Liner shipping hub location and empty container repositioning: Use of foldable containers and container leasing

Wang, Qian; Zheng, Jianfeng; Lu, Bo

doi:10.1016/j.eswa.2023.121592

Cited by 3 publications

(1 citation statement)

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“…With the rapid growth of international and domestic trade volume, the loading and unloading operations of containers have become increasingly busy [1,2], emphasizing the importance of safety in port operations [3]. One typical issue in these operations is mistakenly hoisting trucks when unloading containers with cranes.…”

Section: Introductionmentioning

confidence: 99%

Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading

Wu,

Hu,

Liu

et al. 2024

Sensors

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Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting.

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Section: Introductionmentioning

confidence: 99%