• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.155-161
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• 2023

Vertigo refers to a state in which awareness related to the location, posture, movement, etc. of a helicopter is insufficient in space. It is easy to fall into flight illusion when flying in dense fog or night flight, and even if it has a wide field of view, it can be caused by visual causes such as cloud shapes, wind conditions, conditions of ground objects, and sensory causes such as changes in air posture or gravitational acceleration. The design and program of the motion system are studied that applied a six-axis motion system to a conventional commercial flight simulator program for pilot training, depending on the specificity of helicopter flight training that requires perception and sensitivity. Using the motion-based helicopter simulator produced in this study to train pilots, it is expected to have a positive effect in prevent of vertigo, where high performance could not be confirmed in the previously used visual-based simulation training device.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.362-368
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• 2024

In this paper, we investigated the performance of directional and omnidirectional precoding schemes when transmitting to improve downlink performance in massive MIMO. Omnidirectional precoding was used to broadcast a common signal, such as a synchronization or control signal, to all users. The main purpose of omnidirectional precoding is to design the precoding matrix so that the signal transmitted in the downlink is the same in all directions and emitted with maximum energy. We propose a flexible omnidirectional precoding method for full-dimensional massive MIMO that can set the spatial coverage range to less than 120 degrees. The constraints of omnidirectionality of all antennas, equal transmit power, and maximum transmit rate are used to design the encoding matrix of the proposed method. The performance was evaluated in terms of spatial coverage by considering changing the spatial coverage of the antenna array by changing the distance between neighboring antennas in the antenna array.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.4
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• pp.346-352
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• 2020

It has been about 30 years since women students entered the Maritime College at Mokpo National Maritime University (MMU) and Korea Maritime & Ocean University to train as maritime seafarers. The women have been choosing a maritime college regardless of the Boarding Service Reserve System. Therefore, it is necessary to continuously study the motivation for admission, preferences for boarding, and desired career paths to guide the distinction and vision of maritime colleges. Accordingly, this study conducted a questionnaire survey on 93 women students attending the Maritime College at MMU. Of the respondents, 35.5 % said that they enrolled to become maritime officials and 30.1 % to become maritime seafarers. In addition to the current training for maritime seafarers, additional courses are required to train maritime experts. The study found that 88.2 % of the respondents thought that women's embarkation was more difficult than usual. It is considered that a systematic education program is needed for the onboard life of women maritime seafarers in schools and shipping companies. It was found that 69.6 % of the respondents preferred to embark as seafarers after graduation. After graduating from university, 32.3 % of the respondents said that they preferred to become navigation officers or engineers. It was also found that 24.7 % preferred to become marine-related civil servants / professionals, and 18.3 % preferred to become marine police. From the total, 83.9 % hoped for careers in marine-related fields. It is, therefore, necessary to organize courses and further education according to the motives for admission and preferred occupations of women students.

• Journal of Korean Port Research
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• v.13 no.1
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• pp.101-112
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• 1999

Traffic accidents increase with the increase of the vehicles in operation on the street. Especially big traffic accidents composed of over 3 killed or 20 injured accidents with the property damage become one of the serious problems to be solved in most of the cities. The purpose of this study is to build the discrimination model on big traffic accidents using the Quantification II theory for establishing the countermeasures to reduce the big traffic accidents. The results are summarized as follows. 1)The existing traffic accident related model could not explain the phenomena of the current traffic accident appropriately. 2) Based on the big traffic accident types vehicle-vehicle, vehicle-alone, vehicle-pedestrian and vehicle-train accident rates 73%, 20.5% 5.6% and two cases respectively. Based on the law violation types safety driving non-fulfillment center line invasion excess speed and signal disobedience were 48.8%, 38.1% 2.8% and 2.8% respectively. 3) Based on the law violation types major factors in big traffic accidents were road and environment, human, and vehicle in order. Those factors were vehicle, road and environment, and human in order based on types of injured driver’s death. 4) Based on the law violation types total hitting and correlation rates of the model were 53.57% and 0.97853. Based on the types of injured driver’s death total hitting and correlation rates of the model were also 71.4% and 0.59583.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.165-165
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• 2017

As marine chemical spill incidents increase, and damages caused by chemical spills become bigger and bigger, it is required to educate and train professional personnel for response to chemical spill incidents at sea. In this study, the education contents of domestic and foreign institutions for the training of specialists in response to marine chemical accidents were examined, and a comparative analysis of education and training contents was carried out in order to utilize it in the development of domestic education and training materials for HNS response personnel in Republic of Korea.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.106-118
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• 2019

