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Akademický článek

Cable force estimation method based on UAVs and EMD

Autor: Luo Wenjun, Cai Shufan, Zeng Limei, Chen Gongfa, Bassir David

Publikováno v: International Journal for Simulation and Multidisciplinary Design Optimization, Vol 15, p 14 (2024)

To solve the problems of difficulty in deploying accelerometers in traditional cable force measurement, this paper proposes a cable force measurement method based on unmanned aerial vehicles (UAVs). This method first uses the Kanade-Lucas-Tomasi (KLT

Externí odkaz: https://doaj.org/article/f83b19dda8b34318ba99a3a02fa5b99a

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Akademický článek

Investigation on road underground defect classification and localization based on ground penetrating radar and Swin transformer

Autor: An Jinke, Yang Li, Hao Zhongyu, Chen Gongfa, Li Longjian

Publikováno v: International Journal for Simulation and Multidisciplinary Design Optimization, Vol 15, p 7 (2024)

In response to the low detection efficiency and accuracy of traditional manual methods for detecting road underground defects, this paper proposes an intelligent detection method based on ground penetrating radar (GPR). This method integrates the det

Externí odkaz: https://doaj.org/article/d6e736d8b4f04a8ebdfe256407a73c11

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Akademický článek

Application of artificial intelligence and machine learning for BIM: review

Autor: Bassir David, Lodge Hugo, Chang Haochen, Majak Jüri, Chen Gongfa

Publikováno v: International Journal for Simulation and Multidisciplinary Design Optimization, Vol 14, p 5 (2023)

Quality control is very important aspect in Building Information Modelling (BIM) workflows. Whatever stage of the lifecycle it is important to get and to follow building indicators. The BIM it is very data consuming field and analysis of these data r

Externí odkaz: https://doaj.org/article/4871db54b56a450a9cb16981c175bfbc

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Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion

Autor: Situ, Zuxiang, Wang, Qi, Teng, Shuai, Feng, Wanen, Chen, Gongfa, Zhou, Qianqian, Fu, Guangtao

Publikováno v: Journal of Hydrology 630 (2024) 130743

Deep learning models have become increasingly popular for flood prediction due to their superior accuracy and efficiency compared to traditional methods. However, current machine learning methods often rely on separate spatial or temporal feature ana

Externí odkaz: http://arxiv.org/abs/2304.09994