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Application of Artificial Intelligence and Automatic Control in Marine and...
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Application of Artificial Intelligence and Automatic Control in Marine and Maritime Research

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 2046

Special Issue Editor

Dr. Sergei G. Chernyi

E-Mail Website
Guest Editor
Artificial Intelligence Laboratory, Saint Petersburg State Marine Technical University, Federation Lotsmanskaya Street, 10, 190121 Saint Petersburg, Russia
Interests: marine engineering systems; maritime; engine; fuzzy logic; control; automatic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the development and design of information systems for marine technology, information and control systems for marine robotics, the study of the physical fields of marine objects, issues of electronic circuitry, digital signal processing, automation of information systems, application programming, development of special software for microprocessors, microcontrollers and microprocessor systems for underwater vehicles and robots. We also devote a separate study to intelligent systems and applications, evaluating the contribution to the maritime industry to assess navigation, safety and environmental aspects.

The intensive expansion of the scope of robotic systems, including underwater robotics, and the increasing complexity of tasks are forcing developers to explore new technologies, including those based on the use of artificial intelligence systems. Intelligent systems are able to control autonomous uninhabited underwater vehicles (AUVs), including navigation in sea space, the formation of behavioral logic in unknown environments and movement planning, as well as optimize payload data processing.

Works on the use of artificial intelligence in ports in solving logistics problems are also welcomed.

We welcome scientific research, reviews, short communications, and other types of articles.

Dr. Sergei G. Chernyi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • robotics
  • neural networks
  • artificial intelligence
  • process automation
  • decision making
  • optimal control
  • marine environment
  • mechanics
  • control
  • marine

Published Papers (2 papers)

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Research

20 pages, 6891 KiB  
Article
Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine
by Chang-Min Lee, Byung-Gun Jung and Jae-Hyuk Choi
J. Mar. Sci. Eng. 2023, 11(10), 1993; https://doi.org/10.3390/jmse11101993 - 16 Oct 2023
Cited by 1 | Viewed by 812
Abstract
The International Maritime Organization strives to improve the atmospheric environment in oceans and ports by regulating ship emissions of air pollutants and promoting energy efficiency. This study deals with the prediction of eco-friendly combustion in boilers to reduce air pollution emissions. Accurately measuring [...] Read more.
The International Maritime Organization strives to improve the atmospheric environment in oceans and ports by regulating ship emissions of air pollutants and promoting energy efficiency. This study deals with the prediction of eco-friendly combustion in boilers to reduce air pollution emissions. Accurately measuring air pollutants from ship boilers in real-time is crucial for optimizing boiler combustion. However, using data obtained through an exhaust gas analyzer for real-time control is challenging due to combustion process delays. Therefore, a real-time predictive modeling approach is proposed to enhance the accuracy of prediction models for NOx, SO2, CO2, and O2 by analyzing the color spectrum of flame images in a quasi-instantaneous combustion state. Experimental investigations were carried out on an oil-fired boiler installed on an actual ship, where the air damper was adjusted to create various combustion conditions. This algorithm is a saturation-based feature extraction filter (SEF) through color spectrum analysis using RGB (red, green, and blue) and HSV (hue, saturation, and value). The prediction model applying the proposed method was verified against exhaust gas analyzer data using a new data set, and real-time prediction performance and generalization were confirmed. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Automatic Control in Marine and Maritime Research)
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21 pages, 9068 KiB  
Article
A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine
by Hong Je-Gal, Seung-Jin Lee, Jeong-Hyun Yoon, Hyun-Suk Lee, Jung-Hee Yang and Sewon Kim
J. Mar. Sci. Eng. 2023, 11(8), 1577; https://doi.org/10.3390/jmse11081577 - 11 Aug 2023
Viewed by 894
Abstract
Ensuring operational reliability in machinery requires accurate fault detection. While time-domain vibration pulsation signals are intuitive for pattern recognition and feature extraction, downsampling can reduce analytical complexity, but may result in low-precision data, affecting fault detection performance. To address this, we propose time–frequency [...] Read more.
Ensuring operational reliability in machinery requires accurate fault detection. While time-domain vibration pulsation signals are intuitive for pattern recognition and feature extraction, downsampling can reduce analytical complexity, but may result in low-precision data, affecting fault detection performance. To address this, we propose time–frequency feature fusion, combining information from both the time and frequency domains for fault detection. Our approach transforms vibrational pulse data into instantaneous revolutions per minute (RPM) and employs statistical analysis for the time-domain features. For the frequency-domain features, we use the combined method of empirical mode decomposition and independent component analysis (EMD-ICA), along with the Wigner bispectrum method to capture the nonlinear characteristics and phase conjugation. Using a deep neural network (DNN), we classify the anomaly states, demonstrating the effectiveness and versatility of our approach in detecting anomalies and improving diagnostic precision. Compared to using time or frequency features alone, our time–frequency feature fusion model achieves higher accuracy, with 100% accuracy at lower downsampling rates and 96.3% accuracy at a downsampling rate of 100×. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Automatic Control in Marine and Maritime Research)
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