• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.289-295
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• 2021

Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods. The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.17-23
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• 2016

I think it is valuable to make Chinese ancient character 'Inscriptions on Bones and Tortoise Carapaces' hieroglyphics popular by combining with the digital. Because Chinese characters are ideograms, it has a huge visual effect. When it is thought over meanings with imaged data by using digital technology, it can be easier to conflate Chinese academia and humanities academia, moreover other academia that uses Chinese characters such as jurisprudence or business administration. I believe not only union of humanities and natural science in the global era can be the best example of the word 'consilience', which I mentioned before, but also people enjoy usability from educations, invention of learning contents or open learning to a cultural field by coming into wide use. Futhermore, it should contribute to changing the image of Chinese characteristics from existing difficult stereotype to positive image.

• Asian review of World Histories
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• v.4 no.1
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• pp.3-34
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• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Journal of Engineering Education Research
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• v.20 no.2
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• pp.39-49
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• 2017

This research aims to explore the relationships between 'teamwork skills' (often called team activity competence) and 'thinking styles' of engineering students in Korea, and to provide an explanation for the application of team-based environment as well as for the increase of teamwork skills. Teams and team activity are pervasive in today's organization and there has been relatively much research on teamwork skills and its related factors. However, to date, little attention has been paid to the teamwork skills, essential factor in team-based environment and its relationships with thinking styles. This study was conducted with 383 engineering students at P University, and students' teamwork skills as well as thinking styles have been measured before and after team-based learning class (hereafter TBL). Our findings show that firstly, there was a significant increase of teamwork skills between before and after TBL class. Second, team activity competence was found to have a higher correlation with most of creativity generating styles (i.e. legislative, judicial, hierarchical and global styles). Third, hierarchical style was found to influence team activity more than other components, and also legislative, external, global and judicial styles contributed to team-based activity. These findings are expected to provide an explanation for the application of thinking styles in team-based environment and will be useful for the improvement of related courses in engineering school.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.93-102
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• 2021

The world is facing an unprecedented economic, social and political crisis with the spread of COVID-19. The Corona Virus (COVID-19) and its global spread have resulted in declaring a pandemic by the World Health Organization. The deadly pandemic of 21st century has spread its wings across the globe with an exponential increase in the number of cases in many countries. The developing and underdeveloped countries are struggling hard to counter the rapidly growing and widespread challenge of COVID-19 because it has greatly influenced the global economies whereby the underdeveloped countries are more affected by its devastating impacts, especially the life of the low-income population. Information and Communication Technology (ICT) were particularly useful in spreading key emergency information and helping to maintain extensive social distancing. Updated information and testing results were published on national and local government websites. Mobile devices were used to support early testing and contact tracing. The government provided free smartphone apps that flagged infection hotspots with text alerts on testing and local cases. The purpose of this research work is to provide an in depth overview of emerging technologies and recent ICT developments to combat COVID-19 Pandemic. Finally, the author highlights open challenges in order to give future research directions.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• Journal of Korean Elementary Science Education
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• v.25 no.4
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• pp.383-395
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• 2006

School science is often criticized as being too remote from both learners' interests and needs and as maintaining scientist-oriented approaches rather than humanistic ones. Although science is mainly taught on the basis of textbooks inside classrooms, the learning of science can not be confined to the boundaries of curriculum and school. Firstly, this paper briefly reviews and characterizes the historical development of science education with a series of analogies, and then suggests a new analogy, a so-called 'Hearts-On' approach to science education which emphasizes the humanistic aspects and the contextual dimension of science education. Secondly, it critically examines how much traditional school science teaching, particularly in physics, is limited in terms of the context of learning (i.e. textbook, laboratory, classroom, local, and global) as well as in terms of the context of the contents (i.e. physical, personal, social, and global). Thirdly, some recent attempts initiated by the author and colleagues are explained as examples of the Hearts-On approach to science education. In particular, a series of community-based science programs led by SNU and the development of a series of books on 'Contextual Physics'(i.e. Body Physics, Wearing Physics, Dining Table Physics, and Sports Physics) are outlined. Finally, the idea of scientific humanism is explored in relation to the context-rich approaches in science education. It is hoped that this paper helps us to reconsider how we can expand the world of science education beyond the boundaries of the curriculum and school and into a more humanistic one.

• Journal of Ship and Ocean Technology
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• v.12 no.4
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• pp.1-6
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• 2008

Nowadays, the computational capability and graphical power based on PCs increase very rapidly every year. As a result, the complicated engineering or scientific problems that could have only been handled by supercomputers a couple of decades ago can now be routinely run on PCs. Besides, the PCs can be assembled in parallel to increase its computational capability theoretically without limitation. The Web-based interface and communication tools are also being enhanced very rapidly and the real-time distance learning (E-Learning) and project cooperation on web get increasing attention. Using the-state-of-the-art computational method, a number of complicated and computationally intensive problems are being solved by PCs. The results can be well demonstrated on screen by graphics and animation tools. Those examples include the simulations of fully nonlinear waves, their interactions with floating bodies, global-motion analysis of multi-unit floating production system including complicated mooring lines and risers. Several examples will be presented in this regard. Also, Web and java-applet based educational tools have been developed at Texas A&M University for better understanding of waves and wave-body interactions. The background and examples of such Web-based educational tools published in Kim et al. (2003) are briefly introduced here.

• Journal of Information Technology Services
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• v.17 no.4
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• pp.119-137
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• 2018

Recently, the start-up firms have been under the spotlight because they can create many jobs and are regarded as the growth engine of a country in the fourth industrial revolution. Moreover, after the global financial debacle, many countries emphasize on an entrepreneurial spirit and venture firms. they also pay attention to the importance of educations to promote the start-up firms. In this work, based on technology acceptance model, we try to identify the main factors of accepting online start-up education system. We first defined this system as the online start-up education system which refers to a system to support learning for knowledge, experiences and overall process of start-up firms' tasks. This system also simulates tasks related with prep-entrepreneur so that can be regarded as a kind of decision support systems. As this system has some characteristics, we confirmed some factors of extended technology acceptance model. In our experiments, we find that the perceived ease of use and trust are main factors of acceptance for the online start-up education systems. These results mean that the system should be made to be easy and trustful for their users. Therefore, we expect that this work will be helpful to researchers and developers who consider start-up education systems and provide a way to activate founding start-up firms.