Minimization of ships' passing path in the field of risks

The object of research is the processes of automatic optimal passing of ships in the field of risks. ARPA (automatic radar plotting aid) is used on modern ships to track targets and pass from them. ARPA is an automated system that assumes the presence of an operator in the loop of information processing and management. Today, operator interventions in control processes are quite significant and often lead to an increase in the number of accidents and disasters. Recently, specialists have been paying more and more attention to the automation of ship control processes. The most promising direction of automation is the use of automatic control modules in automated systems. In this case, the shipmaster only decides to use the automatic module and observes its operation. An example of an automatic module in an automated system is autosteering, which has been used on ships for over 100 years. The paper considers the method of automatic optimal passing of ships in the field of risks. The method allows to minimize the path of passing, provided that the given collision risk is not exceeded. The obtained results are explained by the use of an on-board computer for the calculation of controls. In the on-board computer, at each step of the calculation, a field of risks is built. For the position point of the ship in the field of risks, there is a field gradient and a direction of movement of the ship perpendicular to the gradient. The direction of movement of the ship at each point is tangent to the trajectory of passing – an ellipse of equal risk. The ellipse of equal risks is used as a motion program for the formation of controls that ensure the movement of the ship along the ellipse of a given risk during the passing process. The developed method can be used for the development of automatic modules for managing the passing of ships in the field of risks

Use of Simulator Equipment for the Development and Testing of Vessel Control Systems

One of the ways to reduce human influence on the control process is the development of automated and automatic control systems. Modern control systems are quite complex and require preliminary ground testing. In article considered the issues of creating Imitation Modeling Stand for such control system synthesis and testing. For this, a Control System Model was integrated into the local computer network of the navigation simulator NTPRO 5000. Developed and tested software for information exchange between the navigation simulator and Control System Model. Developed a functional module of collision avoidance with many targets for testing in a closed loop system with virtual training objects. The results showed that the developed Imitation Modeling Stand allows to develop and test of functional modules of the control systems; in comparison with the found analogs also makes it easy to include in a closed simulation cycle various models of command devices, actuators, control objects, objects of training scene, weather conditions; is universal, both for solving problems of manual control and for developing and testing automatic and automated control systems is not highly specialized and is created with minimal costs

Use of Simulator Equipment for the Development and Testing of Vessel Control Systems

One of the ways to reduce human influence on the control process is the development of automated and automatic control systems. Modern control systems are quite complex and require preliminary ground testing. The article considers the issues of creating Imitation Modelling Stand for such control system synthesis and testing. For this reason, a Control System Model was integrated into the local computer network of the navigation simulator NTPRO 5000. The authors of the paper developed and tested software for information exchange between the navigation simulator and the Control System Model. The authors also developed a functional module of collision avoidance with many targets for testing in a closed loop system with virtual training objects. The results showed that the developed Imitation Modelling Stand allowed developing and testing functional modules of the control systems. In comparison with the found analogues, it is easy to include in a closed simulation cycle various models of command devices, actuators, control objects, objects of training scene, weather conditions; it is universal both for solving problems of manual control and for developing and testing automatic and automated control systems; it is not highly specialised and is created at minimal costs

Розробка аналізатора для підвищення безпеки морського судноплавства і його експериментальне дослідження

