• Journal of Korea Game Society
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• v.20 no.2
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• pp.183-192
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• 2020

Due to the problem of matching between virtual world space and real-world space, the reality of augmented reality content is rather low. There are many reasons for this result, firstly virtual object is rendered without considering relation between real-world space and virtual world space. Secondly, virtual objects rely too much on the marker to reduce reality. We propose two schemes to improve the reality of augmented reality, one is people occlusion, the other is to reduce the dependence of virtual objects on the marker.

• Korean Circulation Journal
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• v.52 no.12
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• pp.853-864
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• 2022

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

• Journal of Korea Game Society
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• v.9 no.3
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• pp.43-52
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• 2009

Online game is the most popular representative example of the fast growing virtual world. Also, influence of interaction between virtual and real world is getting greater, and much discussion about virtual world and economy. However, it is often overlooked that the types of virtual economy can be very diverse depending on conditions of social and legal environment and policies of companies. Therefore, the influence of interactions between virtual world and real world is not managed by government and companies properly enough. We propose that virtual world is a system which has six types of agents and five types of transactions. Then there are twenty-four combinations, and we illustrated four typical combinations among them to explain practical applications of our model. The purpose of our categorization is not only to help to understand characteristics but also to provide insights for management and improvement for virtual worlds.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1427-1434
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• 2004

Database system benchmarks, which are usually evaluated to use the maximized resource in order to get the best results, arc not likely to simulate the real environment. We propose a workload generator that helps benchmarks be executed in the environment similar to a real world. The workload generator can create memory-bound, CPU-bound, and I/O-bound workloads. The workload generator allows users to create an integrated workload. which is similar to a real workload users run across in practice. Finally, we conducted the experiments that the Wisconsin benchmark was performed with the TPC-C and with the workload generation tool, and showed the feasibility of the proposed workload gen-eration tool comparing with two experimental results.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.290-293
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• 2009

In this paper, we propose a method to estimate pointing region in real-world from images of cameras. In general, arm-pointing gesture encodes a direction which extends from user's fingertip to target point. In the proposed work, we assume that the pointing ray can be approximated to a straight line which passes through user's face and fingertip. Therefore, the proposed method extracts two end points for the estimation of pointing direction; one from the user's face and another from the user's fingertip region. Then, the pointing direction and its target region are estimated based on the 2D-3D projective mapping between camera images and real-world scene. In order to demonstrate an application of the proposed method, we constructed an ICGS (interactive cinema guiding system) which employs two CCD cameras and a monitor. The accuracy and robustness of the proposed method are also verified on the experimental results of several real video sequences.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.

• Journal for History of Mathematics
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• v.33 no.6
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• pp.327-343
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• 2020

This study investigate philosophical discussions related to the Zeno's paradoxes in order to derive the mathematics educational implications. The paradox of Zeno's motion is sometimes explained by the calculus theories. However, various philosophical discussions show that the resolution of Zeno's paradox by calculus is not a real solution, and the concept of a continuum which is composed of points and the real number continuum may not coincide with the physical space and time. This is supported by the fact that the hyperreal number system of nonstandard analysis could be another model of a straight line or time and that an alternative explanation of Zeno's paradox was possible by the hyperreal number system. The existence of two different theories of the continuum suggests that teachers and students may not have the same view of the continuum. It is also suggested that the real world model used in school mathematics may not necessarily match the student's intuition or mathematical practice, and that the real world application of mathematics theory should be emphasized in education as a kind of 'correspondence.'

• The Journal of Korea Robotics Society
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• v.16 no.1
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• pp.49-55
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• 2021

Recently, a smart farm technology is drawing attention as an alternative to the decline of farm labor population problems due to the aging society. Especially, there is an increasing demand for automatic harvesting system that can be commercialized in the market. Pre-harvest crop detection is the most important issue for the harvesting robot system in a real-world environment. In this paper, we proposed a real-time tomato instance tracking algorithm by using deep learning and probability models. In general, It is hard to keep track of the same tomato instance between successive frames, because the tomato growing environment is disturbed by the change of lighting condition and a background clutter without a stochastic approach. Therefore, this work suggests that individual tomato object detection for each frame is conducted by YOLOv3 model, and the continuous instance tracking between frames is performed by Kalman filter and probability model. We have verified the performance of the proposed method, an experiment was shown a good result in real-world test data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.13-24
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• 2022

The main goal of this study is to look at how South Korea can catch up to the rest of the world through policy-driven structural change and manufacturing revolutions. To achieve the objective, this study used annual data on real exports and real GDP from the World Development Indicator WDI of South Korea for the period 1960 to 2019. The study's goal is to use econometrics to detect this policy-driven structural change trend. Multiple nonlinear Granger causality test was used to accomplish this. The findings revealed structural breaks and nonlinearities in the dynamic link between South Korea's real GDP and real exports. Furthermore, results also show evidence of multiple structural breaks in South Korean data. South Korea's economic catch-up was the result of a constant reevaluation of industrial policies, readjustment, and structural change to constantly explore and utilize comparative advantage, realizing economies of scale at the global level, and reallocating and redistribution of resources towards productive sectors with high value-added output, according to econometric analysis. If South Korea would have not done this structural change this miracle to escape the middle-income trap would not have been possible. These findings support the descriptive evidence of structural change in favor of manufacturing revolutions and value addition industry development in South Korea.