РОЗРОБКА КВАЛІФІКАЦІЙНОЇ МОДЕЛІ СУДНОВОДІЯ ДЛЯ ЗАДАЧ АВТОМАТИЗОВАНОГО КЕРУВАННЯ СУДНОМ
https://doi.org/10.33815/2313-4763.2024.2.29.006-023
Ключові слова:
кермове управління, оптимізація процесів керування, модуль автоматичного керування, аварійні ситуації, транспортні потоки, інформаційна підтримка, Safety Depth, ECDIS, маневрування у стиснених водах, система розпізнавання
Анотація
Дослідження спрямоване на розробку комплексної кваліфікаційної моделі штурманів в автоматизованому управлінні судном, яка оцінює технічну, когнітивну та поведінкову компетентність для підвищення ефективності прийняття рішень у реальному часі в умовах змінного навігаційного середовища.
Головним викликом є інтеграція передових технологій, таких як штучний інтелект та нечітка логіка, для точного моніторингу ризиків, що виникають через людський фактор.
Методологія включає створення моделі, яка оцінює компетенції штурмана шляхом інтеграції різних аспектів. Дані з ECDIS та інших сенсорів обробляються у вектор ознак. Алгоритм Мамдані агрегує нечіткі правила, що визначають кваліфікаційні параметри, а нейронні мережі моделюють складні взаємозв'язки. Модель використовує нечіткі функції належності для оцінки ризиків з урахуванням швидкості, глибини під кілем, погодних умов та ймовірності зіткнення.
Результати показують, що модель вчасно виявляє потенційні ризики та автоматизує процес прийняття рішень, зменшуючи навантаження на штурмана в складних умовах. Вона ефективно прогнозує траєкторію судна, ідентифікує ризикові зони та надає рекомендації щодо безпеки.
Практично це підвищує безпеку мореплавства через персоналізовану оцінку штурмана. Інтеграція з існуючими системами, такими як ECDIS, пропонує гнучкість без значних змін інфраструктури. Система індивідуалізує рекомендації, знижуючи ризик аварій та покращуючи ефективність підготовки. Майбутні дослідження включають розширення бази даних для підготовки, удосконалення алгоритмів та вивчення впливу психофізіологічного стану штурмана на ефективність управління судном.
Посилання
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13. Lei, Jinyu & Sun, Yuan & Wu, Yong & Zheng, Fujin & He, Wei & Liu, Xinglong (2024). Association of AIS and Radar Data in Intelligent Navigation in Inland Waterways Based on Trajectory Characteristics. Journal of Marine Science and Engineering. 12. 890. 10.3390/jmse12060890.
14. Car, Maro & Vujicic, Srdjan & Žuškin, Srđan & Brčić, David (2019). Human Machine Interface: Interaction of OOWs with the ECDIS system. Conference: 1st International Conference of Maritime Science & Technology – Naše More 2019.
15. Medic, Dario & Bošnjak, Rino & Bukljaš, Mihaela & Vukša, Srđan (2021). Research Study and the Model for Improving the Safety of Navigation when Using the AIS. TransNav the International Journal on Marine Navigation and Safety of Sea Transportation. 15. 577. 10.12716/1001.15.03.10.
16. Liu, Tian & MA, Jianwen (2022). Ship Navigation Behavior Prediction based on AIS Data. IEEE Access. 10. 1–1. 10.1109/ACCESS.2022.3172308.
17. Jiang, Zhiqiang & Zhang, Lingyu & Li, Weijia (2024). A machine vision method for the evaluation of ship-to-ship collision risk. Heliyon. 10. e25105. 10.1016/j.heliyon.2024.e25105.
18. Vukić, Đurđica & Candrlic, Sanja & Poščić, Patrizia (2020). COMPARATIVE OVERVIEW OF ADAPTIVE ASSESSMENT ACTIVITY IN OPEN SOURCE LMS: DATA MODEL PERSPECTIVE. 4944–4954. 10.21125/inted.2020.1356.