In order to develop pedestrian navigation service that provides optimal pedestrian routes based on pedestrian satisfaction levels, it is required to develop a prediction model that can estimate a pedestrian's satisfaction level given a certain condition. Thus, the aim of the present study is to develop a pedestrian satisfaction prediction model based on three machine learning algorithms: Logistic Regression, Random Forest, and Artificial Neural Network models. The 2009, 2012, 2013, 2014, and 2015 Pedestrian Satisfaction Survey Data in Seoul, Korea are used to train and test the machine learning models. As a result, the Random Forest model shows the best prediction performance among the three (Accuracy: 0.798, Recall: 0.906, Precision: 0.842, F1 Score: 0.873, AUC: 0.795). The performance of Artificial Neural Network is the second (Accuracy: 0.773, Recall: 0.917, Precision: 0.811, F1 Score: 0.868, AUC: 0.738) and Logistic Regression model's performance follows the second (Accuracy: 0.764, Recall: 1.000, Precision: 0.764, F1 Score: 0.868, AUC: 0.575). The precision score of the Random Forest model implies that approximately 84.2% of pedestrians may be satisfied if they walk the areas, suggested by the Random Forest model.

• Journal of Korean Port Research
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• v.6 no.1
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• pp.69-91
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• 1992

A new type of horizontal submerged break water or fixed structure to control waves near coastal area is introduced to focus wave energy before or behind it. Intentionally, the water depth near the structure is changed gradually to get a refraction and diffraction effect. The concentration of wave energy due to the structure was analyzed for the selected design of structure. The shape of the submerged structure in consideration is a circular combined with elliptical curve not to cause reflection of waves at the extreme edge of the structure but cause wave scattering. The direction of the structure against the incident wave is changed easily in the model Applying a regular wave train the following were examined. 1) whether a crescent plain submerged structure designed by the wave refraction theory can concentrate wave energy at a focal zone behind and before it without wave breaking phenomenon. 2) Location of maximum wave amplification factor in terms of the incident wave direction, wave period, etc. In any event the study would contribute to control waves near coastal area and to protect a beach from erosion without interruption of ocean view it is an useful study for the concentration of wave energy efficiently with the increase of wave height.

• Journal of Navigation and Port Research
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• v.36 no.3
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• pp.205-213
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• 2012

This study focused on the supply-demand and training system of the crew for domestic coastal shipping. First of all, it forecasted the prospect and effect in the future of the crew supply-demand through the analysis to the current situation of crew employment and the internal and external environment changes. Next, it suggested the specific role and alternatives of government, industry and educational institutions after the comparison and examination of the sailor policies among Korea and major shipping countries. In regard to the demand of crew manpower in coastal shipping, it figured out the bottoms and the current circumstances of sailors, and it could anticipate the future demand by the gradational approach. According to the findings, firstly the result of this simulation by the changes of the ship numbers demonstrated that the demand over the next 10 years will be 7,890~8,025 in the case of the growth 0.4%, and 7,894~8,063 in 0.5%. Secondly, assuming the growth 0.1~1%, the result illustrated that the demand will come to 7,879~8,258. This means the fact that the additional manpower has to be input to 20~430 annually from now on. To conclude, this study showed the more rational numbers about the supply-demand than the past researches and displayed the systematic approach to supply and train the crew in domestic coastal shipping.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.149-154
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• 2024

Research on particulate matter is advancing in real-time, and various methods are being studied to improve the accuracy of prediction models. Furthermore, studies that take into account various factors to understand the precise causes and impacts of particulate matter are actively being pursued. This paper trains an LSTM model using seasonal data and another LSTM model using concentration-based data. It compares and analyzes the PM_2.5 prediction performance of the two models. To train the model, weather data and air pollutant data were collected. The collected data was then used to confirm the correlation with PM_2.5. Based on the results of the correlation analysis, the data was structured for training and evaluation. The seasonal prediction model and the concentration-specific prediction model were designed using the LSTM algorithm. The performance of the prediction model was evaluated using accuracy, RMSE, and MAPE. As a result of the performance evaluation, the prediction model learned by concentration had an accuracy of 91.02% in the "bad" range of AQI. And overall, it performed better than the prediction model trained by season.