Application of Broad Learning System for Container Number Identification Due to the Information Technologies (ITs) and Computer Technologies (CTs) have been dramatically developed in recent years, harbor cities pay more attentions on implementing the smart ports. In such kind of ports, the containers loading and uploading are almost autonomous, this can sufficiently enhance throughput ability and improve the management efficiency. To address this issue, automatic and correct identification of containers number is the bottleneck, and also the key technology. The container number characters are usually deformed or missing due to influences caused by rain, fog, oil stains, and creases on the surface of the containers, which would influence the recognition accuracy rate. Therefore, this study introduces a novel method named Broad Learning System (BLS) for identification of the container number characters. To compare with other methods, our algorithm presents fast training speed and high testing accuracy, which makes it more suitable for container number identification in practice.

Forecast Application of Time Series Model Based on BLS in Port Cargo Throughput During the last decade, there was a dramatic increasing of container throughput in China, especially in server harbor cities such as Shanghai and Shenzhen. It is a necessary and crucial task to enhance the ability of port throughput. In the existing studies, timeseries model is one of the most powerful methods to solve this problem, which can predict the container cargo throughput accurately and effectively. Based on this technical background, this paper employs a novel algorithm to design a new type of timeseries model for predicting port throughput. In the experiments, we firstly use the Matlab to pursue the statistical analyses on the throughput data. Secondly, we apply our method to predict the changing rate of container throughput, and compare the results with several classic timeseries models. Finally, the experimental results show that our method was optimized based on the training data, and outperformed other timeseries models in the prediction of 10 months throughput.