scholarly journals Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data

2020 ◽  
Vol 9 (6) ◽  
pp. 351 ◽  
Author(s):  
Zhihuan Wang ◽  
Mengyuan Yao ◽  
Chenguang Meng ◽  
Christophe Claramunt
Keyword(s):  
Spatial Clustering ◽  
Infection Risk ◽  
Data Driven ◽  
Automatic Identification ◽  
Quick Response ◽  
Support Mechanism ◽  
Risk Levels ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
And Control

Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.

scholarly journals Modelling the Risk of Imported COVID-19 Infections at Maritime Ports Based on the Mobility of International-Going Ships

2022 ◽  
Vol 11 (1) ◽  
pp. 60
Author(s):  
Zhihuan Wang ◽  
Chenguang Meng ◽  
Mengyuan Yao ◽  
Christophe Claramunt
Keyword(s):  
Clustering Algorithm ◽  
Infection Risk ◽  
Data Driven ◽  
Risk Level ◽  
Automatic Identification ◽  
Identification System ◽  
Prevention Measures ◽  
Increase Rate ◽  
Spatio Temporal ◽  
Imported Infections

Maritime ports are critical logistics hubs that play an important role when preventing the transmission of COVID-19-imported infections from incoming international-going ships. This study introduces a data-driven method to dynamically model infection risks of international ports from imported COVID-19 cases. The approach is based on global Automatic Identification System (AIS) data and a spatio-temporal clustering algorithm that both automatically identifies ports and countries approached by ships and correlates them with country COVID-19 statistics and stopover dates. The infection risk of an individual ship is firstly modeled by considering the current number of COVID-19 cases of the approached countries, increase rate of the new cases, and ship capacity. The infection risk of a maritime port is mainly calculated as the aggregation of the risks of all of the ships stopovering at a specific date. This method is applied to track the risk of the imported COVID-19 of the main cruise ports worldwide. The results show that the proposed method dynamically estimates the risk level of the overseas imported COVID-19 of cruise ports and has the potential to provide valuable support to improve prevention measures and reduce the risk of imported COVID-19 cases in seaports.

scholarly journals An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 799
Author(s):  
Hyeong-Tak Lee ◽  
Jeong-Seok Lee ◽  
Hyun Yang ◽  
Ik-Soon Cho
Keyword(s):  
Human Error ◽  
Significant Contribution ◽  
Pattern Analysis ◽  
Spatial Clustering ◽  
Data Driven ◽  
Automatic Identification ◽  
Maritime Industry ◽  
Clustering Method ◽  
Dbscan Algorithm ◽  
Data Driven Approach

As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems’ data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port.

A Data-Driven Methodology for Fault Detection in Electromechanical Actuators

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Anthony J. Chirico ◽  
Jason R. Kolodziej
Keyword(s):  
Feature Extraction ◽  
Failure Modes ◽  
Extraction Process ◽  
Data Driven ◽  
Ball Screw ◽  
Fault Classification ◽  
Electromechanical Actuators ◽  
Rotating Machine ◽  
Data Driven Approach ◽  
And Control

This research investigates a novel data-driven approach to condition monitoring of electromechanical actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMAs typically operate under nonsteady conditions. The feature extraction process exposes fault frequencies in signal data that are synchronous with motor position through a series of signal processing techniques. A resulting reduced dimension feature is then used to determine the condition with a trained Bayesian classifier. The approach is based on signal analysis in the frequency domain of inherent EMA signals and accelerometers. For this work, two common failure modes, bearing and ball screw faults, are seeded on a MOOG MaxForce EMA. The EMA is then loaded using active and passive load cells with measurements collected via a dSPACE data acquisition and control system. Typical position commands and loads are utilized to simulate “real-world” inputs and disturbances and laboratory results show that actuator condition can be determined over a range of inputs. Although the process is developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.

scholarly journals In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5009 ◽  
Author(s):  
Stefania Tronci ◽  
Paul Van Neer ◽  
Erwin Giling ◽  
Uilke Stelwagen ◽  
Daniele Piras ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Continuous Process ◽  
Principal Component ◽  
Feedforward Neural Networks ◽  
Data Driven ◽  
Monitoring And Control ◽  
Soft Sensors ◽  
Continuous Processes ◽  
Data Driven Approach ◽  
And Control

The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

scholarly journals Data-driven approach for monitoring, protection, and control of distribution system assets using micro-PMU technology

2017 ◽  
Vol 2017 (1) ◽  
pp. 1011-1014 ◽  
Author(s):  
Emma Stewart ◽  
Michael Stadler ◽  
Ciaran Roberts ◽  
Jim Reilly ◽  
Dan Arnold ◽  
...  
Keyword(s):  
Distribution System ◽  
Data Driven ◽  
Data Driven Approach ◽  
Protection And Control ◽  
And Control

scholarly journals Evaluation of Aviation Emissions and Environmental Costs in Europe Using OpenSky and OpenAP

2021 ◽  
Vol 13 (1) ◽  
pp. 5
Author(s):  
Junzi Sun ◽  
Irene Dedoussi
Keyword(s):  
Data Driven ◽  
Time Interval ◽  
Environmental Costs ◽  
Continental Scale ◽  
Level Flight ◽  
Data Driven Approach ◽  
Emission Models ◽  
Historical Database ◽  
Aviation Emissions ◽  
Spatio Temporal

