• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.354-362
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• 2015

In this study, for integrated management and inter-exchange of operational data generated by ships and land-side information on safe and business, a vessel communication system with modular functions was designed that applied high efficiency compression, least-cost algorithms and Inmarsat FBB connection automation system. Performance test at the KTsat Kumsan satellite earth station; system was found to delivered an average transfer speed of 7 kB/S, which was significant improvement from the existing commercial product's average speed of 5 kB/S. It also delivered twice the efficiency of the existing product in terms of compression rate and transfer of the most widely used office files in maritime businesses.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.227-232
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• 2004

Vehicle dynamics control systems are complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed supplies good results in normal conditions. But the estimation error in severe braking is discontented In this paper, we estimate the absolute vehicle speed of UCT(Unmanned Container Transporter) by using the wheel speed data from standard anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used 10 algorithms are verified experimentally to estimate the absolute vehicle speed and one of them is perfectly shown to estimate the vehicle speed within 4％ error during a braking maneuver.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.296-303
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• 2014

Recently a system is being required to replace ILS due to increasing air traffic. In this paper, Curved approach is applied to an airport where ILS approach cannot be applied due to its geographical condition and restricted aerospace condition, and verified by flight test. After analysing conditions of Tae-an airfield of Hanseo University with virtual ILS approach, airfield applicability was evaluated by Curved approach using by GPS. Normally simulation is performed after establishing approach procedure using electric map, but recently verification is being performed by flight test without simulation because accuracy and reliability are increased. In this paper, established procedure is verified modified by flight test with Pilot Test and Auto Pilot test and controllability and passenger's stability were also checked.

• Journal of Navigation and Port Research
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• v.29 no.1 s.97
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• pp.29-34
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• 2005

A mis-handling of the ship operators, treated as one qf the main causes of a ship accidents, normally has caused a ship to collide with obstacles like a reef, a rock and other ships etc. since their ability has been declining little by little even though the port conditions have been getting worse. The ship needs a highly sophisticated technology as her size and speed increase as the ship have been demanded. For example, Auto Avoidance Control System gradually has been receiving a growing interest to control the entire ship safely. From that purpose, this research has been done. The research was based on the MMG mathematical model, used Surge-Sway-Yaw-Roll motion equation and Fuzzy theory for calculating the collision-risk Also the research successively was done when the ship encountered continual multitude ships.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.111-115
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• 2024

This study builds a machine learning model optimized for clocks among various techniques in the field of artificial intelligence and applies it to clock stabilization or synchronization technology based on atomic clock noise characteristics. In addition, the possibility of providing stable source clock data is confirmed through the characteristics of machine learning predicted values during holdover of atomic clocks. The proposed machine learning model is evaluated by comparing its performance with the AutoRegressive Integrated Moving Average (ARIMA) model, an existing statistical clock prediction model. From the results of the analysis, the prediction model proposed in this study (MSE: 9.47476) has a lower MSE value than the ARIMA model (MSE: 221.2622), which means that it provides more accurate predictions. The prediction accuracy is based on understanding the complex nature of data that changes over time and how well the model reflects this. The application of a machine learning prediction model can be seen as a way to overcome the limitations of the statistical-based ARIMA model in time series prediction and achieve improved prediction performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2360-2366
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• 2011

The Unmanned Ground Vehicle is comprised of four systems of obstacle detection: The navigation system, vehicle controlling system, obstacle detecting and an integration system that use the various sensors. The research introduced utilizes 6 lasers to recognize obstacles. The system operates an avoidance system within the unmanned ground vehicle, using six lasers. The Unmanned Ground Vehicle's parallel parking and right angle parking is in development using algorithms. This algorithms' certification is intended to be installed in the encoder, in the GPS. By using the Laser Scannerfor the position's calculation, errors are both reduced and minimized, so the tire's slip minimized to the point where the vehicle had a limit of about 5Km/h.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.687-690
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• 2000

Lately, many studies have been progressed for the protection human's lives and property as holding in check accidents happened by human's carelessness or mistakes. One part of these is the development of an autonomouse vehicle. General control method of vision-based autonomous vehicle system is to determine the navigation direction by analyzing lane images from a camera, and to navigate using proper control algorithm. In this paper, characteristic points are abstracted from lane images using lane recognition algorithm with sobel operator. And then the vehicle is controlled using two proposed auto-steering algorithms. Two steering control algorithms are introduced in this paper. First method is to use the geometric relation of a camera. After transforming from an image coordinate to a vehicle coordinate, a steering angle is calculated using Ackermann angle. Second one is using a neural network algorithm. It doesn't need to use the geometric relation of a camera and is easy to apply a steering algorithm. In addition, It is a nearest algorithm for the driving style of human driver. Proposed controller is a multilayer neural network using Levenberg-Marquardt backpropagation learning algorithm which was estimated much better than other methods, i.e. Conjugate Gradient or Gradient Decent ones.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.381-382
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• 2007

Recently fishing industry is interested in efficient and automated fishing implementations to reach the level of the international leading technology. One of the important device used in fishing boat is an automated electric compass that harnesses the GPS and terrestrial magnetic sensor. The electric compass is desired to be minimized in size while keeping a high effectiveness in the characteristic of a magnetic compass. This device also can be used as a heading angle sensor to construct auto-navigation system in a small size of ships. However, there exists measurement errors induced from the slope of terrestrial magnetic sensor caused by the motion of boat. In this paper, a method has been proposed removing the measurement error arising from the slope of terrestrial magnetic sensor when the ship is in motion. We have designed a sensor with two DOF(degree of freedom) and a weight to maintain the horizontality of the sensor. Through this research, the hardware has been designed and also a test has been performed. The test shows a promissory result.

• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.418-426
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• 1994

Auto Navigation System is to provide a vehicle driver with more driving information by developing a computer-based system which supports advanced knowledge to a vehicle driving automation system and a driver. In this paper, we propose a new algorithm for the extraction of passing car which removes a background region using a series of images. First, we generate two difference images from three original images by getting the difference values between every two of them in sequence. Second, we generate two mask images from the two difference images. Finally, we extract passing car using the one original image and the two mask images. Using this algorithm we can extract the moving object in the outdoors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.289-295
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• 2021

Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods. The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.