• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.507-512
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• 2008

This study investigated the navigation ability of patients with MCI in Virtual Environments(VE) and on the visual functioning. The participants consisted of elderly adults with/without MCI. Neuropsychological tests(RCFT, BVRT, TMT, and Digit Span), the Groton Maze Learning Test(12trials), and the VE navigation learning task(6 trials) were performed. As a result, there were significant group differences for the RCFT and BVRT, but not for the GMLT. For the VE task, there was a significant difference between the MCI and normal group and no interactions between the groups and trials were found. The VE task was correlated with The RCFT, the BVRT, and the GMLT and omnibus the RCFT and the BVRT accounted for 45% of VE performances. Thus, we concluded that patients with MCI are inferior to VE navigation and visual retention/memory play a role in navigation abilities.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.4
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• pp.173-180
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• 2016

This paper presents a method to suppress multipath induced by pulses using supervised learning. In modern electronics, pulses have been used for various purposes such as communication or distance measurements. Like other signals, pulses also suffer from multipath. When a pulse and a multipath are overlapped, the original pulse shape is distorted. The distorted pulse could result in communication failures or distance measurement errors. However, a large number of samples available from a pulse can be used to effectively reject multipath by using a supervised learning method. This paper introduces how a supervised learning method can be applied to Distance Measuring Equipment. Simulation results show that multipath induced distance measuring error can be suppressed by 10 ~ 45 percent depending on the allowed pulse shape variation allowed in a standard.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.383-384
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• 2022

Gas Combustion Unit (GCU) onboard liquefied natural gas carriers handles boil-off to stabilize tank pressure. There are many factors for LNG cargo operators to take into consideration to determine whether to use GCU or not. Gas consumption of main engine and re-liquefied gas through the Partial Re-Liquefaction System (PRS) are good examples of these factors. Human gas operators have decided the operation so far. In this paper, some deep learning neural network models were developed to provide human gas operators with a decision support system. The models consider various factors specially into GCU operation. A deep learning model with Sigmoid activation functions in input layer and hidden layers made the best performance among eight different deep learning models.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.41-42
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• 2018

This study is about the prediction of ship's roll motion characteristic which has been used for evaluating ship's seakeeping performance. In order to obtain the ship's roll RAO during voyage, this paper utilized machine learning-based surrogate model. By comparing the prediction result data of surrogate model with test data, we suggest the best approximation technique and data sampling interval of the surrogate model appropriate for predicting the ships' roll motion characteristic.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1994.04a
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• pp.55-67
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• 1994

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics ar low speed. In this paper the authors propose a new berthing control system which can evaluate as closely as captain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's decision-making by using the FNN(Fuzzy neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS90 MK III) and represent the ship motion characteristics internally According to learning procedure both FNN controllers can tune membership functions and identify fuzzy control rules automatically The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.

• Journal of the Korean Institute of Navigation
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• v.18 no.2
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• pp.69-81
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• 1994

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model, but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics at low speed. In this paper, the authors propose a new berthing control system which can evaluate as closely as cap-tain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS-90 MK Ⅲ) and represent the ship motion characteristics internally. According to learning procedure, both FNN controllers can tune membership functions and identify fuzzy control rules automatically. The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.395-405
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• 2018

Seakeeping safety module in Korean e-Navigation system is one of the ship remote monitoring services that is employed to ensure the safety of ships by monitoring the ship's real time performance and providing a warning in advance when the abnormal conditions are encountered in seakeeping performance. In general, seakeeping performance has been evaluated by simulating ship motion analysis under specific conditions for its design. However, due to restriction of computation time, it is not realistic to perform simulations to evaluate seakeeping performance under real-time operation conditions. This study aims to introduce a reasonable and faster method to predict a ship's roll motion which is one of the factors used to evaluate a ship's seakeeping performance by using a machine learning-based surrogate model. Through the application of various learning techniques and sampling conditions on training data, it was observed that the difference of roll motion between a given surrogate model and motion analysis was within 1%. Therefore, it can be concluded that this method can be useful to evaluate the seakeeping performance of a ship in real-time operation.

• Journal of Advanced Navigation Technology
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• v.15 no.5
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• pp.857-864
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• 2011

Today's education make an effort to be done on self-directed learning away from teaching students through a passive learning. In this paper, we implemented leveled incorrect analysis system based on SCORM which targets elementary students in mathematics. This system can analyze learner's weak points of problem scope and problem ability. Also, This led to higher academic achievement in the low level group by compensatory learning of incorrect analysis system according to individual level.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.10a
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• pp.176-183
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• 2006

A novel intelligent anti-collision decision-making supporting system is addressed in this paper. To obtain precise anti-collision information capability, an innovative neurofuzzy network is proposed and applied. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to train the parameters of the Fuzzy Inference System (FIS). The learning process is based on a hybrid learning algorithm and off-line training data. The training data are obtained by trial manoeuvre. This neurofuzzy network can be considered to be a self-learning system with the ability to learn new information adaptively without forgetting old knowledge. This supporting system can decrease ship operators' burden to deal with bridge data and help them to make a precise anti-collision decision.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.1
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• pp.45-49
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• 2022

In this paper, we propose a method for estimating the error of the Ultra-Wideband (UWB) distance measurement using the channel impulse response (CIR) of the UWB signal based on machine learning. Due to the recent demand for indoor location-based services, wireless signal-based localization technologies are being studied, such as UWB, Wi-Fi, and Bluetooth. The constructive obstacles constituting the indoor environment make the distance measurement of UWB inaccurate, which lowers the indoor localization accuracy. Therefore, we apply machine learning to learn the characteristics of UWB signals and estimate the error of UWB distance measurements. In addition, the performance of the proposed algorithm is analyzed through experiments in an indoor environment composed of various walls.