In this paper, we propose a data-driven approach that estimates cruise-level flight emissions over Europe using OpenSky ADS-B data and OpenAP emission models. Flight information, including position, altitude, speed, and the vertical rate are obtained from the OpenSky historical database, gathered at a sample rate of 15 s. Emissions from each flight are estimated at a 30-s time interval. This study makes use of the first four months of flights in 2020 over the major part of Europe. The dataset covers the period before and at the start of the COVID-19 pandemic. The aggregated results show cruise-level flight emissions by different airlines, geographic regions, altitudes, and timeframe (e.g., weeks). We also estimate environmental costs associated with aviation in Europe by using marginal cost values from the literature. Overall, we have demonstrated how open flight data from OpenSky can be employed to rapidly assess aviation emissions at varying spatio-temporal resolutions on a continental scale.

scholarly journals A triple-network organization for the mouse brain

2021 ◽  
Author(s):  
Francesca Mandino ◽  
Roël M. Vrooman ◽  
Heidi E. Foo ◽  
Ling Yun Yeow ◽  
Thomas A. W. Bolton ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Data Driven ◽  
Network Organization ◽  
Brain Disorder ◽  
Default Mode ◽  
Temporal Properties ◽  
Disease Condition ◽  
Data Driven Approach ◽  
Species Comparisons ◽  
Spatio Temporal

AbstractThe triple-network model of psychopathology is a framework to explain the functional and structural neuroimaging phenotypes of psychiatric and neurological disorders. It describes the interactions within and between three distributed networks: the salience, default-mode, and central executive networks. These have been associated with brain disorder traits in patients. Homologous networks have been proposed in animal models, but their integration into a triple-network organization has not yet been determined. Using resting-state datasets, we demonstrate conserved spatio-temporal properties between triple-network elements in human, macaque, and mouse. The model predictions were also shown to apply in a mouse model for depression. To validate spatial homologies, we developed a data-driven approach to convert mouse brain maps into human standard coordinates. Finally, using high-resolution viral tracers in the mouse, we refined an anatomical model for these networks and validated this using optogenetics in mice and tractography in humans. Unexpectedly, we find serotonin involvement within the salience rather than the default-mode network. Our results support the existence of a triple-network system in the mouse that shares properties with that of humans along several dimensions, including a disease condition. Finally, we demonstrate a method to humanize mouse brain networks that opens doors to fully data-driven trans-species comparisons.

scholarly journals Quantifying the Spatio-Temporal Process of Township Urbanization: A Large-Scale Data-Driven Approach

2019 ◽  
Vol 8 (9) ◽  
pp. 389
Author(s):  
Xinliang Liu ◽  
Yi Wang ◽  
Yong Li ◽  
Jinshui Wu
Keyword(s):  
Large Scale ◽  
Driving Forces ◽  
Data Driven ◽  
Population Migration ◽  
Urbanization Process ◽  
Large Scale Data ◽  
Temporal Process ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
Scale Data

The integrated recognition of spatio-temporal characteristics (e.g., speed, interaction with surrounding areas, and driving forces) of urbanization facilitates regional comprehensive development. In this study, a large-scale data-driven approach was formed for exploring the township urbanization process. The approach integrated logistic models to quantify urbanization speed, partial triadic analysis to reveal dynamic relationships between rural population migration and urbanization, and random forest analysis to identify the response of urbanization to spatial driving forces. A typical subtropical town was chosen to verify the approach by quantifying the spatio-temporal process of township urbanization from 1933 to 2012. The results showed that (i) urbanization speed was well reflected by the changes of time-course areas of urban cores fitted by a four-parameter logistic equation (R2 = 0.95–1.00, p < 0.001), and the relatively fast and steady developing periods were also successfully predicted, respectively; (ii) the spatio-temporal sprawl of urban cores and their interactions with the surrounding rural residential areas were well revealed and implied that the town experienced different historically aggregating and splitting trajectories; and (iii) the key drivers (township merger, elevation and distance to roads, as well as population migration) were identified in the spatial sprawl of urban cores. Our findings proved that a comprehensive approach is powerful for quantifying the spatio-temporal characteristics of the urbanization process at the township level and emphasized the importance of applying long-term historical data when researching the urbanization process.

scholarly journals Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

2021 ◽  
Vol 13 (15) ◽  
pp. 8162
Author(s):  
Xuyang Han ◽  
Costas Armenakis ◽  
Mojgan Jadidi
Keyword(s):  
Situational Awareness ◽  
Spatial Clustering ◽  
Route Planning ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Transportation ◽  
Dbscan Clustering ◽  
Clustering Model ◽  
Data Driven Approach

Today, maritime transportation represents a substantial portion of international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this paper, we present an enhanced density-based spatial clustering of applications with noise (DBSCAN) method to model vessel behaviours based on trajectory point data. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis distance metric, which considers the correlation between the points representing vessel locations. This research proposes applying the clustering method to historical Automatic Identification System (AIS) data using an algorithm to generate a clustering model of the vessels’ trajectories and a model for detecting vessel trajectory anomalies, such as unexpected stops, deviations from regulated routes, or inconsistent speed. Further, an automatic and data-driven approach is proposed to select the initial parameters for the enhanced DBSCAN approach. Results are presented from two case studies using an openly available Gulf of Mexico AIS dataset as well as a Saint Lawrence Seaway and Great Lakes AIS licensed dataset acquired from ORBCOMM (a maritime AIS data provider). These research findings demonstrate the applicability and scalability of the proposed method for modeling more water regions, contributing to situational awareness, vessel collision prevention, safe navigation, route planning, and detection of vessel behaviour anomalies for auto-vessel development towards the sustainability of marine transportation.

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