scholarly journals AIS Data Case Study : Selecting Design Vessels for New Jersey Back Bays Storm Surge Barriers Study

2021 ◽  
Author(s):  
Martin A. Kress ◽  
Samuel J. Weintraub
Keyword(s):  
New Jersey ◽  
Storm Surge ◽  
Atlantic Coast ◽  
Hurricane Sandy ◽  
Technical Note ◽  
Automatic Identification ◽  
Identification System ◽  
The North ◽  
Position Data

The purpose of this Coastal and Hydraulics Engineering technical note (CHETN) is to describe how historic Automatic Identification System (AIS) vessel position data were used to identify a design vessel for use in a storm surge barrier design study. Specifically, this CHETN describes how the AIS data were accessed, how the universe of vessel data was refined to allow for design vessel selection, and how that selection was used in a storm surge barrier (SSB) study. This CHETN draws upon the New Jersey Back Bays Coastal Storm Risk Management Feasibility Study (USACE-NAP 2019), specifically the Appendix B.2 Engineering Appendix Civil document1. The New Jersey Back Bays Study itself builds upon the work of the North Atlantic Coast Comprehensive Study (NACCS) initiated after Hurricane Sandy in 2012 (USACE 2015a).

scholarly journals ADAPTATION PATHWAY FOR A BARRIER ISLAND TO FUTURE HURRICANES

2018 ◽  
pp. 52
Author(s):  
Stephanie Smallegan ◽  
Evan Mazur
Keyword(s):  
Storm Surge ◽  
Atlantic Coast ◽  
Barrier Island ◽  
Hurricane Sandy ◽  
Water Levels ◽  
Storm Damage ◽  
Sea Levels ◽  
The North ◽  
Adaptation Pathway ◽  
Comprehensive Study

The numerical model XBeach is used to simulate hydrodynamics and morphological change of Bay Head, NJ, which is located on a developed barrier island. Bay Head is fronted with a seawall buried beneath its dunes, and the seawall has been shown to mitigate damage due to storm surge and waves during Hurricane Sandy (2012). The objective of this study is to re-evaluate the effectiveness of the seawall in mitigating damage from a synthetic storm and sea level rise, and refine an adaptation pathway previously created for Bay Head. Utilizing the wave and surge data generated from the North Atlantic Coast Comprehensive Study, synthetic Storm 391 is simulated using XBeach. Model results show the seawall is overtopped by storm surge and waves, causing overwash and reducing dune heights. As sea levels rise, the backbarrier region of the barrier island is severely eroded and the seawall acts as a barrier preventing elevated bay water levels from freely flowing across the island and into the ocean, exacerbating sediment transport on the backbarrier. To fully evaluate the capabilities and limitations of the seawall in mitigating storm damage, additional synthetic storms need to be simulated and the results re-evaluated. This will, in turn, lead to a comprehensive, more robust adaptation pathway for Bay Head.

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

scholarly journals Positioning Error Analysis of Ranging-Mode Using AIS Signals in China

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Zheng ◽  
Qing Hu ◽  
Jingbo Zhang
Keyword(s):  
Measurement Errors ◽  
Phase Locking ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Positioning Error ◽  
North East ◽  
The North ◽  
Error Distributions ◽  
Shift Keying

In order to provide resilient position, navigation, and time (PNT) information forE-Navigation, the ranging-mode (R-Mode) positioning using automatic identification system (AIS) signals is encouraged. As the accuracy is the key for the positioning system, this paper investigates the position error of the R-Mode positioning based on AIS shore-based station in China. The measurement errors of Gaussian filtered minimum shift keying (GMSK) demodulation based on carrier phase locking loop are investigated in theory. The dilution of precision (DOP) for time of arrival (TOA) and time difference of arrival (TDOA) used in R-Mode positioning of AIS is discussed in two measurement mechanisms. The positioning error distributions in the North, East, and South Sea regions of China based on the existing AIS shore-based stations are evaluated. The positioning accuracy is at the meter level in the most traffic dense areas to meet the requirements for vessel navigation.

