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Modern Technologies and Methods of Development for the Maritime Industry
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Modern Technologies and Methods of Development for the Maritime Industry

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 (1 March 2022) | Viewed by 10709

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,

The term maritime refers to almost everything connected to the sea or waterways throughout the world, especially in relation to navigation, shipping and marine engineering. The industry has a direct impact on much of our everyday lives.

The issues of navigation, pilotage and decision making are considered. This Special Issue is also devoted to the thematic area of automation systems and information systems for the maritime industry. Recently, this are of study has been changing very rapidly and there are many implementations in the technological and general cycles.

The development of the industry requires research in various fields of engineering, such as robotics, automation, neural networks and ecology, among others. This research will significantly contribute to the development of the maritime industry as a whole. The Special Issue will be interesting to both professionals and ordinary readers who research in and are interested in the maritime industry.

Prof. Dr. Sergei 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

  • maritime
  • automated
  • information technology
  • engine
  • energy
  • neural network
  • making decisions
  • nautical tourism
  • environmental protection
  • ship structure

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

13 pages, 2710 KiB  
Article
Application of Artificial Intelligence Technologies for Diagnostics of Production Structures
by Sergei Chernyi, Vitalii Emelianov, Elena Zinchenko, Anton Zinchenko, Olga Tsvetkova and Aleksandr Mishin
J. Mar. Sci. Eng. 2022, 10(2), 259; https://doi.org/10.3390/jmse10020259 - 14 Feb 2022
Cited by 7 | Viewed by 2505
Abstract
The paper presents that during the operation of torpedo ladle cars in metallurgical production, problems periodically arise with ensuring the safety of their use. The authors have highlighted the relevance and necessity of the solution to the problem of diagnosing the lining state [...] Read more.
The paper presents that during the operation of torpedo ladle cars in metallurgical production, problems periodically arise with ensuring the safety of their use. The authors have highlighted the relevance and necessity of the solution to the problem of diagnosing the lining state of ladle cars to ensure their safe functioning. To solve the problem of diagnosing the lining state of ladle cars for the maritime industry, an algorithm for detecting burnout zones of a lining based on a neural network has been developed. The authors propose and describe a distributed multi-agent information control system for the operation of torpedo ladle cars. The results for detecting burnout zones of a lining by the standard system and newly developed system are presented. To automate assessing the lining state of the ladle car and support in making decisions regarding operation mode of the ladle cars, the software has been developed. Full article
(This article belongs to the Special Issue Modern Technologies and Methods of Development for the Maritime Industry)
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13 pages, 3702 KiB  
Article
Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach
by Raisa Borovskaya, Denis Krivoguz, Sergei Chernyi, Efim Kozhurin, Victoria Khorosheltseva and Elena Zinchenko
J. Mar. Sci. Eng. 2022, 10(2), 257; https://doi.org/10.3390/jmse10020257 - 14 Feb 2022
Cited by 10 | Viewed by 4281
Abstract
Knowledge of the spatio-temporal distribution of salinity provides valuable information for understanding different processes between biota and environment, especially in hypersaline lakes. Remote sensing techniques have been used for monitoring different components of the environment. Currently, one of the biggest challenges is the [...] Read more.
Knowledge of the spatio-temporal distribution of salinity provides valuable information for understanding different processes between biota and environment, especially in hypersaline lakes. Remote sensing techniques have been used for monitoring different components of the environment. Currently, one of the biggest challenges is the spatio-temporal monitoring of the salinity level in water bodies. Due to some limitations, such as the inability to be located there permanently, it is difficult to obtain these data directly. In this study, machine learning techniques were used to evaluate the salinity level in hypersaline East Sivash Bay. In total, 93 in situ data samples and 6 Sentinel-2 datasets were used, according to field measurements. Using linear regression, random forest and AdaBoost models, eight water salinity evaluation models were built (six with simple, one with random forest and one with AdaBoost). The accuracy of the best-fitted simple linear regression model was 0.8797; for random forest, it was equal, at 0.808, and for AdaBoost, it was −0.72. Furthermore, it was found that with an increase in salinity, the absorbing light shifts from the ultraviolet part of the spectrum to the infrared and short-wave infrared parts, which makes it possible to produce continuous monitoring of hypersaline water bodies using remote sensing data. Full article
(This article belongs to the Special Issue Modern Technologies and Methods of Development for the Maritime Industry)
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14 pages, 3493 KiB  
Article
Optimal Ways of Unloading and Loading Operations under Arctic Conditions
by Marat Eseev and Dmitry Makarov
J. Mar. Sci. Eng. 2021, 9(10), 1050; https://doi.org/10.3390/jmse9101050 - 24 Sep 2021
Viewed by 2729
Abstract
Usually, loading and unloading of cargo ships takes place in ports that are equipped with the infrastructure necessary to carry out such operations. In the Arctic, often a helicopter is the only way to get the cargo to the right place. Finding the [...] Read more.
Usually, loading and unloading of cargo ships takes place in ports that are equipped with the infrastructure necessary to carry out such operations. In the Arctic, often a helicopter is the only way to get the cargo to the right place. Finding the optimal geographic location for unloading a ship using helicopters is an important task. It is necessary to create a support system for making the right decisions in such situations. Mathematical modeling has been used to find the geographical location that ensures the most favorable and quickest delivery of cargo from a vessel to its destination, using a helicopter. A criterion has also been found in which the search for the optimum point is a more rational way of unloading the vessel compared to other discharge options. The maps of the economic benefits of loading and unloading operations in this model have been developed. Using the example of the developed model, it is shown that during the transportation of goods in Ob Bay, significant economic and temporary advantages can be obtained. The developed model can be extended to the case of cargo delivery not only in the Arctic conditions, but also where the transport infrastructure is insufficiently developed. Full article
(This article belongs to the Special Issue Modern Technologies and Methods of Development for the Maritime Industry)
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