Jingxian Liu

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

Trajectory compression-guided visualization of spatio-temporal AIS vessel density

As an automatic tracking system, the shipboard Automatic Identification System (AIS) has been widely adopted to identify and locate the vessels by electronically exchanging data with other nearby ships. With the development of computer technology, AIS-based visualization of vessel traffic has attracted increasing attention during the past several years. The vessel density visualization can be used as an appropriate computer-aided method to better understand the maritime traffic situation and (abnormal) vessel behaviors. However, it often suffers from high computational cost due to the massive sample size of spatio-temporal AIS trajectories datasets. To handle the problem of high computational cost, the Douglas-Peucker (DP) algorithm was firstly introduced to simplify the massive AIS trajectories. The final Kernel Density Estimation (KDE)-based vessel density visualization was implemented based on the simplified trajectory datasets to shorten the visualization time. To guarantee a good balance between AIS trajectory simplification and visualization performance, numerous experiments haven been conducted to optimally select an appropriate threshold for DP-based AIS trajectory simplification. Comprehensive experiments on realistic spatio-temporal datasets have illustrated that our proposed method can achieve a satisfactory visualization of AIS vessel density while reducing the visualization time.

Hierarchical and coupling model of factors influencing vessel traffic flow

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Single-image blind deblurring with hybrid sparsity regularization

Single-image blind deblurring could be considered as an important preprocessing step in imaging information fusion. Its purpose is to simultaneously estimate blur kernel and latent sharp image from only one observed blurred image. Blind deblurring has been attracting increasing attention in the fields of image processing, computer vision, computational photography, etc. However, it is a typically ill-posed inverse problem, which requires regularization methods to guarantee stable image restoration results. We first proposed to robustly estimate the blur kernels by exploiting non-convex sparsity constraints on image gradients and blur kernels. The corresponding combined non-convex regularization term has the capacity of enhancing estimation accuracy. To guarantee the high-quality non-blind deblurring with estimated blur kernels, the hybrid non-convex first- and second-order TV regularizer was then introduced to stabilize the final image restoration process. The hybrid non-convex regularizer is able to achieve a good balance between sharp edges preservation and undesirable artifacts suppression. The resulting non-convex minimization problems related to blur kernel estimation and non-blind deblurring were handled using efficient numerical optimization algorithms in this paper. Numerous experiments on both synthetic and realistic images have demonstrated the good performance of the proposed blind deblurring method.

Data-Driven Statistical Analysis of Dynamic Vessel Trajectories in Wuhan Section of the Yangtze River

Automatic identification system (AIS), which records the spatio-temporal dynamic vessel trajectories, has recently attracted increasing attention due to its great potential in maritime management and ocean engineering. AIS has been successfully utilized to assist intelligent navigation and enhance transportation safety. To better understand the vessel behavior behind the massive AIS trajectories, it is necessary to analyze the statistical properties of AIS trajectories from different aspects. In this work, we mainly focus on the distribution of vessel speeds, distribution of spherical distances, distribution of longitude and latitude differences between successive trajectory points for both downstream and upstream vessels in Wuhan Section of the Yangtze River. To accurately approximate these distributions, Gaussian mixture model (GMM) was introduced to analyze the dynamic vessel trajectories. In particular, the optimal parameters of GMM were estimated using the iterative Expectation-Maximization (EM) algorithm. Experiments on massive realistic vessel trajectories have demonstrated that there exists significantly different distributions of vessel speeds, spherical distances, longitude and latitude differences between downstream and upstream vessels.

Nonconvex Second-Order Variational Image Denoising Model with Adaptive Selection of Regularization Parameters

Image denoising is a typically ill-conditioned inverse problem, which has attracted much attention in the fields of image processing and computer vision. In order to overcome the ill-conditioned nature of this inverse problem, a nonconvex total generalized variation (NTGV)-regularized variational model was proposed in this paper for edge-preserving image denoising. The introduced NTGV regularizer is capable of restoring the degraded images while preserving the fine image details. To further improve the image quality, a local variance-based estimation method was introduced to automatically compute the spatially variant regularization parameters, which can make a good balance between noise suppression and detail preservation. A numerical optimization algorithm based on Alternating Direction Method of Multipliers was proposed to effectively solve the resulting image restoration model. The numerical experiments have been conducted to compare the proposed model with current state-of-the-art image denoising methods. The experimental results have illustrated the good performance of the proposed method.

Model of Prediction and Statistics on Time Pattern of Traffic Flow

Time pattern of traffic flow is one of the important features of vessel traffic flow, and indicates the aggregation and dispersion degree, and reflects the risk of the vessel traffic flow. The model is constructed for doing statistical analysis and prediction to the time pattern of traffic flow by Mathematical statistics and Theory of Forecasting. The prediction model can be used to forecast the future time pattern of traffic flow by parameters of the statistic model. And the time pattern of traffic flow of Tianjin Port is statistically analyzed and forecasted to judge risk of time pattern of traffic flow. 1. FOREWORD Time pattern of traffic flow is one important feature of vessel traffic flow, and