• Journal of Ocean Engineering and Technology
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• v.37 no.5
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• pp.198-204
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• 2023

In response to the complexity and time demands of conventional methods for estimating the hydrodynamic coefficients, this study aims to revolutionize ship maneuvering analysis by utilizing automatic identification system (AIS) data and the Support Vector Regression (SVR) algorithm. The AIS data were collected and processed to remove outliers and impute missing values. The rate of turn (ROT), speed over ground (SOG), course over ground (COG) and heading (HDG) in AIS data were used to calculate the rudder angle and ship velocity components, which were then used as training data for a regression model. The accuracy and efficiency of the algorithm were validated by comparing SVR-based estimated hydrodynamic coefficients and the original hydrodynamic coefficients of the Mariner class vessel. The validated SVR algorithm was then applied to estimate the hydrodynamic coefficients for real ships using AIS data. The turning circle test wassimulated from calculated hydrodynamic coefficients and compared with the AIS data. The research results demonstrate the effectiveness of the SVR model in accurately estimating the hydrodynamic coefficients from the AIS data. In conclusion, this study proposes the viability of employing SVR model and AIS data for accurately estimating the hydrodynamic coefficients. It offers a practical approach to ship maneuvering prediction and control in the maritime industry.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.257-269
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• 2023

This paper presents a method for detecting faults in data obtained from the Automatic Identification System (AIS) of surface vessels. The data include latitude, longitude, Speed Over Ground (SOG), and Course Over Ground (COG). We derive two methods that utilize two models: a constant state model and a derivative augmented model. The constant state model incorporates noise variables to account for state changes, while the derivative augmented model employs explicit variables such as first or second derivatives, to model dynamic changes in state. Generally, the derivative augmented model detects faults more promptly than the constant state model, although it is vulnerable to potentially overlooking faults. The effectiveness of this method is validated using AIS data collected at a harbor. The results demonstrate that the proposed approach can automatically detect faults in AIS data, thus offering partial assistance for enhancing navigation safety.

• Asia pacific journal of information systems
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• v.3 no.2
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• pp.93-116
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• 1993

This study surveys 212 accounting information systems (AIS) of 85 manufacturing firms by using the research model based on the management process of AIS to figure out the current status and problems of the computing environment and AIS of Korean firms. The analysis of the current status leads to the suggestions to promote the utilization and efficiency of AIS. The level of experiences and education of information system (IS) personnel turns out to be still low. More education is needed to upgrade the IS personnel. AIS users lack in the computer knowledge. The users need more computer education. The analysis on the computerization and information characteristics of the AIS subsystems shows that the computerization is well established in the financial accounting area. On the other hand, the computerization for managerial accounting areas is in its early stage. The managerial accounting systems need be developed to support the managerial decision making effectively. The majority of firms develop the AIS by their own IS teams. When firms use the consulting services in developing AIS, they prefer accounting firms. The majority of firms fail to evaluate the AIS because the evaluation tools are not available. Most firms do not perform the auditing for AIS. It is needed to develop the tool and techniques for evalauation and auditing AIS.

• Asian Journal of Business Environment
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• v.12 no.3
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• pp.1-12
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• 2022

Purpose: This study aims to investigate whether AIS data can be used as a supporting indicator or as an initial signal to describe Indonesia's export-import conditions in real-time. Research design, data, and methodology: This study performs several stages of data selection to obtain indicators from AIS that truly reflect export-import activities in Indonesia. Also, investigate the potential of AIS indicators in producing forecasts of the value and volume of Indonesian export-import using conventional statistical methods and machine learning techniques. Results: The six preprocessing stages defined in this study filtered AIS data from 661.8 million messages to 73.5 million messages. Seven predictors were formed from the selected AIS data. The AIS indicator can be used to provide an initial signal about Indonesia's import-export activities. Each export or import activity has its own predictor. Conventional statistical methods and machine learning techniques have the same ability both in forecasting Indonesia's exports and imports. Conclusions: Big data AIS can be used as a supporting indicator as a signal of the condition of export-import values in Indonesia. The right method of building indicators can make the data valuable for the performance of the forecasting model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.1-10
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• 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
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• 2014.10a
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• pp.114-116
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• 2014

The interpolation of the missing AIS dynamic data can be used for predicting the lost data of the ship's state which is able to product the valuable information for analyzing and investigating the maritime accidents. The previous research proposed some interpolating methods however there exists some problem, firstly, the interpolated parameters such as COG, SOG, HDG weren't described sufficiently and accurately as in AIS message, secondly, each method is only suitable to some kinds of given AIS data, finally at heavy wind and current area, the parameters of AIS dynamic change quickly in short time, therefore, the modelling of the variation of ship's dynamic based on the physical characteristic is very difficult, in these cases the time-series and numerical method are usually better. This research proposes the other method through numerical analysis which can be suitable for many different kinds of the lost data, parameters are interpolated sufficiently, beside that this model is appropriate to all variation in short time interval. All the given AIS dynamic are regarded as the functions to time, then curves are established for fitting all data. Experiments are carried out to evaluate the performance of this approach, the interpolation results show this approach can be applied well in practice.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.435-459
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• 2022

The accounting literature abounds with many studies concerning the organizational and technical aspects of the AIS to simulate progress in the business environment. However, few studies have focused on the role of individual factors in overcoming resistance to change and maximizing the value of using the system. Therefore, this study aims to shed light on user beliefs by evaluating the factors that affect the use of the AIS using a developed TAM. A total of 132 subjects participated in this study, in which the questionnaire was used as a data collection tool and AMOS was used to test the model. The results showed that subjective norm, training and experience were the most important previous factors that affect the perceptual factors represented in usefulness, ease of use and the inevitability of change, which all had an impact on the continuance intention to use the AIS among users in Algerian firms. This study shed light on the importance of assessing individual factors rather than focusing only on the ways to develop AIS or researching for new technologies and the costs of this investment because this will increase the chances of success in using the system.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.245-253
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• 2020

The purpose of the research was to identify the level of accounting information system for SMBs and environmental factors, and to analyze whether the level of accounting information system affects system performance. The research method measured the AIS level using Nolan's growth phase model, and the verification of the factors affecting the situation, AIS level, and performance was verified using the regression analysis model. The results of the study are summarized as follows: In measuring the level of an accounting information system, it was found that it was in the stage of integration, which is Step 4, and the analysis of the factors influencing the level of an accounting information system showed that the uncertainty in the environment was absolutely affected.

• The Journal of the Acoustical Society of Korea
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• v.37 no.3
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• pp.129-138
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• 2018

In port and coastal areas where ship traffic is frequent, ship noise dominantly influences underwater noise in low frequency band below 1 kHz. In this paper, we propose a modeling method to estimate the underwater shipping noise using the voyage information of ship observed in AIS (Automatic Identification System). For the purpose of ship noise modeling, the navigation information of the vessels operating in the southern part of Jeju was observed using AIS and underwater noise was measured by installing a hydrophone in the experimental area to verify the modeled ship noise. AIS data were used to model the noise level of ship and compared with measured underwater noise. The variation of noise level with time was found to be similar, and the cause of the error was discussed. Through this study, it was confirmed that the noise level of ship can be estimated within 5 dB error range using AIS data.