Research on Ship Classification Based on Trajectory Features

2017 ◽  
Vol 71 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Kai Sheng ◽  
Zhong Liu ◽  
Dechao Zhou ◽  
Ailin He ◽  
Chengxu Feng
Keyword(s):  
Pattern Mining ◽  
Extraction Methods ◽  
Behaviour Pattern ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Trajectory Features ◽  
Ship Classification ◽  
Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

The Automated Risk Estimation for the Navigation of Autonomous Ships by Learning with Navigation Feature

2020 ◽  
pp. 2041003
Author(s):  
Wei Chian Tan ◽  
Kie Hian Chua ◽  
Yanling Wu
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Risk Estimation ◽  
Nearest Neighbor Search ◽  
Mathematical Representation ◽  
Automatic Identification ◽  
Identification System ◽  
North West ◽  
The North ◽  
Neighbor Search

This work presents a data-driven approach for the automated risk estimation of the voyage of a vessel or ship. While the industry is moving from a compliance-based framework with existing rules to a risk-based one, there is also a need to monitor the risk of a vessel from the perspective of the navigation. This is of even higher importance for the case of autonomous ships. Built based on the state-of-the-art mathematical representation, the navigation feature, each existing voyage is transformed into a corresponding series of points in [Formula: see text]-dimensional space. During the stage of pre-processing, given a set of historical Automatic Identification System (AIS) data, those records that belong to the same vessel within a certain period of time are taken as a voyage and mapped to the corresponding space of the navigation feature. After the pre-processing and during the online monitoring, the current trajectory of the vessel is transformed into the corresponding representation in the same way. Based on a nearest-neighbor search scheme, the distance from the nearest neighbor is taken as the risk of the current voyage. In other words, the deviation from the closest route in the historical data is taken as the risk. The developed method has demonstrated encouraging performance on a set of challenging historical AIS data from the Australian Maritime Safety Authority, covering three regions in the Australian territory, namely, the Bass Strait, the Great Australian Bight and the North West.

scholarly journals Automatic Identification of Environment Haptic Properties

1999 ◽  
Vol 8 (4) ◽  
pp. 394-411 ◽  
Author(s):  
Pierre E. Dupont ◽  
Capt. Timothy M. Schulteis ◽  
Paul A. Millman ◽  
Robert D. Howe
Keyword(s):  
Haptic Feedback ◽  
Sensory Information ◽  
Automatic Identification ◽  
Identification System ◽  
General Description ◽  
Research Issues ◽  
Property Identification ◽  
Remote Environment ◽  
Force Data

Many applications can be imagined for a system that processes sensory information collected during telemanipulation tasks in order to automatically identify properties of the remote environment. These applications include generating model-based simulations for training operators in critical procedures and improving real-time performance in unstructured environments or when time delays are large. This paper explores the research issues involved in developing such an identification system, focusing on properties that can be identified from remote manipulator motion and force data. As a case study, a simple block-stacking task, performed with a teleoperated two-fingered planar hand, is considered. An algorithm is presented that automatically segments the data collected during the task, given only a general description of the temporal sequence of task events. Using the segmented data, the algorithm then successfully estimates the weight, width, height, and coefficient of friction of the two blocks handled during the task. This data is used to calibrate a virtual model incorporating visual and haptic feedback. This case study highlights the broader research issues that must be addressed in automatic property identification.

Analysis of Tugboat Activities using AIS Data for the Tianjin Port

2020 ◽  
Vol 2674 (5) ◽  
pp. 498-509
Author(s):  
Shukai Chen ◽  
Feng Wang ◽  
Xiaoyang Wei ◽  
Zhijia Tan ◽  
Hua Wang
Keyword(s):  
Spatial Distribution ◽  
Spatial Analysis ◽  
Service Time ◽  
Automatic Identification ◽  
Identification System ◽  
Crucial Importance ◽  
Two Stage ◽  
Temporal And Spatial ◽  
Shed Light

