• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.109-119
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• 2022

The computational efficiency of reverse time migration (RTM) based on numerical modeling is not secured due to the high-frequency band of several hundred Hz or higher for data acquired through a three-dimensional (3D) ultra-high-resolution (UHR) seismic survey. Therefore, this study develops an RTM program to derive high-quality 3D geological structures using UHR seismic data. In the traditional 3D RTM program, an excitation amplitude technique that stores only the maximum amplitude of the source wavefield and a domain-limiting technique that minimizes the modeling area where the source and receivers are located were used to significantly reduce memory usage and calculation time. The program developed through this study successfully derived a 3D migration image with a horizontal grid size of 1 m for the 3D UHR seismic survey data obtained from the Korea Institute of Geoscience and Mineral Resources in 2019, and geological analysis was conducted.

• The Mathematical Education
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• v.61 no.3
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• pp.477-497
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• 2022

The rapidly changing world calls for reform in mathematics education from lifelong learning perspectives. This study examines adults' perception of mathematics by reflecting on their experiences of mathematics in and out of school in order to understand what the current needs of adults are. With the two questions: "what experiences do participants have during their learning of mathematics in schools?" and "how do they perceive mathematics in their current life?", we analyzed the semi-structured interviews with 10 adults who have different sociocultural backgrounds using narrative inquiry methodology. As a result, participants tended to accept school mathematics as simply a technique for solving computational problems, and when they had not known the usefulness of mathematical knowledge, they experienced frustration with mathematics in the process of learning mathematics. After formal education, participants recognized mathematics as the basic computation skill inherent in everyday life, the furniture of their mind, and the ability to efficiently express, think, and judge various situations and solve problems. Results show that adults internalized school education to clearly understand the role of mathematics in their lives, and they were using mathematics efficiently in their lives. Accordingly, there was a need to see school education and adult education on a continuum, and the need to conceptualize the mathematical abilities required for adults as mathematical literacy.

• Journal of Industrial Convergence
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• v.21 no.2
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• pp.125-131
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• 2023

In this paper, research was conducted to solve the problem of reducing the dimming level and flicker that occurs in the visible light communication system. Visible light communication is a convergence technology that provides both communication and lighting, and must satisfy not only communication performance but also lighting performance. However, since the existing data transmission method transmits without considering the transmission data sequence, it reduces the dimming level and causes a flicker phenomenon. To solve this problem, in this paper, the Dimming Improvement and Flicker Reduction Mapping technique was studied. Existing systems simply transmitted data of '0' and '1', but in this system, original data transmission channels and DIFR (Dimming Improvement and Flicker Reduction) transmission channels are assigned to RGB channels. Original data is allocated to the R channel and original data or inverse original data is allocated to the DIFR-G channel, and the DIFR-B channel maintains the maximum dimming level by transmitting through the logical operation of the R channel and the G channel. At the same time, the flicker phenomenon is prevented by preventing continuous occurrence of 'OFF' patterns. Through this, we proposed an adaptive data allocation algorithm that can faithfully play a role as a light as well as a communication function.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.65-84
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• 2023

In order to establish a balanced development plan at the local government level, it is necessary to understand the current urban spatial structure. In particular, since the central area is a key element of balanced development, it is necessary to accurately identify its location and size. Therefore, the purpose of this study was to identify the central area system for Busan and to derive underprivileged areas that were alienated from the service areas where the functions of the central area could be used. To identify the central area system, four indicators(De facto Population, Land Price, Commercial Buildings, Credit Card Consumption) were used to calculate the central area index, and Getis-Ord Gi* and DBSCAN analysis were performed. Next, the hierarchy of the central areas were classified and the service areas were derived through network analysis by using it. As a result of the analysis, a total of 12 central areas were found in Seomyeon, Jungang, Yeonsan, Jangsan, Haeundae, Deokcheon, Dongnae, Daeyeon, Sasang, Pusan National University, Busan Station, and Sajik. Most of the underprivileged areas affected by the central area appeared in the Eastern area of Busan and the Western area of Busan, and were derived from old industrial areas, residential areas, and some new cities. Based on the results of the study, we can find three meanings. First, we have made a new attempt to apply a machine learning methodology that has not been covered in previous studies. Second, our data show the difference between the actual data and the existing planned central areas. Third, we not only found the location of the central areas, but also identified the underprivileged areas.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.253-262
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• 2023

E-voting is a concept that includes actions such as kiosk voting at a designated place and internet voting at an unspecified place, and has emerged to alleviate the problem of consuming a lot of resources and costs when conducting offline voting. Using E-voting has many advantages over existing voting systems, such as increased efficiency in voting and ballot counting, reduced costs, increased voting rate, and reduced errors. However, centralized E-voting has not received attention in public elections and voting on corporate agendas because the results of voting cannot be trusted due to concerns about data forgery and modulation and hacking by others. In order to solve this problem, recently, by designing an E-voting system using blockchain, research has been actively conducted to supplement concepts lacking in existing E-voting, such as increasing the reliability of voting information and securing transparency. In this paper, we proposed an electronic voting system that introduced hybrid blockchain that uses public and private blockchains in convergence. A hybrid blockchain can solve the problem of slow transaction processing speed, expensive fee by using a private blockchain, and can supplement for the lack of transparency and data integrity of transactions through a public blockchain. In addition, the proposed system is implemented as BaaS to ensure the ease of type conversion and scalability of blockchain and to provide powerful computing power. BaaS is an abbreviation of Blockchain as a Service, which is one of the cloud computing technologies and means a service that provides a blockchain platform ans software through the internet. In this paper, in order to evaluate the feasibility, the proposed system and domestic and foreign electronic voting-related studies are compared and analyzed in terms of blockchain type, anonymity, verification process, smart contract, performance, and scalability.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.512-519
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• 2023

