• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.2
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• pp.109-116
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• 2010

We consider an information system where its throughput deteriorates due to security threats and evaluate information security investment portfolios. We assume that organizations adopt information security countermeasures (or portfolios consisted of countermeasures) to lessen the damage resulted from the productivity (or throughput) deterioration. A probability model is used to derive the system throughput and the average number of repairs according to the occurrence rate of security threats. Considering the revenue from throughput, the repair cost, and the investment for the security system, the net present value for each portfolio is derived. Organizations can compare information security investment portfolios and select the optimal portfolio.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.65-72
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• 2020

In this paper, we developed a web-based DApp system based on a private blockchain by applying machine learning techniques to automatically identify Android malicious apps that are continuously increasing rapidly. The optimal machine learning model that provides 96.2587% accuracy for Android malicious app identification was selected to the authorized experimental data, and automatic identification results for Android malicious apps were recorded/managed in the Hyperledger Fabric blockchain system. In addition, a web-based DApp system was developed so that users who have been granted the proper authority can use the blockchain system. Therefore, it is possible to further improve the security in the Android mobile app usage environment through the development of the machine learning-based Android malicious app identification block chain DApp system presented. In the future, it is expected to be able to develop enhanced security services that combine machine learning and blockchain for general-purpose data.

• Applied Chemistry for Engineering
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• v.32 no.1
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• pp.91-96
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• 2021

This study was conducted to improve the operating conditions of an adsorption tower filled with potassium impregnated activated carbon for high hydrogen sulfide capture capacity. Heat treatment modified the surface properties of activated carbon, and ultimately determined its adsorption capacity. The activated carbon doped with potassium showed 57 times more adsorption at room temperature than that of using the raw adsorbent. It is believed that uniform pore formation and strong bonding of the potassium on the surface of carbon contributed to the chemical and physical absorption of hydrogen sulfide. The SEM analysis on the surface structure of various commercial carbons showed that the modification of surface properties through the heat treatment generated the destruction of pore structures resulted in the decrease of the absorption performance. The pressure drop across the activated carbon bed was closely related with the grain size and shape. The optimum size of irregularly shaped activated carbon granules was 2~4 mesh indicating economical feasibility.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.91-97
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• 2020

Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1591-1604
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• 2020

This study is a study on classifying land cover by applying high-resolution satellite images to deep learning algorithms and verifying the performance of algorithms for each spatial object. For this, the Fully Convolutional Network-based algorithm was selected, and a dataset was constructed using Kompasat-3 satellite images, land cover maps, and forest maps. By applying the constructed data set to the algorithm, each optimal hyperparameter was calculated. Final classification was performed after hyperparameter optimization, and the overall accuracy of DeeplabV3+ was calculated the highest at 81.7%. However, when looking at the accuracy of each category, SegNet showed the best performance in roads and buildings, and U-Net showed the highest accuracy in hardwood trees and discussion items. In the case of Deeplab V3+, it performed better than the other two models in fields, facility cultivation, and grassland. Through the results, the limitations of applying one algorithm for land cover classification were confirmed, and if an appropriate algorithm for each spatial object is applied in the future, it is expected that high quality land cover classification results can be produced.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.662-674
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• 2021

Metabolic syndrome is closely associated with cardiovascular disease, there is increasing attentions in prevention of metabolic syndrome through prediction. The aim of this study was to systematically review the literature by collecting, analyzing, and synthesizing articles of predicting metabolic syndrome in Koreans. For systemic review, data search was conducted on Global journals Pubmed, WoS and domestic journals DBPia, KISS published in 2011-2020 year. Three keyword 'Metabolic syndrome', 'predict', and 'korea' were used for searching under AND condition. Total 560 articles were searched and the final 22 articles were selected according to the data selection criteria. The most useful variable was WHtR(AUC=0.897), most frequently used analysis method was logistic regression(63.6%), and most accurate analysis method was XGBOOST(AUC=0.879) for predicting metabolic syndrome. Prediction accuracy was slightly improved when sasang constitution types was used. Based on the results of this study, it is believed that various large-scale longitudinal studies for the prediction and management of the Metabolic syndrome in Korean should be followed in the future.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.19-26
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• 2021

A purpose of this study is to suggest distribution tariff regulation that encourages private investment on the energy efficiency industry of Ukraine. As the electricity market reform and the regulation introduction to encourage energy efficiency are ongoing in Ukraine, it is best time for Korean companies to enter to the market. Therefore, studies on the regulation and the market of Ukraine are required in advance. A simulation of private investment feasibility on AMI business is conducted on one of 32 DSOs in Ukraine. Through the simulation, the directions of RAB tariff regulation, which is the core of the distribution service tariff regulation, were derived. It is essential for DSOs to permit AMI lease assets, introduced by private investors, as regulated assets while other regulations are maintained as it is for investment. This study provides a practical basis by presenting objective data through simulation. It is expected to be helpful for overseas expansion of companies if the study is expanded to the various energy efficiency industries.

• Transactions of the Korean hydrogen and new energy society
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• v.32 no.6
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• pp.455-463
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• 2021

In this study, CeO₂ support is synthesized from cerium hydroxy carbonate prepared using precipitation/digestion method using KOH and K₂CO₃ as the precipitants. The Cu was impregnated to CeO₂ support with the different loading (Cu loading=10-40 wt. %). The prepared Cu/CeO₂ catalysts were applied to a single stage water gas shift (WGS) reaction. Among the prepared catalysts, the 20Cu/CeO₂ catalyst contained 20 wt.% of Cu showed the highest CO conversion (Xco=68% at 400℃). This result was mainly due to a large amount of active sites. In addition, the activity of the 20 Cu/CeO₂ catalyst was maintained without being deactivated for 100 hours because of the strong interaction between Cu and CeO₂. Therefore, it was confirmed that 20 Cu/CeO₂ is a suitable catalyst for a single WGS reaction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.87-92
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• 2022

In this paper, a study was conducted on the design of an array element that can be applied to the AESA radar for seeker. An antenna for application to AESA radar should choose an optimal radiation element to be applied to an array antenna in order to secure electronical beam steering characteristics, and consider beam steering characteristics when designing. In particular, in order to satisfy the wide-angle beam steering characteristics, the wide-angle impedance matching technique should be used to minimize the scan blindness region that may occur during wide-angle steering. As such, securing the stability of system operation is becoming an important design consideration for AESA radar. In this paper, WAIM is applied to the end of the radiation element to improve the characteristics of the radiation element applied to the AESA radar antenna device, and the change in the performance of the active reflection coefficient, which is a stable operation index of the system, is reviewed. The final performance result verified the validity of the proposed method by mathematically synthesizing the simulation data.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.1
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• pp.66-73
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• 2022

The purpose of this study was to develop self-heating concrete by utilizing the conduction resistance of concrete in order to reduce the risk of occurrence of black ice in the concrete pavement in winter and to prevent damage caused by freez-thawing effect. For this purpose, it was attempted to evaluate the strength and temperature exothermic characteristics using powder and liquid waste CNTs and a waste cathode agent as a conduction promotion. It was analyzed that liquid waste CNT had an effective dispersion degree in the mortar and a small decrease in strength occurred. In addition, DC 24 V was supplied by applying steel mesh, copper foil and copper wire to the mortar as electrodes, and the temperature change characteristics according to the mixing ratio of spent CNTs, anodes and carbon fibers were evaluated. In addition, by evaluating the temperature characteristics according to the electrode spacing from the selected optimal mixture, it was confirmed that it had sufficient heating characteristics up to an electrode spacing of 100 mm up to AC 50 V.