• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.5
• /
• pp.519-526
• /
• 2018

In this study, an algorithm to predict evacuation routes in support of shipboard lifesaving activities is presented. As the first step of algorithm development, the feasibility and necessity of an evacuation route prediction algorithm are shown numerically. The proposed algorithm can be explained in brief as follows. This system continuously obtains and analyzes passenger movement data from the ship's monitoring system during non-disaster conditions. In case of a disaster, evacuation route prediction information is derived using the previously acquired data and a prediction tool, with the results provided to rescuers to minimize casualties. In this study, evacuation-related data obtained through fire evacuation trials was filtered and analyzed using a statistical method. In a simulation using the conventional evacuation prediction tool, it was found that reliable prediction results were obtained only in the SN1 trial because of the conceptual and structural nature of the tool itself. In order to verify the validity of the algorithm proposed in this study, an industrial engineering tool was adapted for evacuation characteristics prediction. When the proposed algorithm was implemented, the predicted values for average evacuation time and route were very similar to the measured values with error ranges of 0.6-6.9 % and 0.6-3.6 %, respectively. In the future, development of a high-performance evacuation route prediction algorithm is planned based on shipboard data monitoring and analysis.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.1-10
• /
• 2024

The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.100-103
• /
• 2023

Recently, development of maritime autonomoust surface ships and eco-friendly ships, production and evaluation research considering various marine environments is needed in the field of optimal routes as the demand for accurate and detailed real-time marine environment prediction information expands. An algorithm that can calculate the optimal route while reducing the risk of the marine environment and uncertainty in energy consumption in smart ships was developed in 2 stages. In the first stage, a profile was created by combining marine environmental information with ship location and status information within the Automatic Ship Identification System(AIS). In the second stage, a model was developed that could define the marine environment energy map using the configured profile results, A regression equation was generated by applying Random Forest among machine learning techniques to reflect about 600,000 data. The Random Forest coefficient of determination (R²) was 0.89, showing very high reliability. The Dijikstra shortest path algorithm was applied to the marine environment prediction at June 1 to 3, 2021, and to calculate the optimal safety route and express it on the map. The route calculated by the random forest regression model was streamlined, and the route was derived considering the state of the marine environment prediction information. The concept of route calculation based on real-time marine environment prediction information in this study is expected to be able to calculate a realistic and safe route that reflects the movement tendency of ships, and to be expanded to a range of economic, safety, and eco-friendliness evaluation models in the future.

• Journal of Astronomy and Space Sciences
• /
• v.32 no.1
• /
• pp.39-44
• /
• 2015

It is well known that the space radiation dose over the polar route should be carefully considered especially when the space weather shows sudden disturbances such as CME and flares. The National Meteorological Satellite Center (NMSC) and Korea Astronomy and Space Science Institute (KASI) recently established a basis for a space radiation service for the public by developing a space radiation prediction model and heliocentric potential (HCP) prediction model. The HCP value is used as a critical input value of the CARI-6 and CARI-6M programs, which estimate the aviation route dose. The CARI-6/6M is the most widely used and confidential program that is officially provided by the U.S. Federal Aviation Administration (FAA). The HCP value is given one month late in the FAA official webpage, making it difficult to obtain real-time information on the aviation route dose. In order to overcome this limitation regarding time delay, we developed a HCP prediction model based on the sunspot number variation. In this paper, we focus on the purpose and process of our HCP prediction model development. Finally, we find the highest correlation coefficient of 0.9 between the monthly sunspot number and the HCP value with an eight month time shift.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.24 no.4
• /
• pp.44-52
• /
• 2016

The departure flow management is the planning tool to optimize the schedule of the departure aircraft and allows them to join smoothly into the overhead traffic flow. To that end, the arrival time prediction to the merge point for the cruising aircraft is necessary to determined. This paper proposes a trajectory prediction model for the cruising aircraft based on the machine learning approach. The proposed method includes the trajectory vectored from the procedural route and is applied to the historical data to evaluate the prediction performances.

• International journal of advanced smart convergence
• /
• v.7 no.4
• /
• pp.27-39
• /
• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.6
• /
• pp.157-167
• /
• 2023

The use of big data for transportation often involves using data that includes personal information, such as the driver's driving routes and coordinates. This study explores the creation of a route choice prediction model using a large dataset from mobile navigation apps using federated learning. This privacy-focused method used distributed computing and individual device usage. This study established preprocessing and analysis methods for driver data that can be used in route choice modeling and compared the performance and characteristics of widely used learning methods with federated learning methods. The performance of the model through federated learning did not show significantly superior results compared to previous models, but there was no substantial difference in the prediction accuracy. In conclusion, federated learning-based prediction models can be utilized appropriately in areas sensitive to privacy without requiring relatively high predictive accuracy, such as a driver's preferred route choice.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.13 no.2 s.29
• /
• pp.133-140
• /
• 2007

Since early times, captain have been sailing to select the optimum route considering the weather, ship loading status condition and operational scheduling empirically. However, it is rare to find digitalized onboard route support system whereas weather facsimile or wave and swell chart are utilized for the officer, based on captain's experience. In this paper, optimal route safety assessment system which is composed of voyage efficiency and safety component is introduced. Optimum route minimizea ETA(estimated time of arrival) and fuel consumption that shipping company and captain are requiring to evaluate for efficient voyage considering speed loss and power increase based on wave added resistance of ship. In the view point of safety, seakeeping prediction is performed based on 3 dimensional panel method. Finally, It is assistance measure for ship's optimum navigation route safety planning & assessment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.277-279
• /
• 2022

Rapid Development and adoption of AIS as a survailance tool has resulted in widespread application of data analysis technology, in addition to AIS ship trajectory clustering. AIS data-based clustering has become an increasingly popular method for marine traffic pattern recognition, ship route prediction and anomaly detection in recent year. In this paper we propose a route waypoint extraction by clustering ships CoG variance trajectory using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm in both port approach channel and coastal waters. The algorithm discovers route waypoint effectively. The result of the study could be used in traffic route extraction, and more-so develop a maritime anomaly detection tool.

• Geomechanics and Engineering
• /
• v.13 no.3
• /
• pp.371-384
• /
• 2017

Physical and mechanical properties of rocks are of interest in many fields, including materials science, petrophysics, geophysics and geotechnical engineering. Uniaxial compressive strength UCS is one of the key mechanical properties, while density and porosity are important physical parameters for the characterization of rocks. The economic interest of carbonate rocks is very important in chemical or biological procedures and in the field of construction. Carbonate rocks exploitation depends on their quality and their physical, chemical and geotechnical characteristics. A fast, economic and reliable technique would be an evolutionary advance in the exploration of carbonate rocks. This paper discusses the ability of ultrasonic wave velocity to evaluate some mechanical and physical parameters within carbonate rocks (collected from different regions within Tunisia). The ultrasonic technique was used to establish empirical correlations allowing the estimation of UCS values, the density and the porosity of carbonate rocks. The results illustrated the behavior of ultrasonic pulse velocity as a function of the applied stress. The main output of the work is the confirmation that ultrasonic velocity can be effectively used as a simple and economical non-destructive method for a preliminary prediction of mechanical behavior and physical properties of rocks.