19. Moon, Kibum & Kim, Jinwon & Lee, Jinsook (2021). Early Prediction Model of Student Performance Based on Deep Neural Network Using Massive LMS Log Data. The Journal of the Korea Contents Association. 21. 1–10. 10.5392/JKCA.2021.21.10.001.
20. Putra, Bagas & Soeprijanto, & Daryanto (2024). Evaluation of the Utilization of the Learning Management System (LMS) Using the CIPP Model. Global Synthesis in Education Journal. 1. 19-27. 10.61667/dp8q3757.
21. Elov, Botir & Rasulovna, Jamoldinova & Mastura, Primova & Khusainova, Zilola & Raxmatilloyevich, Aloyev & O'g'li, Xudayberganov (2024). The Stages of Creation of LMS Model.
22. Liu, Dongqin & Zheng, Zhongyi & Liu, Zihao (2024). Research on Dynamic Quaternion Ship Domain Model in Open Water Based on AIS Data and Navigator State. Journal of Marine Science and Engineering. 12. 516. 10.3390/jmse12030516.
23. Okechukwu, Ogochukwu & Amaechi, Eziechina & George, Onyemachi & Isaac, Onuwa (2024). Enhanced Text Recognition in Images Using Tesseract OCR within the Laravel Framework. Asian Journal of Research in Computer Science. 17. 58–69. 10.9734/ajrcos/2024/v17i9499.
24. Manjunath, Akanksh & Nayak, Manjunath & Nishith, Santhanam & Pandit, Satish & Sunkad, Shreyas & Deenadhayalan, Pratiba & Gangadhara, Shobha (2023). Automated invoice data extraction using image processing. IAES International Journal of Artificial Intelligence (IJ-AI). 12. 514. 10.11591/ijai.v12.i2.pp514–521.
25. Sumana, K. R. (2024). Optimized OCR Data Extraction Using Custom-Trained NLP-NER Models for Enhanced Image Analysis. Journal of Emerging Technologies and Innovative Research. 11. 159–162.
26. Ujkani, Arbresh & Hohnrath, Pascal & Grundmann, Robert & Burmeister, Hans-Christoph (2024). Enhancing Maritime Navigation with Mixed Reality: Assessing Remote Pilotage Concepts and Technologies by In Situ Testing. Journal of Marine Science and Engineering. 12. 1084. 10.3390/jmse12071084.
27. Martes, Liliana (2020). Best Practices in Competency-based Education in Maritime and Inland Navigation. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation. 14. 557–562. 10.12716/1001.14.03.06.
28. Victoria Ponomaryova, Pavlo Nosov (2023). Method of automated identi-fication of qualification parameters for marine operators under risk conditions // Naukovyi visnyk Khersonskoi derzhavnoi morskoi akademii (Avtomatyzatsiia ta kompiuterno-intehrovani tekhnolohii): naukovyi zhurnal. – Kherson: Khersonska derzhavna morska akademiia, № 26–27 (2023). S. 144–165.
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30. Victoria Ponomaryova (2024). Method of decision support for navigators in automated vessel traffic safety control based on ecdis data // Naukovyi visnyk Khersonskoi derzhavnoi morskoi akademii (Avtomatyzatsiia ta kompiuterno-intehrovani tekhnolohii): naukovyi zhurnal. Kherson: Khersonska derzhavna morska akademiia, № 1 (28), 2024. S. 22–40.
2. Liu, Wenwen & Liu, Yuanchang & Gunawan, Bryan & Bucknall, Richard (2020). Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion. International Journal of Fuzzy Systems. 23. 10.1007/s40815-020-00963-1.
3. Du, Zhixiu (2024). A Risk Assessment Model for Navigation Safety of Maritime Aquaculture Platform Based on AIS Ship Trajectory. Journal of Electrical Systems. 20. 116–123. 10.52783/jes.2364.
4. Devikala, S. (2024). Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT. Journal of Electrical Systems. 20. 114–121. 10.52783/jes.1254.
