• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.2
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• pp.17-28
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• 2024

In order to achieve the management goals (30by30) mandated by the Kunming-Montreal Global Biodiversity Framework, this study established the site-level assessment criteria for OECMs, tailored to domestic circumstances using the Delphi analysis. Subsequently, a site-level assessment was conducted on the National Gariwangsan Natural Recreation Forest. As a result of the study, the initial step involved presenting criteria for the site-level assessment of OECMs, with 'consent for the assessment and recognition of OECM by competent and management authority' proposed as a prerequisite. Subsequently, seven evaluation criteria were established, including 'other than a legally protected area', 'spatially separated area with defined boundaries', 'effective in-situ conservation of biodiversity', 'sustainable management based on the competent and management authority', 'long-term sustainability of conservation outcomes', and 'provision of ecosystem services'. The results of applying site-level assessment criteria to the National Gariwangsan Natural Recreation Forest indicate that six criteria were met, while one criterion (sustainable management based on the competent and management authority) requires further improvement. Specifically, the key competent and management authorities for the National Gariwangsan Natural Recreation Forest are the Korea Forest Service and the National Natural Recreation Forest Management Office, with competent and management organizations established. However, the management focus is primarily on providing forest recreation services centered on users and facilities, making it difficult to confirm the long-term biodiversity conservation plans and implementation by the competent and management authorities. Therefore, it is deemed necessary to improve the long-term biodiversity conservation plans and implementation for the recognition of the National Gariwangsan Natural Recreation Forest as an OECM.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.157-172
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• 2024

Autonomous driving technology, when commercialized, has the potential to improve the safety, mobility, and environmental performance of transportation networks. However, safe autonomous driving may be hindered by poor sensor performance and limitations in long-distance detection. Therefore, cooperative autonomous driving that can supplement information collected from surrounding vehicles and infrastructure is essential. In addition, since HDVs, AVs, and CAVs have different ranges of perceivable information and different response protocols, countermeasures are needed for mixed traffic that occur during the transition period of autonomous driving technology. There is a lack of research on traffic flow optimization that considers the penetration rate of autonomous vehicles and the different characteristics of each road segment. The objective of this study is to develop weights based on safety, operational, and environmental factors for each infrastructure control use case and autonomous vehicle MPR. To develop an integrated evaluation index, infra-guidance AHP and hybrid AHP weights were combined. Based on the results of this study, it can be used to give right of way to each vehicle to optimize mixed traffic.

• Economic and Environmental Geology
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• v.57 no.2
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• pp.271-280
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• 2024

This study examines the European Union (EU)'s policies on managing nitrate contamination in groundwater and provides implications for the future groundwater management in South Korea. Initiated by the 1991 Nitrate Directive, the EU has pursued a multifaceted approach to reduce agricultural nitrate pollution through sustainable ('good') farming practices, regular nitrate level monitoring, and designating Nitrate Vulnerable Zones. Further policy integrations, like the Water Framework Directive and Groundwater Directive, have established comprehensive protection strategies, including the use of pollutant threshold values. Recently, the 2019 Green Deal escalated efforts against nitrates, aligning with broader environmental and climate objectives. This review aims to explore these developments, highlighting key mitigation strategies against nitrate pollution, and providing valuable insights for the future sustainable groundwater nitrate management in South Korea, emphasizing the importance of preventive measures and collaborative efforts to restore and improve groundwater quality.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.157-165
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• 2024

The evolution of the Distributed Denial of Service Attack(DDoS Attack) method has increased the difficulty in the detection process. One of the solutions to overcome the problems caused by the limitations of the existing victim-side detection method was the source-side detection technique. However, there was a problem of performance degradation due to network traffic irregularities. In order to solve this problem, research has been conducted to detect attacks using a collaborative network between several nodes based on artificial intelligence. Existing methods have shown limitations, especially in nonlinear traffic environments with high Burstness and jitter. To overcome this problem, this paper presents a collaborative source-side DDoS attack detection technique introduced with an attention mechanism. The proposed method aggregates detection results from multiple sources and assigns weights to each region, and through this, it is possible to effectively detect overall attacks and attacks in specific few areas. In particular, it shows a high detection rate with a low false positive of about 6% and a high detection rate of up to 4.3% in a nonlinear traffic dataset, and it can also confirm improvement in attack detection problems in a small number of regions compared to methods that showed limitations in the existing nonlinear traffic environment.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.37-44
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• 2024

