Shichen Li

Bio: Shichen Li is an academic researcher from Wuhan University of Technology. The author has contributed to research in topics: Trajectory & Autoencoder. The author has an hindex of 1, co-authored 3 publications receiving 10 citations.

AIS-Based Vessel Trajectory Reconstruction with U-Net Convolutional Networks

Abstract: The vessel trajectory data indicated by the Automatic Identification System (AIS) is important and useful in maritime data analysis, navigational safety and maritime risk assessment. However, the raw trajectory data contains noise, missing data and other errors which can lead to a wrong conclusion. Therefore, it is essential to develop a vessel trajectory reconstruction method, which is meaningful for enhancing the applicability of vessel trajectory and improving the navigation safety. In recent years, there have been many studies about vessel trajectory reconstruction, but the performance of these methods will degrade when they are faced with curved trajectories with high loss rate. In this paper, we propose a novel trajectory reconstruction method via U-net. Benefiting from the architecture of U-net, this method makes great use of historical trajectories and takes advantage of the rich skip connections in this network which help copy low-level features to corresponding high-level features. Consequently, this method is robust to the trajectories with different sampling rates, missing points, and noisy data. In addition, the proposed method is tested and compared with cubic spline interpolation. The results show that our method is capable of higher accuracy than the cubic spline interpolation especially when the trajectories are curved and have a high loss rate.

Vessel Trajectory Similarity Measure Based on Deep Convolutional Autoencoder

Abstract: With the Widespread use of Internet of Thing (IoT) technology and the extensive application of wireless communication technologies, innovational changes have been made in all walks of life. In the field of navigation, Automatic Identification System (AIS) is widely equipped in vessels, which can obtain continuous position data from vessels and assemble them to vessel trajectories. While the massive vessel trajectory data obtained through AIS make great difficulty in maritime data analysis, however, they also make the vessel trajectory similarity measure become a hot topic in spatial database research. In recent years, there have been many studies about trajectory similarity measure of maritime, but these methods only consider the calculation of relative position between trajectory points, and these methods are inefficient with a low accuracy of trajectory similarity measure. In this study, we propose a novel approach called Convolutional Autoencoder (CAE), for measuring vessel trajectory similarity based on Convolutional Neural Network (CNN) and Autoencoder (AE). In this model, the vessel trajectory was gridded, and the grid-based Convolutional Autoencoder was proposed to extract the trajectory data as feature vectors to represent the original trajectories. Then the low-dimensional feature vectors were used for estimating the original trajectory similaiity. In addition, an expeiiment was conducted to prove the effectiveness of our model. Compared with the Frechet distance and Dynamic Time Warping (DTW distance with the CAE, the results prove that CAE is capable of more efficient trajectory similarity computation and search.

Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things

Abstract: The maritime Internet of Things (IoT) has recently emerged as a revolutionary communication paradigm where a large number of moving vessels are closely interconnected in intelligent maritime networks. However, the tremendous growth of vessel trajectories, collected from the combined satellite-terrestrial AIS (automatic identification system) base stations, could lead to unsatisfactory maritime safety and efficacy. To promote smart traffic services in maritime IoT, it is necessary to accurately and robustly predict the spatiotemporal vessel trajectories. It is beneficial for collision avoidance, maritime surveillance, and abnormal behavior detection, etc. Motivated by the strong learning capacity of deep neural networks, this work proposes an AIS data-driven trajectory prediction framework, whose main component is a long short term memory (LSTM) network. In particular, the vessel traffic conflict situation modeling, generated using the dynamic AIS data and social force concept, is embedded into the LSTM network to guarantee high-accuracy vessel trajectory prediction. In addition, a mixed loss function is reconstructed to make our prediction results more reliable and robust in different navigation environments. Several quantitative and qualitative experiments have been implemented on realistic AIS-based vessel trajectories. Our results have demonstrated that the proposed method could achieve satisfactory prediction performance in terms of accuracy and robustness.

STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network

Abstract: The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base stations, are of considerable importance for promoting traffic situation awareness and vessel traffic services, etc. To guarantee traffic safety and efficiency, it is essential to robustly and accurately predict the AIS-based vessel trajectories (i.e., the future positions of vessels) in maritime IoT. In this work, we propose a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm. Our STMGCN is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to closest point of approach, and the size of surrounding vessels. These three graphs are then jointly embedded into the prediction framework by introducing the spatio-temporal multigraph convolutional layer. To further enhance the prediction performance, the self-attention temporal convolutional layer is proposed to further optimize STMGCN with fewer parameters. Owing to the high interpretability and powerful learning ability, STMGCN is able to achieve superior prediction performance in terms of both accuracy and robustness. The reliable prediction results are potentially beneficial for traffic safety management and intelligent vehicle navigation in MEC-enabled maritime IoT.

Random vector functional link neural network based ensemble deep learning for short-term load forecasting

Abstract: Electricity load forecasting is crucial for the power systems' planning and maintenance. However, its un-stationary and non-linear characteristics impose significant difficulties in anticipating future demand. This paper proposes a novel ensemble deep Random Vector Functional Link (edRVFL) network for electricity load forecasting. The weights of hidden layers are randomly initialized and kept fixed during the training process. The hidden layers are stacked to enforce deep representation learning. Then, the model generates the forecasts by ensembling the outputs of each layer. Moreover, we also propose to augment the random enhancement features by empirical wavelet transformation (EWT). The raw load data is decomposed by EWT in a walk-forward fashion, not introducing future data leakage problems in the decomposition process. Finally, all the sub-series generated by the EWT, including raw data, are fed into the edRVFL for forecasting purposes. The proposed model is evaluated on twenty publicly available time series from the Australian Energy Market Operator of the year 2020. The simulation results demonstrate the proposed model's superior performance over eleven forecasting methods in three error metrics and statistical tests on electricity load forecasting tasks.

Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery

Abstract: Owing to the space–air–ground integrated networks (SAGIN), seaborne shipping has attracted increasing interest in the research on the motion behavior knowledge extraction and navigation pattern mining problems in the era of maritime big data for improving maritime traffic safety management. This study aims to develop a novel unsupervised methodology for feature extraction and knowledge discovery based on automatic identification system (AIS) data, allowing for seamless knowledge transfer to support trajectory data mining. The unsupervised hierarchical methodology is constructed from three parts: trajectory compression, trajectory similarity measure, and trajectory clustering. In the first part, an adaptive Douglas–Peucker with speed (ADPS) algorithm is created to preserve critical features, obtain useful information, and simplify trajectory information. Then, dynamic time warping (DTW) is utilized to measure the similarity between trajectories as the critical indicator in trajectory clustering. Finally, the improved spectral clustering with mapping (ISCM) is presented to extract vessel traffic behavior characteristics and mine movement patterns for enhancing marine safety and situational awareness. Comprehensive experiments are conducted and implemented in the Chengshan Jiao Promontory in China to verify the feasibility and effectiveness of the novel methodology. Experimental results show that the proposed methodology can effectively compress the trajectories, determine the number of clusters in advance, guarantee the clustering accuracy, and extract useful navigation knowledge while significantly reducing the computational cost. The clustering results are further explored and follow the Gaussian mixture distribution, which can help provide new discriminant criteria for trajectory clustering.