• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.77-85
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• 2010

As the ticket issuing methods have been diversified for the convenience of the passengers such as ticketless service(SMS ticket, e-ticket, home ticket), automatic ticket issuing machine and consignment ticket sale, maintaining the current number of ticket booth has been becoming a issue. This study is designed to simulation for the optimum number of ticket booth and which can affect an efficient operation of train station and improvement of customer convenience. This study will contribute to minimize customer waiting time at the ticket booth. In addition, presenting the optimum number of booth is expected to have an effect on the increase of productivity and cost savings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.51-58
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• 2019

The LTE system decided to build an integrated disaster safety communication network in order to cope with disasters quickly after the April 16, 2014 disaster, and 333 agencies in 8 areas will build a unified disaster safety communication network. It plans to integrate LTE-R, a railway integrated wireless network, and LTE-M, a land-based communication network, in the 700MHz band. LTE-R, a wireless integrated network, and the Ministry of Land, Transport and Maritime Affairs, are currently using VHF and TRS, which are voice communication-oriented communication systems, in order to cope with demand for high-speed railway service, With this possible LTE-R, it is possible to provide advanced services such as radio-based train control and improve railway safety. The company plans to invest KRW 1.1 trillion in new routes to be launched in 2018 and the existing route to be improved in the future, and to build up all routes of general and high-speed railways to LTE-R by 2027.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.3
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• pp.341-352
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• 2019

LTV(Light Tactical vehicle) operating in our military requires higher levels of performance and durability to withstand harsher conditions than ordinary vehicles, as they must travel on both rough-train and off-road as well as on public roads. Recently, developed light tactical vehicle is developed by a variety of test evaluations in order to satisfy ROC(Required Operational Capability) by the requirement military group. However, there is no standardized driving test condition for satisfying the durability performance of Korean tactical vehicle. Therefore, this study aims to provide basic data to establish reliable driving test conditions by analyzing the maneuver conditions and the driving data in order to select the representative drive course required. To do this, we analyzed the future operational environment, the area of operation analysis and the driving information of light tactical vehicle.

• Journal of IKEEE
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• v.22 no.4
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• pp.922-932
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• 2018

Radial distance sensors are widely used for surveying and autonomous navigation. It is necessary to train the operation principle of these sensors and how to apply them. Although commercialization of radial distance sensor continues to be cost-effective through lower performance, but it is still expensive for educational purposes. In this paper, we propose a distance sensor module with object tracking using radial array of low cost phototransistor which can be used for educational robot. The proposed method is able to detect the position of a fast moving object immediately by arranging the phototransistor in the range of 180 degrees and improve the sensing angle range and track the object by the sensor rotation using the servo motor. The scan speed of the proposed sensor is 50~200 times faster than the commercial distance sensor, thus it can be applied to a high performance educational mobile robot with 1ms control loop.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.36-43
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• 2021

In this research, the accuracy of YOLO v3 algorithm in object detection during AGV (Automated Guided Vehicle) operation was investigated. First of all, AGV with 2D LiDAR and stereo camera was prepared. AGV was driven along the route scanned with SLAM (Simultaneous Localization and Mapping) using 2D LiDAR while front objects were detected through stereo camera. In order to evaluate the accuracy of YOLO v3 algorithm, recall, AP (Average Precision), and mAP (mean Average Precision) of the algorithm were measured with a degree of machine learning. Experimental results show that mAP, precision, and recall are improved by 10%, 6.8%, and 16.4%, respectively, when YOLO v3 is fitted with 4000 training dataset and 500 testing dataset which were collected through online search and is trained additionally with 1200 dataset collected from the stereo camera on AGV.

• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.757-763
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• 2020

Objectives: The objective of this study was to evaluate the concentrations of airborne fungi in public transportation from autumnl 2016 to summer 2017. Methods: This study measured the concentrations of airborne fungi on six subway lines and intercity buses in Seoul. Results: The concentration of fungi in the air in public transportation was found to be lower than the standard (500 CFU/㎥) for vulnerable group facilities among public use facities. In summer, the concentration of airborne fungi was relatively higher than in autumn. The concentrations of airborne fungi in subway (252.0 CFU/㎥) and train (45.1 CFU/㎥) were high tendency during non-rush hours in summer, while intercity bus was hightendency during rush hours in summer (111.9 CFU/㎥). The major types of airborne fungi in public transportation were Cladosporium, Penicillium, and Aspergillus. Conclusions: The harmful airborne fungus were detected though they did not exceed the standard in all public transportation. As a result, further studies on the analysis of the distribution of airborne fungi by ventilation and the characterization of indoor environments are needed to propose effective management of airborne fungi in public transportation.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.135-142
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• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.27-34
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• 2023

This paper proposed an Inter-Carrier-Interference (ICI) Canceling Orthogonal Frequency Division Multiplexing (OFDM) receiver for 5G mobile system to support 500 km/h linear motor high speed terrestrial transportation service. A receiver in such high-speed train sees the transmission channel which is composed of multiple Doppler-shifted propagation paths. Then, a loss of sub-carrier orthogonality due to Doppler-spread channels causes ICI. The ICI Canceler is realized by the following three steps. First, using the Demodulation Reference Symbol (DMRS) pilot signals, it analyzes three parameters such as attenuation, relative delay, and Doppler-shift of each multi-path component. Secondly, based on the sets of three parameters, Channel Transfer Function (CTF) of sender sub-carrier number 𝒏 to receiver sub-carrier number 𝒍 is generated. In case of 𝒏≠𝒍, the CTF corresponds to ICI factor. Thirdly, since ICI factor is obtained, by applying ICI reverse operation by Multi-Tap Equalizer, ICI canceling can be realized. ICI canceling performance has been simulated assuming severe channel condition such as 500 km/h, 2 path reverse Doppler Shift for QPSK, 16QAM, 64QAM and 256QAM modulations. In particular, for modulation schemes below 16QAM, we confirmed that the difference between BER in a 2 path reverse Doppler shift environment and stationary environment at a moving speed of 500 km/h was very small when the number of taps in the multi-tap equalizer was set to 31 taps or more. We also confirmed that the BER performance in high-speed mobile communications for multi-level modulation schemes above 64QAM is dramatically improved by the use of a multi-tap equalizer.