Neural-Network-Based Classification of Commercial Ships From Multi-Influence Passive Signatures

Axelsson, Oskar; Rhen, Christin

doi:10.1109/joe.2020.2982756

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…All kinds of complex technology must be humanized and intelligent in order to be widely used in education. Artificial intelligence categorization technology for educational resources will become a key frontier issue driven by educational technology majors and extensively absorbed by multidisciplinary specialists [6][7][8]. With the fast expansion of the Internet, the number of online resources has exploded in recent years.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks

Hou

Madina

2022

Mathematical Problems in Engineering

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Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.

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Section: Introductionmentioning

confidence: 99%

Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks

Hou

Madina

2022

Mathematical Problems in Engineering

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“…The measurement of underwater electrical potential (UEP) is widely used in civil and military marine applications for different purposes. For example, it is used for monitoring a ship hull corrosion and its protection against corrosion 10 – 15 , in predicting UEP signatures to evaluate a possible risk of the vessel beaning detected by naval mines 16 , in geophysical surveys to obtain the information on seabed structure 17 , 18 , in the exploration of oil and gas deposits located in the seafloor 19 , 20 in the tasks of ships tracking 21 and object localization below the waterline 22 , or in ships classification 23 .…”

Section: Introductionmentioning

confidence: 99%

An analytical four-layer horizontal electric current dipole model for analysing underwater electric potential in shallow seawater

Wołoszyn

Buszman

Rutkowski

et al. 2022

Sci Rep

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The paper presents a new analytical four-layer (air–water–bottom–non-conductive layer) horizontal electric dipole model which allows an accurate approximation of ship's Underwater Electric Potential (UEP) from a sufficient depth in shallow coastal marine waters. The numerical methods, usually Finite Element Method (FEM) or Boundary Elements Method (BEM), are typically used to estimate the electric field and the distribution of static electric components of UEP around the ship. These methods enable analyses with high accuracy but, compared to other point-electrode methods and the proposed analytical model, they are relatively complex and need high computational time. The developed analytical model proposed in this paper allows real-time calculations without significant loss of accuracy of the UEP estimations. In the model, the problem of boundary values at the borders of individual layers is solved using the reflection/image method and applying the idea of continuity of electric potential at a given boundary between two adjacent layers. Its accuracy is verified based on the synthetic data provided by specialised software packages making use of FEM and BEM numerical methods. A dimensionless quantitative analysis of the relationships between basic parameters of the proposed four-layer analytical model and their impact on the accuracy of representation of individual electric field strength components is also delivered. The relationships between water and bottom conductivity and between water depth and bottom thickness are investigated and described. The obtained results show that the developed model allows detailed and reliable analysis of the electric field, especially in shallow coastal waters.

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Underwater sound classification using learning based methods: A review

Aslam,

Zhang,

Liu

et al. 2024

Expert Systems with Applications

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Neural-Network-Based Classification of Commercial Ships From Multi-Influence Passive Signatures

Cited by 5 publications

References 30 publications

Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks

Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks

An analytical four-layer horizontal electric current dipole model for analysing underwater electric potential in shallow seawater

Underwater sound classification using learning based methods: A review

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