Gyei-Kark Park

Ontology-based fuzzy support agent for ship steering control

The important field of research on ship operation is related to the high efficiency of transportation, the convenience of maneuvering ships, and the safety of navigation. This paper proposes an ontology-based fuzzy support agent for ship steering control and desires to testify the validity of the proposal by applying the fuzzy control model to the steering control system based on linguistic instruction. The fuzzy support agent is presented to build the maneuvering models of steersman and the miniature model for steering control system. The proposed fuzzy agent contains three main mechanisms, including the interpretation mechanism of linguistic instruction, the self-regulation mechanism, and the task performance mechanism. Furthermore, the task performance mechanism includes the kinematics module and the performance ontology. The simulation results show that the proposed approach can work effectively for ship steering control.

Ontology-Based Fuzzy-CBR Support System for Ship's Collision Avoidance

Case-based reasoning (CBR) uses the same technique in solving tasks that needs reference from variety of situations. It can render decision-making easier by retrieving past solutions from situations that are similar to the one at hand and make necessary adjustments in order to adopt them. In this paper, an ontology-based fuzzy CBR support system for ships collision avoidance is presented to avoid the cumbersome tasks of creating a new solution each time, when a new situation is encountered. The first level of the ontology-based CBR identifies the dangerous ships and indexes the new case. The second level retrieves cases from the ontology and adapts the solution to solve for the output. The CBRs accuracy depends on the efficient retrieval of possible solutions, and the proposed algorithm improves the effectiveness of solving the similarity to a new case at hand.

Prediction Table for Marine Traffic for Vessel Traffic Service Based on Cognitive Work Analysis

Vessel Traffic Service (VTS) is being used at ports and in coastal areas of the world for preventing accidents and improving efficiency of the vessels at sea on the basis of “IMO RESOLUTION A.857 (20) on Guidelines for Vessel Traffic Services.” Currently, VTS plays an important role in the prevention of maritime accidents, as ships are required to participate in the system. Ships are diversified and traffic situations in ports and coastal areas have become more complicated than before. The role of VTS operator (VTSO) has been enlarged because of these reasons, and VTSO is required to be clearly aware of maritime situations and take decisions in emergency situations. In this paper, we propose a prediction table to improve the work of VTSO through the Cognitive Work Analysis (CWA), which analyzes the VTS work very systematically. The required data were collected through interviews and observations of 14 VTSOs. The prediction tool supports decision-making in terms of a proactive measure for the prevention of maritime accidents.

Assessment of External Force Acting on Ship Using Big Data in Maritime Traffic

For effective ship management in VTS(Vessel Traffic Service), it needs to assess the external force acting on ship. Big data in maritime traffic can be roughly categorized into two groups. One is the traffic information including ships particulars. The other is the external force information e.g., wind, sea wave, tidal current. This paper proposes the method to assess the external force acting on ship using big data in maritime traffic. To approach Big data in maritime traffic, we propose the Waterway External Force Code(WEF code) which consist of wind, wave, tidal and current information, Speed Over the Water(SOW) of each ship, weather information. As a results, the external force acting a navigating ship is estimated.

A Quantitative Collision Probability Analysis in Port Waterway

In terms of the maritime accident prevention, risk analysis at targeted warterways is important for planning safety waterways. This paper analyzes the maritime accidents probability in the Mokpo waterways, South Korea, based on the IWRAP(IALA Waterway Risk Assessment) of the quantitative accident probability tool. Vessel collision probability cate is calculated by vessels meeting direction, using IWRAP. This paper contribute to advance improvement of vessel traffic service by VTS sector providing vessel fairway risk data.

Characteristics of Ship Movements in a Fairway

In a coastal area, all of the vessels are always exposed to the potential risk, taking into the maritime accident statistics account over the last decades. To manage vessels underway safety, the characteristics of ship movements in a fairway should be recognized by VTS system or VTS operators. The IMO has already mandated the shipboard carriage of AIS since 2004, as stated in SOLAS Chapter V Regulation 19. As a result, the static and dynamic information of AIS data has been collected for vessel traffic management in the coastal areas and used for VTS. This research proposes a simple algorithm of recognizing potentially risky ships by observing their trajectories on the fairway. The static and dynamic information of AIS data are collected and the curvature for the ship trajectory is surveyed. The proposed algorithm finds out the irregularity of ship movement. The algorithm effectively monitors the change of navigation pattern from the curvature analysis of ship trajectory. Our method improves VTS functions in an intelligent way by analyzing the navigation pattern of vessels underway.

A Study on Ship Collision Avoidance Algorithm by COLREG

On the basis of DCPA(Distance to Closest Point of Approach) and TCPA(Time to CPA), the conventional algorithms for collision avoidances have a drawback that the `72 CORLEGs(International Regulations for Preventing Collisions at Sea, 1972) has not taken into account to prevent collisions between ships. In this paper, the proposed algorithm decides whether the own ship is a give-way vessel or a stand-on vessel by observing the relative bearing of the encountered ship. To determine the ship position and time for collision avoidance, the proposed algorithm utilizes the ellipse model for ship safety domain. The computer simulation is done to represent the process of adversive behavior. Using the proposed method, the past maritime accident is analyzed. The proposed method can be effectively applied to collision avoidance by CORLEGs even when the target ship`s navigational lights is invisible in poor weather and/or in the restricted visibility.

Utilization of Planned Routes and Dead Reckoning Positions to Improve Situation Awareness at Sea

Understanding a ship’s present position has been one of the most important tasks during a ship’s voyage, in both ancient and modern times. Particularly, a ship’s dead reckoning (DR) has been used for predicting traffic situations and collision avoidance actions. However, the current system that uses the traditional method of calculating DR employs the received position and speed data only. Therefore, it is not applicable for predicting navigation within the harbor limits, owing to the frequent changes in the ship’s course and speed in this region. In this study, planned routes were applied for improving the reliability of the proposed system and predicting the traffic patterns in advance. The proposed method of determining the dead reckoning position (DRP) uses not only the ships’ received data but also the navigational patterns and tracking data in harbor limits. The Mercator sailing formulas were used for calculating the ships’ DRPs and planned routes. The data on the traffic patterns were collected from the automatic identification system and analyzed using MATLAB. Two randomly chosen ships were analyzed for simulating their tracks and comparing the DR method during the timeframes of the ships’ movement. The proposed method of calculating DR, combined with the information on planned routes and DRPs, is expected to contribute towards improving the decision-making abilities of operators.

A Study on Near-miss Incidents from Maritime Traffic Flow by Clustering Vessel Positions

In the maritime traffic environment, the near-miss between vessels is the situation approaching on collision course but collision accident is not occurred. In this study, in order to calculate the near-miss between navigating vessels, the discriminating equation using ship bumper theory and vessel position clustering methods are proposed. Applying proposed module to the vessel trajectories of the WANDO waterway, we assessment navigational risk factors of vessel type, navigational speed, meeting situation.

A Study on Development of Maritime Traffic Assessment Model

Maritime traffic assessment is important to understand the characteristics of maritime traffic and to prevent maritime accidents. The maritime traffic assessment can be calculated from the ship trajectory data observed by using AIS(Automatic Identification System). This paper developes a maritime traffic assessment tool using ship`s position and speed, course, time data from ships navigating waterways. The results are represented in terms of the number of traffic quantity and traffic distribution, speed distribution, geometric collision candidates. The developed tool will contributes to advance maritime traffic safety by VTS(Vessel Traffic Services).