• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.938-943
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• 2018

Thin nano-sized fibres were prepared by an electrospinning method. The spinning appratus consisted of pump for polymer injection, nozzle and nozzle rotus, and an aluminum plate collected the polymer fibers. Its surface was chemically modified for selective improved adsorption of carbon monoxide at indoor level. The chemical activation enabled to form the fibres 250-350 nm in thickness with pore sizes distributed between 0.6 and 0.7 nm and an average specific surface area of $569m^2/g$. The adsorption capacities of pure (100%) and indoor (0.3%) $CO_2$, of which level frequently appears, at the ambient condition were improved from 1.08 and 0.013 to 2.2 and 0.144 mmol/g, respectively. It was found that the adsorption amount of $CO_2$ adsorbed by the chemically activated carbon nanofiber prepared through chemical activation would vary depending on the ratio of specific surface area and micropores. In particular, chemical interaction between adsorbent surface and gas molecules could enhance the selective capture of weak acidic $CO_2$.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.571-581
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• 2020

Vegetation affects water level change and flow resistance in rivers and impacts waterway ecosystems as a whole. Therefore, it is important to have accurate information about the species, shape, and size of any river vegetation. However, it is not easy to collect full vegetation data on-site, so recent studies have attempted to obtain large amounts of vegetation data using terrestrial laser scanning (TLS). Also, due to the complex shape of vegetation, it is not easy to obtain accurate information about the canopy area, and there are limitations due to a complex range of variables. Therefore, the physical structure of vegetation was analyzed in this study by reconfiguring high-resolution point cloud data collected through 3-dimensional terrestrial laser scanning (3D TLS) in a voxel. Each physical structure was analyzed under three different conditions: a simple vegetation formation without leaves, a complete formation with leaves, and a patch-scale vegetation formation. In the raw data, the outlier and unnecessary data were filtered and removed by Statistical Outlier Removal (SOR), resulting in 17%, 26%, and 25% of data being removed, respectively. Also, vegetation volume by voxel size was reconfigured from post-processed point clouds and compared with vegetation volume; the analysis showed that the margin of error was 8%, 25%, and 63% for each condition, respectively. The larger the size of the target sample, the larger the error. The vegetation surface looked visually similar when resizing the voxel; however, the volume of the entire vegetation was susceptible to error.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.557-569
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• 2021

In preparation for the future society, the educational curriculum is changing according to the trend of the times, and with the advent of the era of the 4th Industrial Revolution, the purpose of the new 2015 revised curriculum was suggested to foster the convergence creativity of students. The purpose of software education is to promote creativity and further develop problem-solving skills in connection with real life. In addition, flow in learning leads to outstanding educational achievement. However, in elementary school computer education, there is still a lack of development of a convergence class model for students to easily immerse themselves and promote creative problem-solving skills. Therefore, in this study, we designed convergence computer education using Novel Engineering, which is a convergence class model suitable for these educational conditions and applied it to classes. Further, to measure the effect on the improvement of learning flow and creative problem-solving ability. the Novel Engineering-based computer class was applied to the experimental group for 6th graders, and the general computer class was applied to the control group. As a result of the pre-post test between groups, it was found that computer classes using Novel Engineering had a positive effect on learning flow and creative problem-solving ability.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.369-377
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• 2021

Augmented reality smart-glass enables healthcare providers to use patient and their care related information without interference of workflow. In addition, augmented reality smart-glass simulation had advantages in improving competency via remote collaboration and real-time information sharing. This study investigated perception and attitude regarding augmented reality smart glass based healthcare simulation on three different groups of healthcare major students, computer major students, developers and faculties. Using convenience sampling method, data were obtained from 95 participants and statistical analysis were performed using SPSS 25.0. Developer and faculty group showed the highest scores, followed by healthcare major students. There was the high expectation on augmented reality smart-glass for skill acquisition and the high performance and big screen were essential features of device. The findings of this study revealed that differences between healthcare and computer major students exist and strategies to reduce those gaps are required to adopt augmented reality smart glass in healthcare settings.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.19-26
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• 2021

Cultural assets excavated in historical areas have their own characteristics based on the background of the times, and it can be seen that their patterns and characteristics change little by little according to the history and the flow of the spreading area. Cultural properties excavated in some areas represent the culture of the time and some maintain their intact appearance, but most of them are damaged/lost or divided into parts, and many experts are mobilized to research the composition and repair the damaged parts. The purpose of this research is to learn patterns and characteristics of the past through artificial intelligence neural networks for such restoration research, and to restore the lost parts of the excavated cultural assets based on Generative Adversarial Network(GAN)[1]. The research is a process in which the rest of the damaged/lost parts are restored based on some of the cultural assets excavated based on the GAN. To recover some parts of dammed of cultural asset, through training with the 2D image of a complete cultural asset. This research is focused on how much recovered not only damaged parts but also reproduce colors and materials. Finally, through adopted this trained neural network to real damaged cultural, confirmed area of recovered area and limitation.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.405-413
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• 2021