Noise pollution poses significant challenges to human well-being and marine ecosystems. It is primarily caused by the flow around ships and marine installations, emphasizing the need for accurate noise evaluation of flow noise to ensure environmental safety. Existing flow noise analysis methods for underwater environments typically use a hybrid method combining computational fluid dynamics and Ffowcs Williams-Hawkings acoustic analogy. However, this approach has limitations, neglecting near-field effects such as reflection, scattering, and diffraction of sound waves. In this study, an alternative using direct method flow noise analysis via the lattice Boltzmann method (LBM) is incorporated. The LBM provides a more accurate representation of the underwater structural boundaries and acoustic wave effects. Despite challenges in underwater environments due to numerical instabilities, a novel DM-TS LBM collision operator has been developed for stable implementations for hydroacoustic applications. This expands the LBM's applicability to underwater structures. Validation through flow noise analysis in pipe orifice demonstrates the feasibility of near-field analysis, with experimental comparisons confirming the method's reliability in identifying main pressure peaks from flow noise. This supports the viability of near-field flow noise analysis using the LBM.

• Journal of Science Education
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• v.47 no.2
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• pp.139-153
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• 2023

This study aimed to investigate the effect of environmental education programs using action learning on elementary school students' environmental literacy and self-efficacy. To this end, an environmental education program using action learning was applied to 22 sixth graders of 'A' Elementary School in Busan. The results of this study were as follows. First, environmental education programs using action learning were effective in improving the environmental literacy of elementary school students. After implementing the environmental education program using action learning, students' environmental values, cognitive, and procedural ability changed positively. Environmental issues awareness and ecological knowledge improved, and there was a significant increase in environmental behavior and environmental function. Second, environmental education programs using action learning were effective in enhancing the self-efficacy of elementary school students. After implementing environmental education programs using action learning, students' confidence in problem solving and their ability to judge their behaviors improved. Third, elementary school students showed active participation and interest when conducting environmental education programs using action learning. Students have become open to protecting the environment when they find problems, devise solutions, and practice them. After reflection, it was confirmed that students' immersion and satisfaction in class were high as a solution was reached, leading to efforts to sell items they did not use and donate profits to environmental protection organizations.

• Journal of Science Education
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• v.47 no.2
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• pp.211-224
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• 2023

The purpose of this study is to suggest how to investigate the reliability of the assessment, which consists of items generated by automatic item generation using empirical example data. To achieve this, we analyzed the illustrative assessment data by applying the multivariate generalizability theory, which can reflect the design of responding to different items for each student and multiple error sources in the assessment score. The result of the G-study showed that, in most designs, the student effect corresponding to the true score of the classical test theory was relatively large after residual effects. In addition, in the design where the content domain was fixed, the ranking of students did not change depending on the item types or items. Similarly, in the design where the item format was fixed, the difficulty showed little variation depending on the content domains. The result of the D-study indicated that the original assessment data achieved a sufficient level of reliability. It was also found that higher reliability than the original assessment data could be obtained by reducing the number of items in the content domains of operation, geometry, and probability and statistics, or by assigning higher weights to the domains of letters and formulas, and function. The efficient measurement conditions presented in this study are limited to the illustrative assessment data. However, the method applied in this study can be utilized to determine the reliability and to find efficient measurement conditions for the various assessment situations using automatic item generation based on measurement traits.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.17-25
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• 2023

In this paper, we propose a model that can perform human pose estimation through a MobileViT-based model with fewer parameters and faster estimation. The based model demonstrates lightweight performance through a structure that combines features of convolutional neural networks with features of Vision Transformer. Transformer, which is a major mechanism in this study, has become more influential as its based models perform better than convolutional neural network-based models in the field of computer vision. Similarly, in the field of human pose estimation, Vision Transformer-based ViTPose maintains the best performance in all human pose estimation benchmarks such as COCO, OCHuman, and MPII. However, because Vision Transformer has a heavy model structure with a large number of parameters and requires a relatively large amount of computation, it costs users a lot to train the model. Accordingly, the based model overcame the insufficient Inductive Bias calculation problem, which requires a large amount of computation by Vision Transformer, with Local Representation through a convolutional neural network structure. Finally, the proposed model obtained a mean average precision of 0.694 on the MS COCO benchmark with 3.28 GFLOPs and 9.72 million parameters, which are 1/5 and 1/9 the number compared to ViTPose, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.267-280
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• 2023

With development of computing and communications technologies, IoT environments based on high-speed networks have been extending rapidly. Especially, from home to an office or a factory, applications of IoT devices with sensing environment and performing computations are increasing. Unfortunately, IoT devices which have limited hardware resources can be vulnerable to cyber attacks. Hence, there is a concern that an IoT botnet can give rise to information leakage as a national cyber security crisis arising from abuse as a malicious waypoint or propagation through connected networks. In order to response in advance from unknown cyber threats in IoT networks, in this paper, We firstly define four types of We firstly define four types of characteristics by analyzing darknet traffic accessed from an IoT botnet. Using the characteristic, a suspicious IP address is filtered quickly. Secondly, the filtered address is identified by Cyber Threat Intelligence (CTI) or Open Source INTelligence (OSINT) in terms of an unknown suspicious host. The identified IP address is finally fingerprinted to determine whether the IP is a malicious host or not. To verify a validation of the proposed method, we apply to a Darknet on real-world SOC. As a result, about 1,000 hosts who are detected and blocked preemptively by the proposed method are confirmed as real IoT botnets.