5. Gül, Muhammet & Ak, Muhammet Fatih (2022). Occupational Risk Assessment for Flight Schools: A 3,4-Quasirung Fuzzy Multi-Criteria Decision Making-Based Approach. Sustainability. 14. 9373. 10.3390/su14159373.
6. Wang, Shaobo & Zou, Yiyang & Wang, Xiaohui (2024). An Intelligent Decision-Making Approach for Multi-Ship Traffic Conflict Mitigation from the Perspective of Maritime Surveillance. Journal of Marine Science and Engineering. 12. 1719. 10.3390/jmse12101719.
7. Aylward, Katie & Weber, Reto & Lundh, Monica & MacKinnon, Scott & Dahlman, Joakim (2022). Navigators’ views of a collision avoidance decision support system for maritime navigation. Journal of Navigation. 75. 1–14. 10.1017/S0373463322000510.
8. Wang, Yuqing & Chen, Xinqiang & Wu, Yuzhen & Zhao, Jiansen & Postolache, Octavian & Liu, Shuhao (2024). Visual Navigation Systems for Maritime Smart Ships: A Survey. Journal of Marine Science and Engineering. 12. 1781. 10.3390/jmse12101781.
9. Li, Wei & Chen, Weijiong & Guo, Yunlong & Hu, Shenping & Xi, Yongtao & Wu, Jianjun (2024). Risk Performance Analysis on Navigation of MASS via a Hybrid Framework of STPA and HMM: Evidence from the Human–Machine Co-Driving Mode. Journal of Marine Science and Engineering. 12. 1129. 10.3390/jmse12071129.
10. Houweling, Koen & Mallam, Steven & van de Merwe, Koen & Nordby, Kjetil (2023). An Approach for Measuring The Effects of Augmented Reality on Human Performance in Maritime Navigation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 67. 10.1177/21695067231193647.
11. Mohamed, Eslam & Hosny, Moustafa (2024). Enhancing Safety of Navigation with ECDIS Standardization and S-Mode Adoption. Maritime Research and Technology. 3. 117. 10.21622/MRT.2024.03.2.932.
12. Lee, Changui & Seojeong, Lee (2024). A Risk Identification Method for Ensuring AI-Integrated System Safety for Remotely Controlled Ships with Onboard Seafarers. Journal of Marine Science and Engineering. 12. 1778. 10.3390/jmse12101778.
13. Lei, Jinyu & Sun, Yuan & Wu, Yong & Zheng, Fujin & He, Wei & Liu, Xinglong (2024). Association of AIS and Radar Data in Intelligent Navigation in Inland Waterways Based on Trajectory Characteristics. Journal of Marine Science and Engineering. 12. 890. 10.3390/jmse12060890.
14. Car, Maro & Vujicic, Srdjan & Žuškin, Srđan & Brčić, David (2019). Human Machine Interface: Interaction of OOWs with the ECDIS system. Conference: 1st International Conference of Maritime Science & Technology – Naše More 2019.
15. Medic, Dario & Bošnjak, Rino & Bukljaš, Mihaela & Vukša, Srđan (2021). Research Study and the Model for Improving the Safety of Navigation when Using the AIS. TransNav the International Journal on Marine Navigation and Safety of Sea Transportation. 15. 577. 10.12716/1001.15.03.10.
16. Liu, Tian & MA, Jianwen (2022). Ship Navigation Behavior Prediction based on AIS Data. IEEE Access. 10. 1–1. 10.1109/ACCESS.2022.3172308.
17. Jiang, Zhiqiang & Zhang, Lingyu & Li, Weijia (2024). A machine vision method for the evaluation of ship-to-ship collision risk. Heliyon. 10. e25105. 10.1016/j.heliyon.2024.e25105.
18. Vukić, Đurđica & Candrlic, Sanja & Poščić, Patrizia (2020). COMPARATIVE OVERVIEW OF ADAPTIVE ASSESSMENT ACTIVITY IN OPEN SOURCE LMS: DATA MODEL PERSPECTIVE. 4944–4954. 10.21125/inted.2020.1356.
