• Journal of Digital Convergence
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• v.19 no.10
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• pp.95-104
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• 2021

Software education is drawing attention as an education for fostering future talents who will lead the 4th industrial revolution. The purpose of software education for everyone from kinder to adults is not simply to develop programming skills, but to develop "Computational Thinking," a problem-solving ability that effectively solves real-world problems based on computing. Therefore, how to cultivate and evaluate computational thinking is a very important issue. This paper proposed a method of applying a process-based performance evaluation method to evaluate computational thinking ability in the process of solving learners' problems. The developed contents were revised and supplemented through two Delphi surveys by a group of experts consisting of five university professors and five incumbent information teachers majoring in computer science and computer education to verify the effectiveness of the final model. I hope This paper can contribute to the study of evaluating computer thinking ability from the perspective of problem solving.

• Journal of Korea Trade
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• v.23 no.3
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• pp.118-137
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• 2019

Purpose - As global trade disputes intensify and global trade uncertainty increases due to the prevailing trade protectionism all over the world, mega-FTAs such as the RCEP and CPTPP are suggested as strategic trade policy options for export-driven small open economies, such as Korea. This paper aims to provide a comprehensive analysis of Korea's mega-FTA participation and the induced implications for the Korean economy. Design/methodology - We use a multi-region, multi-sector global CGE model, and investigate the different effects of both the US-China and US-EU trade wars on the relative changes in GDP, welfare, and trade under different trade policy regimes; (i) Korea does not participate in any mega-FTA, (ii) Korea participates in the RCEP, and (iii) Korea participates in the CPTPP. Findings - We show, among others, that though industrial effects might be largely varied, the overall enlarging of free trade zones through multilateral mega-FTA participation may contribute significantly to the macroeconomic soundness and stability of Korea, even when global trade protectionism prevails. Under RCEP and CPTPP trade regimes, Korea's GDP may increase even when the global trade environment deteriorates as trade wars occur and intensify between the US and China, or between the US and EU. It is also estimated that RCEP participation increases Korea's GDP, welfare (measured in equivalent variation), and total trade by 1.12%, $1.09 billion, and 2.54%, respectively, while CPTPP participation increases them by 0.19%, $0.92 billion, and 0.13%, respectively. Originality/value - Existing studies usually focus on the direct impacts of mega-FTA participation on macroeconomic variables such as GDP, welfare, and trade, and do not consider the possible buffer effects of a mega-FTA when the global trade environment worsens. In this paper, we analyze and quantify not only the direct impacts of RCEP and CPTPP on the main macroeconomic variables but also the possible buffer effects of the RCEP and CPTPP in the cases of the US-China and US-EU trade wars.

• Psychiatry investigation
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• v.15 no.12
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• pp.1162-1167
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• 2018

Objective Motor, perceptual, and cognitive functions are known to affect driving competence. Subcortical ischemic changes on brain magnetic resonance imaging (MRI) can reflect reduction in cognitive and motor performance. However, few studies have reported the relationship between subcortical ischemic changes and driving competence of the elderly. Thus, the objective of this study was to investigate the association between subcortical ischemic changes on MRI and driving abilities of the elderly. Methods Participants (n=540) were drawn from a nationwide, multicenter, hospital-based, longitudinal cohort. Each participant underwent MRI scan and interview for driving capacity categorized into 'now driving' and 'driving cessation (driven before, not driving now)'. Participants were divided into three groups (mild, n=389; moderate, n=116; and severe, n=35) depending on the degree of white matter hyperintensity (WMH) on MRI at baseline. Driving status was evaluated at follow-up. Statistical analyses were conducted using ${\chi}^2$ test, analysis of variance (ANOVA), structured equation model (SEM), and generalized estimating equation (GEE). Results In SEM, greater baseline degree of WMH was directly associated with driving cessation regardless of cognitive or motor dysfunction (${\beta}=-0.110$, p<0.001). In GEE models after controlling for age, sex, education, cognitive, and motor dysfunction, more severe change in the degree of WMH was associated with faster change from 'now driving' state to 'driving cessation' state over time in the elderly (${\beta}=-0.508$, p<0.001). Conclusion In both cross-sectional and longitudinal results, the degree of subcortical ischemic change on MRI might predict driving cessation in the elderly.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.865-889
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• 2019

