• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.103-114
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• 2019

Moving-target detection using frequency-modulated continuous-wave (FMCW) radar systems has recently attracted attention. Detection tasks are more challenging with noise resulting from signals reflected from strong static objects or small moving objects(clutter) within radar range. Robust Principal Component Analysis (RPCA) approach for FMCW radar to detect moving objects in noisy environments is employed in this paper. In detail, compensation and calibration are first applied to raw input signals. Then, RPCA via Gradient Descents (RPCA-GD) is adopted to model the low-rank noisy background. A novel update algorithm for RPCA is proposed to reduce the computation cost. Finally, moving-targets are localized using an Automatic Multiscale-based Peak Detection (AMPD) method. All processing steps are based on a sliding window approach. The proposed scheme shows impressive results in both processing time and accuracy in comparison to other RPCA-based approaches on various experimental scenarios.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.443-448
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• 2022

This paper presents a subband affine projection algorithm with variable step size to improve convergence performance in adaptive filtering applications with long adaptive filters and highly correlated input signals. The proposed algorithm can obtain fast convergence speed and small steady-state error by using different step sizes for each adaptive sub-filter in the subband structure to which polyphase decomposition and noble identity are applied. The step size derived to minimize the mean square error of the adaptive filter at each update time shows better convergence performance than the existing algorithm using a variable step size. In order to confirm the convergence performance of the proposed algorithm, which is superior to the existing algorithm, computer simulations are performed for mean square deviation(MSD) for AR(1) and AR(2) colored input signals considering the system identification model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3738-3760
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• 2022

Due to increasing spectrum demand for new wireless devices applications, cooperative spectrum sensing (CSS) paradigm is the most promising solution to alleviate the spectrum shortage problem. However, in the interweave cognitive radio (CR) system, the inherent nature of CSS opens a hole to Byzantine attack, thereby resulting in a significant drop of the CSS security and efficiency. In view of this, a weighted differential sequential single symbol (WD3S) algorithm based on MATLAB platform is developed to accurately identify malicious users (MUs) and benefit useful sensing information from their malicious reports in this paper. In order to achieve this, a dynamic Byzantine attack model is proposed to describe malicious behaviors for MUs in an interweave CR system. On the basis of this, a method of data transmission consistency verification is formulated to evaluate the global decision's correctness and update the trust value (TrV) of secondary users (SUs), thereby accurately identifying MUs. Then, we innovatively reuse malicious sensing information from MUs by the weight allocation scheme. In addition, considering a high spectrum usage of primary network, a sequential and differential reporting way based on a single symbol is also proposed in the process of the sensing information submission. Finally, under various Byzantine attack types, we provide in-depth simulations to demonstrate the efficiency and security of the proposed WD3S.

• Journal of Society of Preventive Korean Medicine
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• v.26 no.3
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• pp.1-16
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• 2022

Backgrounds : Doctor of Osteopathy (D.O.) in the United States have drawn attention as one of the future models of Korean Medicine doctors in Korea in that they have their own fields of care and therapies that distinguish them from medical doctor (M.D.), but are also able to carry out the treatment of general doctors. By analyzing D.O.'s specialization strategy, this study intends to preview points for establishing the future role of Korean Medicine doctors. Methods : We searched books, research papers, reports, conference presentations, and media articles, and chronologically classified and organized the collected data. In addition, the latest update information on related institutions' web pages and expert opinions released were also reviewed. Results : The D.O. emerged as a form of doctor in alternative medicine, however it rapidly turned to an M.D. substitute during the pandemic of the 1910s and World War II in the 1940s. Through the American Osteopathic Association (AOA)'s organizational activity, curriculum specialization, research development, and financial support, D.O. now has secured the status of M.D. in 50 states and federal law in the US. It has its own and exclusive full practice rights, capable of prescribing drugs and practicing surgery, as well as manual therapy. Beginning in July 2020, M.D.-D.O. achieved the full integration-unification of the professional training and residency program. Conclusions : In order to introduce the D.O. model to Korean Medicine system, it is necessary to strengthen biomedicine in the curriculum, and significantly expand the educational infrastructure and faculty manpower.

