• 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.

• Structural Engineering and Mechanics
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• v.91 no.1
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• pp.75-86
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• 2024

In this paper, the porous metal PM-35 is proposed as the filler material of filled thin-walled tubes (FTTs), and a series of experimental study is conducted to investigate the dynamic behavior and energy absorption performance of PM-35 filled thin-walled tubes under impact loading. Firstly, cylinder solid specimens of PM-35 steel are tested to investigate the impact mechanical behavior by using the Split Hopkinson pressure bar set (SHP); Secondly, the filled thin-walled tube specimens with different geometric parameters are designed and tested to investigate the feasibility of PM-35 steel applied in FTTs by the orthogonal test. According to the results of this research, it is concluded that PM-35 steel is with the excellent characteristics of high energy absorption capacity and low yield strength, which make it a potential filler material for FTTs. The micron-sizes pore structure of PM-35 is the main reason for the macroscopic mechanical behavior of PM-35 steel under impact loading, which makes the material to exhibit greater deformation when subjected to external forces and obviously improve the toughness of the material. In addition, PM-35 steel core-filled thin-wall tube has excellent energy absorption ability under high-speed impact, which shows great application potential in the anti-collision structure facilities of high-speed railway and maglev train. The parameter V0 is most sensitive to the energy absorption of FTT specimens under impact loading, and the sensitivity order of different variations to the energy absorption is loading speed V0>D/t>D/L. The loading efficiency of the FTT is affected by its different geometry, which is mainly determined by the sleeve material and the filling material, which are not sensitive to changes in loading speed V0, D/t and D/L parameters.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.2
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• pp.331-346
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• 2018

In the tunnel of domestic high - speed railway, the main fire - fighting facility, fire - extinguishing passageway, is installed. However, due to the high pressure of the high - speed train, frequent breakage and maintenance are caused by strong shock and long - term vibration. In order to solve these problems, it is necessary to improve the fire door, but in Korea, it is installed by submitting a certificate by simple KS F 2296 performance test. At present, it is developed as a simple test certification by producing a real scale fireproof door without the theoretical examination in advance, so that a high cost for improvement is occurring in Korea. Therefore, through this study, structural analysis study which can preliminary structure review was carried out in order to design the refuge connection passage fire door and to improve the performance improvement. In order to secure the reliability of the result value, the official authentication test (KS F 2296) were compared.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.5
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• pp.473-481
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• 2017

Seafarer sea survival training, such as basic safety refresher training and advanced safety refresher training, in accordance with the STCW Convention, is an indispensable program that can increase the crew survival rate during emergency situations at sea. It is important for crew members to carry out theoretical and practical training with various safety equipment in order to effectively train according to IMO model courses. Therefore, this study suggests the following measures to improve safety training facilities for seafarers by reviewing survival training requirements based on the IMO model course and comparing and analyzing related facilities based on operating cases from domestic and overseas training institutes. First, it is necessary to establish a training environment where seafarers can practice utilizing various, updated safety equipment such as marine evacuation equipment (slides, chutes, etc.). Second, it is necessary to construct an educational environment in which learners can directly or indirectly experience realistic emergency situations by installing marine environment simulation facilities with such equipment as a wave generator, rain fall device, wind generating device, etc. Third, it is also necessary to develop and expand customized training using virtual reality equipment in addition to experiential training, audiovisual training and simulation training.

• Progress in Superconductivity
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• v.13 no.1
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• pp.58-63
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• 2011

Recently, superconductor flywheel energy storage systems (SFESs) have been developed for application to a regenerative power of train, a power quality improvement, the storage of distributed power sources such as solar and wind power, and a load leveling. As the high temperature superconductor (HTS) bearings offer dynamic stability without the use of active control, accurate analysis of the HTS bearing is very important for application to SFESs. Mechanical property of a HTS bearing is the main index for evaluating the capacity of an HTS bearing and is determined by the interaction between the HTS bulks and the permanent magnet (PM) rotor. HTS bearing rotor consists of PM and iron collector and the proper dimension design of them is very important to determine a supporting characteristics. In this study, we have optimized a rotor magnet array, which depends on the limited bulk size and performed various dimension layouts for thickness of the pole pitch and iron collector. HTS bearing rotor was installed into a single axis universal test machine for a stiffness test. A hydraulic pump was used to control the amplitude and frequency of the rotor vibration. As a result, the stiffness result showed a large difference more than 30 % according to the thickness of permanent magnet and iron collector. This is closely related to the bulk stiffness controlled by flux pining area, which is limited by the total bulk dimension. Finally, the optimized HTS bearing rotor was installed into a flywheel system for a dynamic stability test. We discussed the dynamic properties of the superconductor bearing rotor and these results can be used for the optimal design of HTS bearings of the 10kWh SFESs.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.9
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• pp.721-736
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• 2019

In this paper, the behavior of the fire smoke due to the operation of the ventilation systems when the fire occurred in the underground station (6 basement floors) and the tunnel at the great depth was measured. Fire smoke was generated by using a smoke generator which realized heat buoyancy effect by using hot air blower. The two locations of the fire were selected on the platform and on the platform of the tunnel located outside the screen door. A ventilation mode is generally used in which smoke is exhausted through a vent hole provided in a platform when a platform fire occurs. The tests were performed by operating the exhaust through the ventilation holes of the tunnel part located at both ends of the platform. The smoke density and the wind speed/velocity were measured at various positions, and the videos were taken to analyze the movement and smoke of the smoke. In both cases for fire inside the platform and in the railway tunnel, due to the ventilation mode operation of the fan for the platform and the exhaust of the fans in the tunnel smoke were well exhausted and the smoke propagation to the area near the smoke zone was suppressed. The smoke-control mode, which is applied to both fans for the platform and fans for in the tunnel at both ends of the platform, can provide a safer evacuation environment to the passengers from the fire smoke when the platform fire or fire train stops.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.