• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.9 no.4
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• pp.204-211
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• 2004

Autonomous profiling CTD floats are a useful tool for observing the oceans. We, however, cannot perform post-deployment calibration of the CTD's attached to the floats, and the assessment of the accuracy and stability of the profile data from the floats is one of the important issues in the delayed mode quality control of the profiles. Variations in salinity in the intermediate level of East Sea is comparable to the accuracy of salinity data required by the international Argo Program, which is 0.01. Therefore, we can assess the credibility of salinity data from the floats deployed in the East Sea using three independent methods while considering the East Sea as a salinity calibration bath. The methods utilized here are 1) comparison of high quality CTD data and float data obtained at similar locations at similar time, 2) comparison of float data obtained at similar locations at similar time, and 3) investigation of long term stability and accuracy of salinity data from parking depths. All three methods show that without any calibration, the salinity data satisfy the accuracy criterion by the Argo Program. While assuming that the intermediate level temperature in the East Sea is as homogeneous as the salinity, we have applied the three methods to temperature data. We found that the accuracy of temperature reading is 0.01$^{\circ}C$, which is about twice larger than the requirement by the Argo Program, 0.005$^{\circ}C$. This does not mean that the temperature readings are inaccurate, because the intermediate level temperature does vary spacially and temporally more than the accuracy interval required by the Argo Program. If we take into account the variation in the intermediate level temperature, the accuracy of temperature data from the floats is not significantly different from that proposed by the Argo Program. Therefore, one could use both temperature and salinity profiles from the floats assessed in this study without calibration.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.1
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• pp.94-106
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• 2020

Recently, the radio navigation method utilizing the GPS(Global Positioning System) satellite information is widely used as the method to measure the position of objects. As GPS applications become wider and fields based on various positioning information emerge, new methods for achieving higher accuracy are required. In the case of autonomous vehicles, the INS(Inertial Navigation System) using the IMU(Inertial Measurement Unit), and the DR(Dead Reckoning) algorithm using the in-vehicle sensor, are used for the purpose of preventing degradation of accuracy of the GPS and to measure the position in the shadow area. However, these positioning methods have many elements of problems due not only to the existence of various shaded areas such as building areas that are continually enlarged, tunnels, underground parking lots and but also to the limitations of accumulation-based location estimation methods that increase in error over time. In this paper, an efficient positioning method in a large underground parking space using Fingerprint method is proposed by placing the AP(Access Points) and directional antennas in the form of four anchors using WLAN, a popular means of wireless communication, for positioning the vehicle in the GPS shadow area. The proposed method is proved to be able to produce unchanged positioning results even in an environment where parked vehicles are moved as time passes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.119-137
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• 2020

Over the last two decades, the attitudes to cars have changed from buying a car to sharing a car, especially among young people. Shared transport services and autonomous vehicles together can resolve the accessibility issue of shared transport services. Furthermore, they will make it possible to develop a new model of apartments without car parking. Therefore, the study estimated the demand for car sharing by young people and the running efficiency of car-sharing dealing with their car-based trip demand. The study chose nine apartment complexes for study sites where a majority of the residents were young people. The questionnaire survey was conducted to collect data on the trip demands of young people. The results showed that there are significant differences in the car-sharing use patterns and demand between the apartment houses located in the Capital region and non-capital region. Young people living in apartments in the Capital region used car sharing once per day per person for approximately 80 minutes per trip and tended to hire that between 8 AM and 10 AM. On the other hand, the young people living in apartments in the non-capital region used car sharing twice per day per person for approximately 200 minutes per trip. They tended to hire that frequently in the afternoon and evening as well as in the morning. The results also showed that a single car-sharing vehicle could deal with 3~4 trips per day in the Capital region and around 2 trips per day in the non-capital region.

• Smart Media Journal
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• v.11 no.11
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• pp.49-62
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• 2022

Recently, with the development of Smart City Solutions such as Big data, AI, IoT, Autonomous driving, and Digital twins around the world, the proliferation of various smart devices and social media, and the record of the deeds that people have left everywhere, the construction of Smart Cities using the "Big Data" environment in which so much information and data is produced that it is impossible to gauge the scale is actively underway. The Purpose of this study is to construct an objective and systematic analysis Model based on Big Data to improve the transportation convenience of citizens and formulate efficient policies in Urban Information and Public Transportation accessibility in sustainable Smart Cities following the 4th Industrial Revolution. It is also to derive the methodology of developing a Big Data-Based public transport accessibility and policy management Platform using a sustainable Urban Public DB and a Private DB. To this end, Detailed Living Areas made a division and the accessibility of basic living amenities of Gwangju Metropolitan City, and the Public Transportation system based on Big Data were analyzed. As a result, it was Proposed to construct a Big Data-based Urban Information and Public Transportation accessibility Platform, such as 1) Using Big Data for public transportation network evaluation, 2) Supporting Transportation means/service decision-making based on Big Data, 3) Providing urban traffic network monitoring services, and 4) Analyzing parking demand sources and providing improvement measures.