On the basis of empirical experimental data, relationships were identified indicating the influence of navigators' response to such vessel control indicators as maneuverability and safety. This formed a hypothesis about a non-random connection between the navigator's actions, response and parameters of maritime transport management. Within the framework of this hypothesis, logical-formal approaches were proposed that allow using server data of both maritime simulators and operating vessels in order to timely identify the occurrence of a critical situation with possible catastrophic consequences. A method for processing navigation data based on the analysis of temporal zones is proposed, which made it possible to prevent manifestations of reduced efficiency of maritime transport management by 22.5 %. Based on cluster analysis and automated neural networks, it was possible to identify temporary vessel control fragments and classify them by the level of danger. At the same time, the neural network test error was only 3.1 %, and the learning error was 3.8 %, which ensures the high quality of simulation results. The proposed approaches were tested using the Navi Trainer 5000 navigation simulator (Wärtsilä Corporation, Finland). The simulation of the system for identifying critical situations in maritime transport management made it possible to reduce the probability of catastrophic situations by 13.5 %. The use of automated artificial neural networks allowed defining critical situations in real time from the database of maritime transport management on the captain's bridge for an individual navigator.На основе эмпирических экспериментальных данных были идентифицированы связи, указывающие на влияние реакций навигаторов (судоводителей) на такие показатели управления судном как маневренность и безопасность. Это сформировало гипотезу о неслучайной связи между действиями навигатора, его реакциями и параметрами управления морским транспортом. В рамках указанной гипотезы были предложены логико-формальные подходы, позволяющие использовать серверные данные как морских симуляторов, так и действующих судов морского транспорта с целью своевременной идентификации возникновения критической ситуации с возможными катастрофическими последствиями. Предложен метод обработки навигационных данных, основанный на анализе темпоральных зон, который позволил предупредить проявления снижений результативности управления морским транспортом на 22,5 %. На основе кластерного анализа и автоматизированных нейронных сетей удалось выделить временные фрагменты управления судном и классифицировать их в соответствии с уровнем опасности. При этом тестовая ошибка нейронной сети составила лишь 3,1 %, а ошибка обучения 3,8 %, что обеспечивает высокое качество полученных результатов моделирования. Предложенные подходы были апробированы с применением навигационного тренажера Navi Trainer 5000 navigation simulator (Wärtsilä Corporation, Финляндия). Проведенное имитационное моделирование системы идентификации критических ситуации при управлении морским транспортом позволило уменьшить вероятность возникновения катастрофических ситуаций на 13,5 %. Использование автоматизированных искусственных нейронных сетей позволило проводить идентификацию критических ситуаций в режиме реального времени на основе базы данных управления морским транспортом на капитанском мостике для индивидуального навигатораНа основі емпіричних експериментальних даних були ідентифіковані зв’язки, що вказують на вплив реакцій навігаторів (судноводіїв) на такі показники управління судном як маневреність і безпека. Це сформувало гіпотезу про невипадковий зв’язок між діями навігатора, його реакціями та параметрами управління морським транспортом. У рамках зазначеної гіпотези були запропоновані логіко-формальні підходи, що дозволяють застосувати серверні дані як морських симуляторів, так і діючих суден морського транспорту з метою своєчасної ідентифікації виникнення критичної ситуації з ймовірними катастрофічними наслідками. Запропоновано метод обробки навігаційних даних, що заснований на аналізі темпоральних зон, який дозволив попередити прояви зниження результативності управління морським транспортом на 22,5 %. На основі кластерного аналізу і автоматизованих нейронних мереж вдалося виділити часові фрагменти управління судном і класифікувати їх відповідно до рівня небезпеки. При цьому тестова помилка нейронної мережі склала лише 3,1 %, а помилка навчання 3,8 %, що забезпечує високу якість отриманих результатів моделювання. Запропоновані підходи були апробовані із застосуванням навігаційного тренажера Navi Trainer 5000 navigation simulator (Wärtsilä Corporation, Фінляндія). Проведене імітаційне моделювання системи ідентифікації критичних ситуації під час управління морським транспортом дозволило зменшити ймовірність виникнення катастрофічних ситуацій на 13,5 %. Використання автоматизованих штучних нейронних мереж дозволило проводити ідентифікацію критичних ситуацій в режимі реального часу на основі бази даних управління морським транспортом на капітанському містку для індивідуального навігатор

Navigation Safety Control System Development Through Navigator Action Prediction by Data Mining Means