The need for cyber resilience is increasingly important in our technology-dependent society where computing devices and data have been, and will continue to be, the target of cyber-attackers, particularly advanced persistent threat (APT) and nation-state/sponsored actors. APT and nation-state/sponsored actors tend to be more sophisticated, having access to significantly more resources and time to facilitate their attacks, which in most cases are not financially driven (unlike typical cyber-criminals). For example, such threat actors often utilize a broad range of attack vectors, cyber and/or physical, and constantly evolve their attack tactics. Thus, having up-to-date and detailed information of APT's tactics, techniques, and procedures (TTPs) facilitates the design of effective defense strategies as the focus of this paper. Specifically, we posit the importance of taxonomies in categorizing cyber-attacks. Note, however, that existing information about APT attack campaigns is fragmented across practitioner, government (including intelligence/classified), and academic publications, and existing taxonomies generally have a narrow scope (e.g., to a limited number of APT campaigns). Therefore, in this paper, we leverage the Cyber Kill Chain (CKC) model to "decompose" any complex attack and identify the relevant characteristics of such attacks. We then comprehensively analyze more than 40 APT campaigns disclosed before 2018 to build our taxonomy. Such taxonomy can facilitate incident response and cyber threat hunting by aiding in understanding of the potential attacks to organizations as well as which attacks may surface. In addition, the taxonomy can allow national security and intelligence agencies and businesses to share their analysis of ongoing, sensitive APT campaigns without the need to disclose detailed information about the campaigns. It can also notify future security policies and mitigation strategy formulation.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.44-54
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• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• Geomechanics and Engineering
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• v.24 no.3
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• pp.253-266
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• 2021

The sufficient early strength of primary support is crucial for stabilizing the surroundings, especially for the tunnels constructed in soil. This paper introduces the Steel-Concrete Composite Support System (SCCS), a new support with high bearing capacity and flexible, rapid construction. The bearing characteristics and construction performance of SCCS were systematically studied using a three-dimensional numerical model. A sensitivity analysis was also performed. It was found that the stress of a π-shaped steel arch decreased with an increase in the thickness of the wall, and increased linearly with an increase in the rate of stress release. In the horizontal direction of the arch section, the nodal stresses of the crown and the shoulder gradually increased in longitudinally, and in the vertical direction, the nodal stresses gradually decreased from top to bottom. The stress distribution at the waist, however, was opposite to that at the crown and the shoulder. By analyzing the stress of the arch section under different installation gaps, the sectional stress evolution was found to have a step-growth trend at the crown and shoulder. The stress evolution at the waist is more likely to have a two-stage growth trend: a slow growth stage and a fast growth stage. The maximum tensile and compressive stresses of the secondary lining supported by SCCS were reduced on average by 38.0% and 49.0%, respectively, compared with the traditional support. The findings can provide a reference for the supporting technology in tunnels driven in loess.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2148-2167
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• 2021

The proliferation of the Internet of Things (IoT) technologies and applications, especially the rapid rise in the use of mobile devices, from individuals to organizations, has led to the fundamental role of secure wireless networks in all aspects of services that presented with many opportunities and challenges. To ensure the CIA (confidentiality, integrity and accessibility) security model of the networks security and high efficiency of performance results in various resource-constrained applications and environments of the IoT platform, DDO-(data-driven operation) based constructions have been introduced as a primitive design that meet the demand of high speed encryption systems. Among of them, the TMN-family ciphers which were proposed by Tuan P.M., Do Thi B., etc., in 2016, are entirely suitable approaches for various communication applications of wireless mobile networks (WMNs) and advanced wireless sensor networks (WSNs) with high flexibility, applicability and mobility shown in two different algorithm selections, TMN64 and TMN128. The two ciphers provide strong security against known cryptanalysis, such as linear attacks and differential attacks. In this study, we demonstrate new probability results on the security of the two TMN construction versions - TMN64 and TMN128, by proposing efficient related-key recovery attacks. The high probability characteristics (DCs) are constructed under the related-key differential properties on a full number of function rounds of TMN64 and TMN128, as 10-rounds and 12-rounds, respectively. Hence, the amplified boomerang attacks can be applied to break these two ciphers with appropriate complexity of data and time consumptions. The work is expected to be extended and improved with the latest BCT technique for better cryptanalytic results in further research.

• Journal of Chest Surgery
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• v.54 no.3
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• pp.186-190
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• 2021

Background: Pre-lifting of the sternum marked a major turning point in pectus excavatum repair. The author developed the crane technique in 2002 and successfully applied it to more than 2,000 cases using sternal wire stitching. However, blind sternal suturing limited the use of the wire-stitch crane. We propose a novel screw for sternal lifting as a new tool for the crane technique. Methods: We developed a screw system strong enough to withstand the pressure needed for sternum lifting. The screw was designed to have a broader thread to hold the bony tissue securely. The screw's sustaining power was tested using the torsion, driving torque, and axial pull-out tests in a polyurethane block and ex-vivo porcine sternum. Results: The screws were easily driven into the sternum, and the head of the screw was connectable to the table-mounted retractor. In the torsion test, the 2° offset torsional yield was 4.53 N·m (reference value, 1 N·m). In the polyurethane block driving torque test, the maximum torque was 0.98 N·m (reference value, 0.70 N·m). The axial pull-out test was 446 N (reference value, 100 N). The maximum pull-out resistance in the ex-vivo porcine sternum model was 1,516 N. Conclusion: The screw crane was strong enough to sustain the chest wall weight to be lifted. Thus, the screws could effectively replace the sternal wire stitching in crane pre-lifting of the sternum. We expect that application of the screw-crane will be easy and that it will improve the safety and success rate of pectus repair surgery.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.261-270
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• 2020

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify "topics" that are corresponding to "key" technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these "key" technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the "key" promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.