• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.391-395
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• 2023

Helicopter accidents due to spatial disorientation in low visibility conditions continue to persist as a major issue. These incidents often stem from human error, typically induced by stress, and frequently result in fatal outcomes. This study employs machine learning to analyze flight data and evaluate the efficacy of a flight illusion detection algorithm, laying groundwork for further research. This study collected flight data from approximately 20 pilots using a simulated flight training device to construct a range of flight scenarios. These scenarios included three stages of flight: ascending, level, and descent, and were further categorized into good visibility conditions and 0-mile visibility conditions. The aim was to investigate the occurrence of flight illusions under these conditions. From the extracted data, we obtained a total of 54,000 time-series data points, sampled five times per second. These were then analyzed using a machine learning approach.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.363-364
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• 2022

The COVID-19 pandemic had substantial negative impacts and caused several disruptions to the global supply chain of the shipping industry. The key challenges identified in terms of maritime manpower are the Certificates of Competency (CoC) or the expiration and/or failure to complete refresher and/or revalidation courses, which directly hinder employment retention and lost opportunities at sea. To tackle this issue directly and swiftly, the creation of the APEC SEN Distance Learning Platform was suggested and approved by APEC as part of an official project. This paper introduces the APEC-wide accessible distance learning platform with the following key topics: the organisation and operation of the platform, the themes and content to be prioritised, the process of education, training, certification, and the ways to promote accreditation, mutual recognition on CoC, education and training videos by taking collaborative actions, and the development of content.

• Journal of Advanced Navigation Technology
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• v.26 no.2
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• pp.72-77
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• 2022

Among the techniques of reinforcement learning, Q-Learning means learning optimal policies by learning Q functions that perform actionsin a given state and predict future efficient expectations. Q-Learning is widely used as a basic algorithm for reinforcement learning. In this paper, we studied the effectiveness of selecting and learning efficient paths by designing policies and rewards based on Q-Learning. In addition, the results of the existing algorithm and punishment compensation policy and the proposed punishment reinforcement policy were compared by applying the same number of times of learning to the 8x8 grid environment of the Frozen Lake game. Through this comparison, it was analyzed that the Q-Learning punishment reinforcement policy proposed in this paper can significantly increase the learning speed compared to the application of conventional algorithms.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.271-279
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• 2001

Sequential pattern discovery is one of main interests in web usage mining. the technique of sequential pattern discovery attempts to find inter-session patterns such that the presence of a set of items is followed by another item in a time-ordered set of server sessions. In this paper, a tree-based sequential pattern finding method is proposed in order to discover navigation patterns in server sessions. At each learning process, the suggested method learns about the navigation patterns per server session and summarized into the modified Rymon's tree.

• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.426-431
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• 2009

Recently, the application of E-learning is changing the method that is able to process the learning to learner more efficiently and conveniently. For this purpose, the application research of U-learning that is able to support the learning using mobile device such as PDA, NetBook, Tablet PC and so on is actively processing. But lots of U-learning framework is only considering the change to fit the exist learning contents the mobile device without SCORM that is able to support to make and process the learning contents by regular forms. In this paper, we proposed the learning contents interconnection of U-learning framework considering SCORM. For this purpose, we have to construct the learning by learning object and asset within SCORM. And this method can support learning information that was reconstructed it by learning contents to fit the mobile device as used the mobile device meta-data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.23-28
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• 2021

Data analysis research using deep learning has recently been studied in various field. In this paper, we conduct a GNSS (Global Navigation Satellite System)-based meteorological study applying deep learning by estimating the ZWD (Zenith tropospheric Wet Delay) through MLP (Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) models. Deep learning models were trained with meteorological data and ZWD which is estimated using zenith tropospheric total delay and dry delay. We apply meteorological data not used for learning to the learned model to estimate ZWD with centimeter-level RMSE (Root Mean Square Error) in both models. It is necessary to analyze the GNSS data from coastal areas together and increase time resolution in order to estimate ZWD in various situations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.230-231
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• 2023

This study proposes a deep reinforcement learning-based algorithm to automatically generate a ship's passage plan. First, Busan Port and Gwangyang Port were selected as target areas, and a container ship with a draft of 16m was designated as the target vessel. The experimental results showed that the ship's passage plan generated using deep reinforcement learning was more efficient than the Q-learning-based algorithm used in previous research. This algorithm presents a method to generate a ship's passage plan automatically and can contribute to improving maritime safety and efficiency.

• Journal of Navigation and Port Research
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• v.30 no.4
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• pp.259-265
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• 2006

This paper presents an improved adaptive neural network autopilot based on our previous study for track-keeping control of ships. The proposed optimal neural network controller can automatically adapt its learning rate and number of iterations. Firstly, the track-keeping control system of ships is described For the track-keeping control task, a way-point based guidance system is applied To improve the track-keeping ability, the off-track distance caused by external disturbances is considered in learning process of neural network controller. The simulations of track-keeping performance are presented under the influence of sea current and wind as well as measurement noise. The toolbox for track-keeping simulation on Mercator chart is also introduced.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.76-81
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• 2012

With the spread of the various mobile devices, the studies on the learning management system based on the u-learning are actively proceeding. The u-learning-based learning management system is very convenient in that there are no restrictions on the various access devices as well as the access time and place. However, the judgments on the authentication for the user and whether learning is focused on are difficult. In this paper, the voice and user face capture interface rather than the common user event oriented interface was applied to the learning management system. When a user is accessing the learning management system, user's registered password is input and login as voice, and the user's learning attitude is judged through the response utterance of simple words during the process of learning through contents. As a result of evaluating the proposed learning management system, the user's learning achievement and concentration were improved, thus enabling the manager to monitor the user's abnormal learning attitude.