Taking into account current trends in the development of ergatic maritime transport systems, the factors of the navigator's influence on vessel control processes were determined. Within the framework of the research hypothesis, to improve navigation safety, it is necessary to apply predictive data mining models and automated vessel control. The paper proposes a diagram of the ergatic vessel control system and a model for identifying the influence of the navigator “human factor” during navigation. Within the framework of the model based on the principles of navigator decision trees, prediction by data mining means is applied, taking into account the identifiers of the occurrence of a critical situation. Based on the prediction results, a method for optimal vessel control in critical situations was developed, which is triggered at the nodes of the navigator decision tree, which reduces the likelihood of a critical impact on vessel control. The proposed approaches were tested in the research laboratory “Development of decision support systems, ergatic and automated vessel control systems”. The use of the Navi Trainer 5,000 navigation simulator (Wärtsilä Corporation, Finland) and simulation of the navigation safety control system for critical situations have confirmed its effectiveness. As a result of testing, it was determined that the activation of the system allowed reducing the likelihood of critical situations by 18–54 %. In 11 % of cases, the system switched the vessel control processes to automatic mode and, as a result, reduced the risk of emergencies. The use of automated data mining tools made it possible to neutralize the negative influence of the “human factor” of the navigator and to reduce the average maneuvering time during vessel navigation to 23

Navigation Safety Control System Development Through Navigator Action Prediction by Data Mining Means

Taking into account current trends in the development of ergatic maritime transport systems, the factors of the navigator's influence on vessel control processes were determined. Within the framework of the research hypothesis, to improve navigation safety, it is necessary to apply predictive data mining models and automated vessel control. The paper proposes a diagram of the ergatic vessel control system and a model for identifying the influence of the navigator “human factor” during navigation. Within the framework of the model based on the principles of navigator decision trees, prediction by data mining means is applied, taking into account the identifiers of the occurrence of a critical situation. Based on the prediction results, a method for optimal vessel control in critical situations was developed, which is triggered at the nodes of the navigator decision tree, which reduces the likelihood of a critical impact on vessel control. The proposed approaches were tested in the research laboratory “Development of decision support systems, ergatic and automated vessel control systems”. The use of the Navi Trainer 5,000 navigation simulator (Wärtsilä Corporation, Finland) and simulation of the navigation safety control system for critical situations have confirmed its effectiveness. As a result of testing, it was determined that the activation of the system allowed reducing the likelihood of critical situations by 18–54 %. In 11 % of cases, the system switched the vessel control processes to automatic mode and, as a result, reduced the risk of emergencies. The use of automated data mining tools made it possible to neutralize the negative influence of the “human factor” of the navigator and to reduce the average maneuvering time during vessel navigation to 23

Development and Experimental Study of Analyzer to Enhance Maritime Safety

On the basis of empirical experimental data, relationships were identified indicating the influence of navigators' response to such vessel control indicators as maneuverability and safety. This formed a hypothesis about a non-random connection between the navigator's actions, response and parameters of maritime transport management. Within the framework of this hypothesis, logical-formal approaches were proposed that allow using server data of both maritime simulators and operating vessels in order to timely identify the occurrence of a critical situation with possible catastrophic consequences. A method for processing navigation data based on the analysis of temporal zones is proposed, which made it possible to prevent manifestations of reduced efficiency of maritime transport management by 22.5 %. Based on cluster analysis and automated neural networks, it was possible to identify temporary vessel control fragments and classify them by the level of danger. At the same time, the neural network test error was only 3.1 %, and the learning error was 3.8 %, which ensures the high quality of simulation results. The proposed approaches were tested using the Navi Trainer 5000 navigation simulator (Wärtsilä Corporation, Finland). The simulation of the system for identifying critical situations in maritime transport management made it possible to reduce the probability of catastrophic situations by 13.5 %. The use of automated artificial neural networks allowed defining critical situations in real time from the database of maritime transport management on the captain's bridge for an individual navigator