• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.192-207
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• 2020

We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• International Commerce and Information Review
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• v.19 no.4
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• pp.29-50
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• 2017

In emerging technologies, innovation processes are dynamic in that the government needs to regularly review its policies to resonate with rapid technological advancements, changing public needs, and evolving global trends. In the 1990s, the Internet grew at an explosive rate, but many applications were constrained due to security concerns. Public Key Infrastructure (PKI) seemed to be the fundamental technology to address these concerns by providing security functions. As of 2017, PKI is still one of the best technologies for electronic authentication in an open network, but it is used only in limited areas: for user authentications in closed networks and for server authentications within network security infrastructure like SSL/TLS. The difference between expectation and reality of PKI usage is due to the evolution of the Internet along with the global adoption of new authentication policies under the Internet governance in the early 2000s. The new Internet governance based on the cooperation between multi-stakeholders is changing the way in which a government should act with regard to its technological policies. This paper analyzes different PKI policy approaches in the United States and Korea from the perspective of path-dependence theory. Their different policy results show evidence of the rise of the Internet governance, and may have important implications for policy-makers in the current global Internet society.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.565-574
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• 2018

The purpose of this study is to investigate the features of science classes of Science Core Schools (SCSs) perceived by students. 654 students from 14 SCSs were surveyed with two open-ended questions on the features of science classes. The students' responses were analyzed with NetMiner 4.5, in terms of the centrality (of betweenness and of degree) analysis and the community analysis. The results of the research are as follows: (1) the science classes of SCSs were perceived by students to be of the environment of free questioning, active participation and communication, caring teacher, more science experiments and advanced contents, and knowledge sharing; (2) science classes in SCSs were perceived to be different from those of ordinary high schools because SCSs provide more opportunities for science-related special courses (like project work, advanced science subjects), extra-curricular activities, inquiry and research activities, school supports, hard-working classroom environment, longer studying hours, R&E and club activities. The students' perceptions of SCS science classes appear to be in line with the characteristics of 'good' science lessons from previous studies. The SCS project itself and the features of SCS science classes would help us to see how we introduce educational innovations into actual schools.

• Journal of Korea Game Society
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• v.15 no.5
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• pp.57-78
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• 2015

This Study is the effect of the inclusive institution on a nation's economic development. Therefore, we focused on the gaming industry as an index that can drive the economic growth in the future. The reason to compare the game institution in South Korea and Germany is that both countries began to develop the game by the State, but the game institution in South Korea and Germany at the present time are sharply opposed, because the institutions can focus on the main points that are experiencing this difference. The results of this study, first, open/closed network in institutionalized aspect affects the social status of the game. This second, game workers in the legal institution has been classified as artists in Germany, as addicts in South Korea. And, Germany also has incentives to creators protected profits reinvested in the gaming industry, Korea leads to punitive exploitation is being transferred to the group for addiction treatment that revenue. Third, this exclusive and inclusive institutional system could affect the stable growth of the game market. As a result, South Korea's state institutions will notice that you get a result away from opportunities for economic development due to the loss of inclusiveness.

• Journal of the Korea Society for Simulation
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• v.16 no.2
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• pp.27-35
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• 2007

When sensor networks are deployed in open environments, all the sensor nodes are vulnerable to physical threat. An attacker can physically capture a sensor node and obtain the security information including the keys used for data authentication. An attacker can easily inject false reports into the sensor network through the compromised node. False report can lead to not only false alarms but also the depletion of limited energy resource in battery powered sensor networks. To overcome this threat, Fan Ye et al. proposed that statistical on-route filtering scheme(SEF) can do verify the false report during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and energy, where security threshold value is the number of message authentication code for verification of false report. In this paper, we propose a fuzzy rule-based system for security threshold determination that can conserve energy, while it provides sufficient detection power in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the probability of a node having non-compromised keys, the number of compromised partitions, and the remaining energy of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.431-440
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• 2018

In this study, authors predicted probability of resignation of newly employed nurses using TensorFlow, an open source software library for numerical computation and machine learning developed by Google, and suggested strategic human resources management plan. Data of 1,018 nurses who resigned between 2010 and 2017 in single university hospital were collected. After the order of data were randomly shuffled, 80% of total data were used for machine leaning and the remaining data were used for testing purpose. We utilized multiple neural network with one input layer, one output layer and 3 hidden layers. The machine-learning algorithm correctly predicted for 88.7% of resignation of nursing staff with in one year of employment and 79.8% of that within 3 years of employment. Most of resigned nurses were in their late 20s and 30s. Leading causes of resignation were marriage, childbirth, childcare and personal affairs. However, the most common cause of resignation of nursing staff with in one year of employment were maladaptation to the work and problems in interpersonal relationship.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.321-334
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• 2020

The US Federal Reserve (Fed) has decided to cut interest rates. When we look at the expression of the FOMC statements at the time of policy change period we can understand that Fed has been communicating with markets through a change of word selection. However, there is a criticism that the method of analyzing the expression of the decision sentence through the context can be subjective and limited in qualitative analysis. In this paper, we evaluate the signaling effect of FOMC statements based on previous research. We analyze decision making characteristics from the viewpoint of text mining and try to predict future policy trend changes by capturing changes in expressions between statements. For this purpose, a decision tree and neural network models are used. As a result of the analysis, it can be judged that the discrepancy indicators between statements could be used to predict the policy change in the future and that the US Federal Reserve has systematically implemented policy signaling through the policy statements.

• Korean journal of food and cookery science
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• v.15 no.5
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• pp.500-506
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• 1999

Scanning electron microscopy was used to study changes in granule shape, dough and cooked noodle structure of pea starch-wheat composite flour with 20% and 30% pea starch substitution. The granule shape of pea starch with low swelling power and solubility was oval, irregular and smooth, which had more a deep groove than corn starch and wheat flour. During gelatinization, pea starch after swelling was partially collapsed but it still held its main shape. The dough microstructure of 20% pea starch substitution showed compact structure distributed with more small starch granules than wheat dough and was held in discontinuous network. When cooked, more open filamentous network where starch gelatinization was complete were noticed. Swollen but partially collapsed large starch granules maintaining their shape were appeared in noodle structure after 30 min soaking in soup. In farinograph studies, 20% pea starch substitution to wheat flour showed that MTI value was as same as wheat flour even though stability was slightly decreased so that it was considered that it has proper property of noodle making.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.31-39
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• 2015

This paper presents the static and kinematic positioning accuracy by the real-time GPS positioning modes of the low-cost GPS receivers using NTRIP-based augmentation service. For this, acquires both the raw measurements data of the field tests by LEA 6T GPS module of u-blox AG, and correction communication via NTRIP caster with RTKLIB as an open source program for GNSS solution. With computing the positions of the check points and road tracks by six kinds of GPS positioning modes which are Single, SBAS, DGPS, PPP, RTK, and TCP/IP_RTK, compared these results to the reference position of the check points. The position error average and rmse of the static test by GPS L1 RTK surveying showed $N=0.002m{\pm}0.001m$, $E=0.004m{\pm}0.001m$ in horizontal plane, and $h=-0.116m{\pm}0.003m$ in vertical, these results are very closed to the coordinates with the geodetic receiver. Especially, in case of the kinematic test with obstacles located on both sides of road, the computed track with ambiguity fixing showed very similar trajectory considerably from VRS network RTK mode. And also, evaluate and verify the performance of the TCP/IP_RTK mode developed based on TCP/IP protocol.