Even though MR can reveal excellent soft-tissue contrast and functional information, CT is also required for electron density information for accurate dose calculation in Radiotherapy. For the fusion of MRI and CT images in RT treatment planning workflow, patients are normally scanned on both MRI and CT imaging modalities. Recently deep-learning-based generations of CT images from MR images became possible owing to machine learning technology. This eliminated CT scanning work. This study implemented a CycleGan deep-learning-based CT image generation from MR images. Three CT generators whose learning is based on T1- , T2- , or T1-&T2-weighted MR images were created, respectively. We found that the T1-weighted MR image-based generator can generate better than other CT generators when T1-weighted MR images are input. In contrast, a T2-weighted MR image-based generator can generate better than other CT generators do when T2-weighted MR images are input. The results say that the CT generator from MR images is just outside the practical clinics and the specific weight MR image-based machine-learning generator can generate better CT images than other sequence MR image-based generators do.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.123-136
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• 2024

The U.S. Department of Defense, leading global cybersecurity policies, has two main cybersecurity frameworks: the Cybersecurity Maturity Model Certification (CMMC) for external defense industry certification, and the Risk Management Framework (RMF) for internal organizational security assessments. For Republic of Korea military, starting from 2026, the Korean version of RMF (K-RMF) will be fully implemented. Domestic defense industry companies participating in projects commissioned by the U.S. Department of Defense must obtain CMMC certification by October 2025. In this paper, a new standard compliance meta-model (R3C) development methodology that can simultaneously support CMMC and RMF security audit readiness tasks is introduced, along with the implementation results of a compliance solution based on the R3C meta-model. This research is based on practical experience with the U.S. Department of Defense's cybersecurity regulations gained during the joint project by the South Korean and U.S. defense ministries' joint chiefs of staff since 2022. The developed compliance solution functions are being utilized in joint South Korean-U.S. military exercises. The compliance solution developed through this research is expected to be available for sale in the private sector and is anticipated to be highly valuable for domestic defense industry companies that need immediate CMMC certification.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.29-37
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• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.116-138
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• 2024

This study presents a new tool and protocol to assess the impact of 'Marine Invasive and Harmful Species' (MIHS) on marine environments and socio-economic aspects. It addresses shortcomings in the Marine Ecosystems Conservation and Management Act in South Korea by proposing an impact assessment framework divided into marine environmental and socio-economic groups. Six distinct evaluation categories are included in each group, and a semi-quantitative five-step scale is utilized to provide a flexible approach, addressing a variety of issues from ecological disturbances to effects on health and property. The assessment tool is applied through a systematic five-stage process based on the Delphi method. This approach posters collaboration among a diverse sets of experts and stakeholders, enabling a comprehensive evaluation that incorporates various perspectives. The study also examines strategies to effectively manage uncertainties and improve the consistency of the outcomes. The application of this assessment protocol is expected to be crucial in quantifying the ecological damage caused by MIHS and in identifying management and prevention priorities. The ultimate aim of this evaluation process is to aid decision-makers in developing strategies to preserve the marine ecosystem and mitigate socio-economic impacts.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.41-63
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• 2024

This study investigated research trends in digital curation indexed in a prominent domestic academic information database. A systematic literature review was conducted on 39 academic papers published from 2009 to 2023. The review examined indexing status according to publication year, venue, academic discipline, research area distribution, research affiliation and occupation, and research types. In addition, network centrality analysis and cohesive group analysis were performed on 69 author keywords. The findings revealed several key points. First, digital curation research peaked in 2015 and 2016 with 5 publications each year, followed by a slight decrease, and then consistently produced 4 or more publications annually since 2019. Second, among the 39 studies, 25 were conducted in interdisciplinary fields, including library and information science, while 11 were in the humanities, such as miscellaneous humanities. The most prominent research areas were theoretical and infrastructural aspects, information management and services, and institutional domains. Third, digital curation research was predominantly led by university-affiliated professors and researchers, with collaborative research more prevalent than solo research. Lastly, analysis of author keywords revealed that "digital curation," "institution," and "content" were the most influential central keywords within the overall network.