in power quality, and to eliminate wind speed fluctuation due to wind shear and tower shadow effect arising from wind turbine structural parameters, an improved prediction model accounting for the dual effect of wind shear and tower shadow is, in this paper, built. Compared to the conventional prediction model, the proposed model contains a new constraint condition, which makes the disturbance term caused by the tower shadow effect always negative so that the prediction result is closer to the actual situation. Furthermore, wind turbine structural parameters such as More > Graphic Abstract