• Geomechanics and Engineering
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• v.32 no.4
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• pp.375-385
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• 2023

There are many joint fissures distributed in the engineering rock mass. In the process of geological history, the underground rock mass undergoes strong geological processes, and undergoes complex geological processes such as fracture breeding, expansion, recementation, and re-expansion. In this paper, the damage-stick-slip process (DSSP), an analysis model used for rock mass failure slip, was established to examine the master control and time-dependent mechanical properties of the new and primary fractures of a multi-fractured rock mass under the action of stress loading. The experimental system for the recemented multi-fractured rock mass was developed to validate the above theory. First, a rock mass failure test was conducted. Then, the failure stress state was kept constant, and the fractured rock mass was grouted and cemented. A secondary loading was applied until the grouted mass reached the intended strength to investigate the bearing capacity of the recemented multi-fractured rock mass, and an acoustic emission (AE) system was used to monitor AE events and the update of damage energy. The results show that the initial fracture angle and direction had a significant effect on the re-failure process of the cement rock mass; Compared with the monitoring results of the acoustic emission (AE) measurements, the master control surface, key blocks and other control factors in the multi-fractured rock mass were obtained; The triangular shaped block in rock mass plays an important role in the stress and displacement change of multi-fracture rock mass and the long fissure and the fractures with close fracture tip are easier to activate, and the position where the longer fractures intersect with the smaller fractures is easier to generate new fractures. The results are of great significance to a multi-block structure, which affects the safety of underground coal mining.

• Korean Journal of Heritage: History & Science
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• v.57 no.1
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• pp.6-19
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• 2024

The Korean Archaeological Dictionary, which represents Korean archaeological knowledge information, contains refined and high-quality information written by expert collective intelligence. This is a characteristic that clearly distinguishes it from overseas archaeological data archives, and can be called differentiated infrastructure data. However, it has not played a role as an information service or knowledge information platform reflecting the latest digital technology. As a way to maximize these strengths and compensate for weaknesses, it was proposed to develop and operate a GIS-based knowledge and information platform for Korean archaeology. To realize this, it is necessary to develop a title management system centered on repositories and metadata that can collect and store various information, link open linked data design and related systems, develop a search function that can analyze and visualize data in response to the big data era, and establish a WebGIS-based information service system. This will be a platform to continuously manage, supplement, and update Korean archaeological knowledge information, build a ubiquitous environment where anyone can use information anytime, anywhere, and create various types of business models.

• Economic and Environmental Geology
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• v.48 no.1
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• pp.51-63
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• 2015

I produced a secular variation model of geomagnetic field by using the magnetic component data from four geomagnetic observatories located in Northeast Asia during the years between 1997 and 2011. The Earth's magnetic field varies with time and location due to the dynamics of fluid outer core and the magnetic observatories on the surface measure in time series. To adequately represent the magnetic field or secular variations of the Earth, a spatio-temporal model is required. In making a global model, satellite observations as well as limited observatory data are necessary to cover the regions and time intervals. However, you need a considerable work and time to process a huge amount of the dataset with complicated signal separation procedures. When you update the model, the same amount of chores is demanded. Besides, the global model might be affected by the measurement errors of each observatory that are biased and the processing errors in satellite data so that the accuracy of the model would be degraded. In this study, as considered these problems, I introduced a localized method in modeling secular variation of the Earth's magnetic field over Northeast Asia region. Secular variation data from three Japanese observatories and one Chinese observatory that are all in the INTERMAGNET are implemented in the model valid between 1997 to 2011 with the interval of 6 months. With the resulting model, I compared with the global model called CHAOS-4, which includes the main, secular variation and secular acceleration models between 1997 to 2013 by using the three satellites' databases and INTERMAGNET observatory data. Also, the geomagnetic 'jerk' which is known as a sudden change in the time derivatives of the main field of the Earth, was discussed from the localized secular acceleration coefficients derived from spline models.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of the Korean GEO-environmental Society
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• v.18 no.2
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• pp.47-51
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• 2017

In this study, the optimization algorithm by inverse analysis that is the core of the adaptive management technique was adopted to update the soil engineering properties based on the ground response during the construction. Adaptive management technique is the framework wherein construction and design procedures are adjusted based on observations and measurements made as construction proceeds. To evaluate the performance of the adaptive management technique, the numerical simulation for the triaxial tests and the synthetic deep excavation were conducted with the Hardening Soil model. To effectively conduct the analysis, the effective parameters among the parameters employed in the model were selected based on the composite scaled sensitivity analysis. The results from the undrained triaxial tests performed with soft Chicago clays were used for the parameter calibration. The simulation for the synthetic deep excavation were conducted assuming that the soil engineering parameters obtained from the triaxial simulation represent the actual field condition. These values were used as the reference values. The observation for the synthetic deep excavation simulations was the horizontal displacement of the support wall that has the highest composite scaled sensitivity among the other possible observations. It was found that the horizontal displacement of the support wall with the various initial soil properties were converged to the reference displacement by using the adaptive management technique.