The tugboat is the vessel that helps to maneuver large ships for berthing and un-berthing operations. To achieve efficient tugboat operations, investigating the features of tugboat activities is of crucial importance. This study aims to use automatic identification system (AIS) data to identify the maneuver services and analyze the characteristics of tugboat activities. A two-stage algorithm is developed to extract the time, locations, and involved tugboats for berthing and un-berthing operations from AIS data. The AIS data from Tianjin port, China, are used in the case study to demonstrate the effectiveness of the proposed method and analyze the pattern of tugboat activities. First, some important features of tugboat jobs are presented, such as the daily number of jobs and the spatial distribution of jobs. Then, a temporal and spatial analysis is conducted to investigate tugboat assignment, service time, tugboat utilization, and locations of berthing and un-berthing operations. The obtained results and implications could shed light on the deployment of tugboat berths, tugboat scheduling, and evaluation of tugboat fleet operation.

scholarly journals A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints

2017 ◽  
Vol 75 (3) ◽  
pp. 988-998 ◽  
Author(s):  
Jennifer L Shepperson ◽  
Niels T Hintzen ◽  
Claire L Szostek ◽  
Ewen Bell ◽  
Lee G Murray ◽  
...  
Keyword(s):  
Missing Data ◽  
Monitoring System ◽  
Spline Interpolation ◽  
Automatic Identification ◽  
Identification System ◽  
Fishing Activity ◽  
Fishing Vessels ◽  
Position Data ◽  
Hermite Spline ◽  
Data Coverage

Abstract Understanding the distribution of fishing activity is fundamental to quantifying its impact on the seabed. Vessel monitoring system (VMS) data provides a means to understand the footprint (extent and intensity) of fishing activity. Automatic Identification System (AIS) data could offer a higher resolution alternative to VMS data, but differences in coverage and interpretation need to be better understood. VMS and AIS data were compared for individual scallop fishing vessels. There were substantial gaps in the AIS data coverage; AIS data only captured 26% of the time spent fishing compared to VMS data. The amount of missing data varied substantially between vessels (45–99% of each individuals' AIS data were missing). A cubic Hermite spline interpolation of VMS data provided the greatest similarity between VMS and AIS data. But the scale at which the data were analysed (size of the grid cells) had the greatest influence on estimates of fishing footprints. The present gaps in coverage of AIS may make it inappropriate for absolute estimates of fishing activity. VMS already provides a means of collecting more complete fishing position data, shielded from public view. Hence, there is an incentive to increase the VMS poll frequency to calculate more accurate fishing footprints.

scholarly journals Simulation of Computation Time of Ship Traffic Analysis using Data Clustering for a Vessel Traffic System

2021 ◽  
Vol 1 (4) ◽  
pp. 41-50
Author(s):  
Rahimuddin ◽  
Mehdi Nakisa ◽  
Haryanti Rivai ◽  
Hasnawiyah Hasan ◽  
Husni Sitepu ◽  
...  
Keyword(s):  
Warning System ◽  
Computation Time ◽  
Critical Issue ◽  
Traffic Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Ship Traffic ◽  
Traffic System ◽  
The North ◽  
Cluster Area

Indonesian water territory is a crossroads of international shipping lanes connecting countries bordering the South China Sea in the north, countries in southern, and countries from western Indonesia. The shipping route goes through the Sunda Strait, Sumbawa Strait, Makassar Strait and Melaka Strait. The shipping lanes are grouped into three main routes called ALKI (Alur Laut Kepulauan Indonsia) I, II, and III. The increase in total number of ships per year with various capacities tends to increases the traffic in the lane and the risk of ship collision. Using AIS (Automatic Identification System) technology with a warning system required to prevent the effect of accidents. Through the Vessel Traffic System (VTS) receive, record, and analysis the traffic data in the covered area. Computation time become a critical issue for ship traffic assessment in the lane. A numerical simulation program developed and analysed the computation time of ship traffic analysis using cluster area method. The result showed a shorter computation time for a more number of cluster area. The computation time increase quadratically with an increase of ships number or objects.

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