19. Moon, Kibum & Kim, Jinwon & Lee, Jinsook (2021). Early Prediction Model of Student Performance Based on Deep Neural Network Using Massive LMS Log Data. The Journal of the Korea Contents Association. 21. 1–10. 10.5392/JKCA.2021.21.10.001.
20. Putra, Bagas & Soeprijanto, & Daryanto (2024). Evaluation of the Utilization of the Learning Management System (LMS) Using the CIPP Model. Global Synthesis in Education Journal. 1. 19-27. 10.61667/dp8q3757.
21. Elov, Botir & Rasulovna, Jamoldinova & Mastura, Primova & Khusainova, Zilola & Raxmatilloyevich, Aloyev & O'g'li, Xudayberganov (2024). The Stages of Creation of LMS Model.
22. Liu, Dongqin & Zheng, Zhongyi & Liu, Zihao (2024). Research on Dynamic Quaternion Ship Domain Model in Open Water Based on AIS Data and Navigator State. Journal of Marine Science and Engineering. 12. 516. 10.3390/jmse12030516.
23. Okechukwu, Ogochukwu & Amaechi, Eziechina & George, Onyemachi & Isaac, Onuwa (2024). Enhanced Text Recognition in Images Using Tesseract OCR within the Laravel Framework. Asian Journal of Research in Computer Science. 17. 58–69. 10.9734/ajrcos/2024/v17i9499.
24. Manjunath, Akanksh & Nayak, Manjunath & Nishith, Santhanam & Pandit, Satish & Sunkad, Shreyas & Deenadhayalan, Pratiba & Gangadhara, Shobha (2023). Automated invoice data extraction using image processing. IAES International Journal of Artificial Intelligence (IJ-AI). 12. 514. 10.11591/ijai.v12.i2.pp514–521.
25. Sumana, K. R. (2024). Optimized OCR Data Extraction Using Custom-Trained NLP-NER Models for Enhanced Image Analysis. Journal of Emerging Technologies and Innovative Research. 11. 159–162.
26. Ujkani, Arbresh & Hohnrath, Pascal & Grundmann, Robert & Burmeister, Hans-Christoph (2024). Enhancing Maritime Navigation with Mixed Reality: Assessing Remote Pilotage Concepts and Technologies by In Situ Testing. Journal of Marine Science and Engineering. 12. 1084. 10.3390/jmse12071084.
27. Martes, Liliana (2020). Best Practices in Competency-based Education in Maritime and Inland Navigation. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation. 14. 557–562. 10.12716/1001.14.03.06.
28. Victoria Ponomaryova, Pavlo Nosov (2023). Method of automated identi-fication of qualification parameters for marine operators under risk conditions // Naukovyi visnyk Khersonskoi derzhavnoi morskoi akademii (Avtomatyzatsiia ta kompiuterno-intehrovani tekhnolohii): naukovyi zhurnal. – Kherson: Khersonska derzhavna morska akademiia, № 26–27 (2023). S. 144–165.
29. Ponomaryova, V., Nosov, P., Ben, A., Popovych, I., Prokopchuk, Y., Mamenko, P., Dudchenko, S., Appazov, E., & Sokol, I. (2024). Devising an approach for the automated restoration of shipmaster’s navigational qualification parameters under risk conditions. Eastern-European Journal of Enterprise Technologies, 1(3 (127), 6–26. https://doi.org/10.15587/1729-4061.2024.296955.
30. Victoria Ponomaryova (2024). Method of decision support for navigators in automated vessel traffic safety control based on ecdis data // Naukovyi visnyk Khersonskoi derzhavnoi morskoi akademii (Avtomatyzatsiia ta kompiuterno-intehrovani tekhnolohii): naukovyi zhurnal. Kherson: Khersonska derzhavna morska akademiia, № 1 (28), 2024. S. 22–40.
Опубліковано
2025-01-24
Розділ
АВТОМАТИЗАЦІЯ ТА КОМП’ЮТЕРНО-ІНТЕГРОВАНІ ТЕХНОЛОГІЇ