In this study, based on the theory of use and satisfaction, the motives of consumers who use Instagram accounts for luxury fashion brands were classified and the effect of the flow state experienced according to the motives on the purchase intention and the enjoyment was empirically studied. As a result of the analysis, the conclusions obtained through this study can be summarized as follows. First, it was found that the motive for using Instagram, a luxury fashion brand, had a significant positive effect on flow, followed by social interaction motive, informational motive, and enjoyment motive. Among the motivations for use, it was confirmed that the motivation for social interaction had the greatest influence on flow. Second, when using the luxury fashion brand Instagram account, the state of consumer flow induces positive emotions such as pleasure, which has a positive effect on purchase intention. Third, age, which is a characteristic of luxury fashion brand Instagram account users, had a significant positive moderating effect on the relationship between informational motives and flow, and had a significant negative effect with playful motives. Through the results of this study, we contributed to the flow of previous related studies by empirically showing the dynamics of consumer psychology related to the use of luxury Instagram accounts.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.297-304
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• 2021

Commercial obfuscation tools (protectors) aim to create difficulties in analyzing the operation process of software by applying obfuscation techniques and Anti-reversing techniques that delay and interrupt the analysis of programs in software reverse engineering process. In particular, in case of virtualization detection and anti-debugging functions, the analysis tool exits the normal execution flow and terminates the program. In this paper, we analyze Anti-reversing techniques of executables with Debugger Detection and Viralization Tools Detection options through VMProtect 3.5.0, one of the commercial obfuscation tools (protector), and address bypass methods using Pin. In addition, we predicted the location of the applied obfuscation technique by finding out a specific program termination routine through API analysis since there is a problem that the program is terminated by the Anti-VM technology and the Anti-DBI technology and drew up the algorithm flowchart for bypassing the Anti-reversing techniques. Considering compatibility problems and changes in techniques from differences in versions of the software used in experiment, it was confirmed that the bypass was successful by writing the pin automation bypass code in the latest version of the software (VMProtect, Windows, Pin) and conducting the experiment. By improving the proposed analysis method, it is possible to analyze the Anti-reversing method of the obfuscation tool for which the method is not presented so far and find a bypass method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.405-412
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• 2022

This paper studies the method of estimating power loss and classifying the factors for improving the power generation efficiency through O&M. It is installed under various climatic conditions worldwide, operational and maintenance technologies suitable for the characteristics of the installation location are required. Existing studies related to solar power generation efficiency have been actively quantifying the impact on short-term losses by environmental factors such as high temperature, dust accumulation, precipitation, humidity, and wind speed, but analysis of the overall impact from a long-term operation perspective is limited. In this study, the potential for efficiency improvement was analyzed by re-establishing a loss classification system according to the power flow of solar power to derive a comprehensive efficiency model for long-term operation and estimating power loss through a case study for each region where climate conditions are classified. As a result of the analysis, the average annual potential for improving soiling loss was 26.9%, Death Valley 7.2%, and Seoul 3.8%. Aging losses was 6.6% in the 20th year as a cumulative. The average annual potential due to temperature loss was 2.9 % for Doha, 1.9% for Death Valley, and 0.2% for Seoul.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.177-190
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• 2022

In this study, using data from the second wave of Panel Survey of Employment for The Disabled[PESD] (2016) to the sixth year (2021), the longitudinal changes between disability acceptance and job satisfaction of adult wage workers aged 20 or older with disabilities and the correlation An autoregressive cross-lagged model was applied to analyze the causal relationship. As a result of the analysis, first, the disability acceptance and job satisfaction at the previous time point of the disabled workers were stably significant to the disability acceptance(𝛽=.556~.610) and job satisfaction(𝛽=.554~.585) at the later time point. Second, disability acceptance at the previous point in time for wage workers with disabilities crosses the job satisfaction at the later time point(𝛽=.077~.090), and the job satisfaction at the previous point crosses the disability acceptance at the later time point(𝛽=.087~.092). Third, as a result of model fit analysis according to the gender group of wage workers with disabilities, the difference in the autoregressive effect between disability acceptance and job satisfaction and the cross-lagged effect between disability acceptance and job satisfaction according to the passage of time was not significant.