Maritime accidents in the Yangtze River: A time series analysis for 2011-2020.

The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.

Meso-level carbon dioxide emission model based on voyage for inland ships in the Yangtze River.

To simplify the micro-level CO2 (carbon dioxide) emission calculation model, reduce the dataset quality requirement of the model, and cut down the volume of calculation, a meso-level voyage-based emission model (MeVEM) for inland ships is proposed with their navigation characteristics considered. The navigation characteristics and the main influencing factors of inland ship emissions are analyzed. The main engine power and average speed of the ships are selected as the model inputs. Accurate CO2 emissions are calculated by the use of the micro-level emission model. With that, first-order and second-order polynomial regression models are employed to establish the fitting formula to estimate the emissions per kilometer. To validate the proposed model, the Junshan segment in the middle reaches of the Yangtze River is selected as the study area, and the model parameters are determined to estimate the CO2 emissions. It is found that the model of emission per kilometer (ej, k) established by second-order polynomial regression is more accurate. The results show that the percentage error in the total amount (PETA) of the results estimated by the four proposed models (CO2 emission estimation model for the upstream cargo ships, the downstream cargo ships, the upstream oil tankers, and the downstream oil tankers) are all within ±5%, which verifies the feasibility and applicability of the model. The proposed meso-level model allows us to use a smaller input dataset which is easier to obtain, and estimate CO2 emissions from ships simply and accurately.

Inland ship emission inventory and its impact on air quality over the middle Yangtze River, China.

Shipping emissions have been considered a significant source of air pollution in the cities along the Yangtze River, with severe impacts on the climate and human health. This study created a complete annual ship emission inventory for the middle reaches of the Yangtze River and assessed its impact on air quality on a regional scale. To estimate the complete emissions, 9 main engine power regression models for different ship types were created to handle those vessels with absent main power data, and a high spatial-temporal resolution annual emission inventory was developed with the activity-based method combined with Automatic Identification System (AIS) data of the full year of 2018. The total emissions of CO2, CO, SO2, NOX, PM2.5, PM10 and HC in middle reaches of the Yangtze River were 5.67 × 105, 1.02 × 103, 5.41 × 102, 1.06 × 104, 2.43 × 102, 2.45 × 102 and 3.52 × 102 tons respectively. Then, the Weather Research and Forecasting with Chemistry (WRF-Chem) model was used to study the dispersion of the ship pollutants in the atmosphere and quantize the impact on the urban area. This research will provide services for the maritime authorities to develop green shipping and emission supervision.

Single-Vessel Plume Dispersion Simulation: Method and a Case Study Using CALPUFF in the Yantian Port Area, Shenzhen (China).

To study the impact of vessel pollution on the atmospheric environment of the surrounding area, we present a numerical simulation method based on regional emissions inventories. The general spatial resolution is ≥1 km and the temporal resolution is ≥1 h; parameters which are suitable for the study of larger space-time scales. In this paper, the WRF/CALMET/CALPUFF model and Automatic Identification System (AIS) data are employed to develop a single-vessel atmospheric pollution diffusion model. The goal of this research uses existing meteorological models and diffusion models to provide a simulation technology method for studying the diffusion of SO2 from a single ship. We take the outgoing phase of ocean-going container vessels in Yantian Port as an example. It can be used to set the position of sensitive receptors near the port area. Simulations are implemented with CALPUFF and the results are compared with data derived from on-site monitoring instrument. The CALPUFF modelling domain covers an area of 925 km2 with a grid spacing of 500 m. The simulation results demonstrated agreement with the measured data. The ground concentration contribution value ranged from 10 to 102 µg/m3, while the affected area was about 4-26 km2 and the high-value area of the ground concentration contribution was distributed within 1-2 km from the ship track. Emissions generated by the vessels represent a considerable contribution to SO2 pollution around the harbor areas.

Path following control for towing system of cylindrical drilling platform in presence of disturbances and uncertainties.

Towing is a critical process to deploy a cylindrical drilling platform. However, the towing process faces a great variety of risks from a complex nautical environment, the dynamics in towing and maneuvering, to unexpected events. Therefore, safely navigating the towing system following a planned route to a target sea area is essential. To tackle the time-varying disturbances induced by wind, current and system parametric uncertainties, a path following control method for a towing system of cylindrical drilling platform is designed based on linear active disturbance rejection control. By utilizing Maneuvering Modeling Group model as well as a catenary model, we develop a three degree-of-freedom dynamic mathematical model of the towing system under external environmental disturbances and internal uncertainties. Furthermore, we design a linear active disturbance rejection control path following controller for real-time tracking error correction based on a guidance method combining cross-track error and parallax. Finally, the path following performance of the towing system is evaluated in a simulation environment under various disturbances and internal uncertainties, where the corresponding tracking error is analyzed. The results show that the linear active disturbance rejection control performs well under both the external disturbance and inherent uncertainties, and better satisfy the tracking performance criteria than a traditional proportional-integral-derivative controller.

Autonomous Visual Perception for Unmanned Surface Vehicle Navigation in an Unknown Environment.

Robust detection and recognition of water surfaces are critical for autonomous navigation of unmanned surface vehicles (USVs), since any none-water region is likely an obstacle posing a potential danger to the sailing vehicle. A novel water region visual detection method is proposed in this paper. First, the input image pixels are clustered into different regions and each pixel is assigned a label tag and a confidence value by adaptive multistage segmentation algorithm. Then the resulting label map and associated confidence map are fed into a convolutional neural network (CNN) as training samples to train the network online. Finally, the online trained CNN is used to segment the input image again but with greater precision and stronger robustness. Compared with other deep-learning image segmentation algorithms, the proposed method has two advantages. Firstly, it dispenses with the need of manual labeling training samples which is a costly and painful task. Secondly, it allows real-time online training for CNN, making the network adaptive to the navigational environment. Another contribution of this work relates to the training process of neuro network. An effective network training method is designed to learn from the imperfect training data. We present the experiments in the lake with a various scene and demonstrate that our proposed method could be applied to recognize the water region in the unknown navigation environment automatically.

A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification.

In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel's vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision.