AIMC Topic: Ships

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Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning.

Sensors (Basel, Switzerland)
The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidan...

Control of Dynamic Positioning System with Disturbance Observer for Autonomous Marine Surface Vessels.

Sensors (Basel, Switzerland)
The main goal of the research is to design an efficient controller for a dynamic positioning system for autonomous surface ships using the backstepping technique for the case of full-state feedback in the presence of unknown external disturbances. Th...

Ship Radiated Noise Recognition Technology Based on ML-DS Decision Fusion.

Computational intelligence and neuroscience
Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads...

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

Sensors (Basel, Switzerland)
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...

Detection of Inflatable Boats and People in Thermal Infrared with Deep Learning Methods.

Sensors (Basel, Switzerland)
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...

Surveillance of ship emissions and fuel sulfur content based on imaging detection and multi-task deep learning.

Environmental pollution (Barking, Essex : 1987)
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...

A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method.

PloS one
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...

Automatic ship classification for a riverside monitoring system using a cascade of artificial intelligence techniques including penalties and rewards.

ISA transactions
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...

Adaptive Neural Backstepping Sliding Mode Heading Control for Underactuated Ships with Drift Angle and Ship-Bank Interaction.

Computational intelligence and neuroscience
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...

Microbiome composition and implications for ballast water classification using machine learning.

The Science of the total environment
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...