AIMC Topic: Ships

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A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety.

Sensors (Basel, Switzerland) Aug 23, 2021
The ability to exploit data for obtaining useful and actionable information and for providing insights is an essential element for continuous process improvements. Recognizing the value of data as an asset, marine engineering puts data considerations...
Ships Artificial Intelligence Deep Learning Neural Networks, Computer
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Detection of Inflatable Boats and People in Thermal Infrared with Deep Learning Methods.

Sensors (Basel, Switzerland) Aug 6, 2021
Smuggling of drugs and cigarettes in small inflatable boats across border rivers is a serious threat to the EU's financial interests. Early detection of such threats is challenging due to difficult and changing environmental conditions. This study re...
Humans Deep Learning Neural Networks, Computer Ships Algorithms Early Diagnosis
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Surveillance of ship emissions and fuel sulfur content based on imaging detection and multi-task deep learning.

Environmental pollution (Barking, Essex : 1987) Jul 3, 2021
Shipping makes up the major proportion of global transportation and results in an increasing emission of air pollutants. It accounts for 3.1%, 13%, and 15% of the annual global emissions of CO, SO, and NO, respectively. Hence, effective regulatory me...
Ships Vehicle Emissions Sulfur Air Pollutants Particulate Matter Deep Learning Environmental Monitoring
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A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method.

PloS one May 10, 2021
Ship collision accidents are the primary threat to traffic safety in the sea. Collision accidents can cause casualties and environmental pollution. The collision risk is a major indicator for navigators and surveillance operators to judge the collisi...
Statistics as Topic Accident Prevention Taiwan Least-Squares Analysis Ships Accidents Models, Theoretical Support Vector Machine Computer Simulation Risk Assessment Humans Algorithms
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Automatic ship classification for a riverside monitoring system using a cascade of artificial intelligence techniques including penalties and rewards.

ISA transactions Apr 12, 2021
Riverside monitoring systems are used for controlling the passage of ships, counting them to prevent overcrowding in a port, or raising an alarm if the ship is unknown or not safe. This type of control and analysis is commonly carried out by many peo...
Humans Artificial Intelligence Ships Reward Data Management Monitoring, Physiologic
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Adaptive Neural Backstepping Sliding Mode Heading Control for Underactuated Ships with Drift Angle and Ship-Bank Interaction.

Computational intelligence and neuroscience Sep 27, 2020
In order to track the desired path under unknown parameters and environmental disturbances, an adaptive backstepping sliding mode control algorithm with a neural estimator is proposed for underactuated ships considering both ship-bank interaction eff...
Neural Networks, Computer Algorithms Ships
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Microbiome composition and implications for ballast water classification using machine learning.

The Science of the total environment Jul 4, 2019
Ballast water is a vector for global translocation of microorganisms, and should be monitored to protect human and environmental health. This study utilizes high throughput sequencing (HTS) and machine learning to examine the bacterial and fungal mic...
Machine Learning Ships Water Microbiology Environmental Monitoring Microbiota
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Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports.

Environment international Jan 17, 2019
While ballast water has long been linked to the global transport of invasive species, little is known about its microbiome. Herein, we used 16S rRNA gene sequencing and metabarcoding to perform the most comprehensive microbiological survey of ballast...
DNA Barcoding, Taxonomic Water Microbiology Introduced Species Ships Los Angeles Machine Learning Singapore South Africa Escherichia coli China RNA, Ribosomal, 16S Bacteria
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Adaptive Neural Control of Underactuated Surface Vessels With Prescribed Performance Guarantees.

IEEE transactions on neural networks and learning systems Nov 12, 2018
This paper presents adaptive neural tracking control of underactuated surface vessels with modeling uncertainties and time-varying external disturbances, where the tracking errors consisting of position and orientation errors are required to keep ins...
Ships Humans Neural Networks, Computer
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High-resolution acoustic surveys with diving gliders come at a cost of aliasing moving targets.

PloS one Aug 16, 2018
Underwater gliders are autonomous robots that follow a slow, see-saw path and may be deployed for months on end. Gliders have a dramatically lower payload capacity than research vessels and are thus limited to more simple instrumentation. They have t...
Motion Ships Oceanography Robotics Oceans and Seas Computer Simulation Acoustics Models, Theoretical
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