• Informatization Policy
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• v.30 no.4
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• pp.40-61
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• 2023

The adoption of smart factories and smart manufacturing as strategies to enhance competitiveness and stimulate growth in the manufacturing sector is vital for a country's future competitiveness and industrial transformation. The government has consistently pursued smart manufacturing innovation policies starting with the Manufacturing Innovation 3.0 strategy in the Ministry of Industry. This study aims to identify policy areas for smart factories and smart manufacturing based on technical standards. Analyzing policy areas at the current stage where the establishment and support of domestic standards aligning with international technical standards are required is crucial. By prioritizing smart manufacturing process areas within the industry, policymakers can make well-informed decisions to advance smart manufacturing without blindly following international standardization in already well-established areas. To achieve this, the study utilizes a hierarchical analysis method including expert interviews and importance-performance analysis for the five major process areas. The findings underscore the importance of proactive participation in standardization for emerging technologies, such as data and security, instead of solely focusing on areas with extensive international standardization. Additionally, policymakers need to consider carbon emissions, energy costs, and global environmental challenges to address international trends in export and digital trade effectively.

• Tunnel and Underground Space
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• v.33 no.5
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• pp.312-338
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• 2023

The underground disposal facility for spent nuclear fuel demands a specialized design, distinct from conventional practices, to ensure long-term thermal, mechanical, and hydraulic integrity, preventing the release of radioactive isotopes from high-temperature spent nuclear fuel. SKB has established design criteria for such facilities and executed practical design implementations for Forsmark. Moreover, in response to subsurface uncertainty, SKB has proposed an empirical approach involving monitoring and adaptive design modifications, alongside stepwise development. SKB has further introduced a unique support system, categorizing ground types and behaviors and aligning them with corresponding support types to confirm safety through comparative analyses against existing systems. POSIVA has pursued a comparable approach, developing a support system for Onkalo while accounting for distinct geological characteristics compared to Forsmark. This demonstrates the potential for domestic implementation of spent nuclear fuel disposal facility designs and the establishment of a support system adapted to national attributes.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• 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.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.57-67
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• 2024

This research investigates training methods for large language models to accurately identify sentiments and comprehend information about increasing and decreasing fluctuations in the financial domain. The main goal is to identify suitable datasets that enable these models to effectively understand expressions related to financial increases and decreases. For this purpose, we selected sentences from Wall Street Journal that included relevant financial terms and sentences generated by GPT-3.5-turbo-1106 for post-training. We assessed the impact of these datasets on language model performance using Financial PhraseBank, a benchmark dataset for financial sentiment analysis. Our findings demonstrate that post-training FinBERT, a model specialized in finance, outperformed the similarly post-trained BERT, a general domain model. Moreover, post-training with actual financial news proved to be more effective than using generated sentences, though in scenarios requiring higher generalization, models trained on generated sentences performed better. This suggests that aligning the model's domain with the domain of the area intended for improvement and choosing the right dataset are crucial for enhancing a language model's understanding and sentiment prediction accuracy. These results offer a methodology for optimizing language model performance in financial sentiment analysis tasks and suggest future research directions for more nuanced language understanding and sentiment analysis in finance. This research provides valuable insights not only for the financial sector but also for language model training across various domains.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.19-24
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• 2024

The future military combat environment is rapidly expanding the role and importance of artificial intelligence (AI) in defense, aligning with the current trends of declining military populations and evolving dynamics. Particularly, in the civilian sector, AI development has surged into new domains based on foundation models, such as OpenAI's Chat-GPT, categorized as Super-Giant AI or Hyperscale AI. The U.S. Department of Defense has organized Task Force Lima under the Chief Digital and AI Office (CDAO) to conduct research on the application of Large Language Models (LLM) and generative AI. Advanced military nations like China and Israel are also actively researching the integration of Super-Giant AI into their military capabilities. Consequently, there is a growing need for research within our military regarding the potential applications and fields of application for Super-Giant AI in weapon systems. In this paper, we compare the characteristics and pros and cons of specialized AI and Super-Giant AI (Foundation Models) and explore new application areas for Super-Giant AI in weapon systems. Anticipating future application areas and potential challenges, this research aims to provide insights into effectively integrating Super-Giant Artificial Intelligence into defense operations. It is expected to contribute to the development of military capabilities, policy formulation, and international security strategies in the era of advanced artificial intelligence.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.326-334
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• 2024

Extra-oral imaging techniques such as Panoramic X-rays (PXs) and Cone Beam Computed Tomography (CBCT) are the most preferred imaging modalities in dental clinics owing to its patient convenience during imaging as well as their ability to visualize entire teeth information. PXs are preferred for routine clinical treatments and CBCTs for complex surgeries and implant treatments. However, PXs are limited by the lack of third dimensional spatial information whereas CBCTs inflict high radiation exposure to patient. When a PX is already available, it is beneficial to reconstruct the 3D oral structure from the PX to avoid further expenses and radiation dose. In this paper, we propose 3DentAI - an U-Net based deep learning framework for 3D reconstruction of oral structure from a PX image. Our framework consists of three module - a reconstruction module based on attention U-Net for estimating depth from a PX image, a realignment module for aligning the predicted flattened volume to the shape of jaw using a predefined focal trough and ray data, and lastly a refinement module based on 3D U-Net for interpolating the missing information to obtain a smooth representation of oral cavity. Synthetic PXs obtained from CBCT by ray tracing and rendering were used to train the networks without the need of paired PX and CBCT datasets. Our method, trained and tested on a diverse datasets of 600 patients, achieved superior performance to GAN-based models even with low computational complexity.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.107-125
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• 2023

The pace of sustainability transition within the maritime industry has been accelerating. This shift primarily necessitates changes in the industry's heavy reliance on fossil fuel-driven ecosystems. Additionally, numerous sustainability laws and regulations, such as the EU's CBAM and IMO's EEXI, have been implemented. This transition is poised to amplify the competitive edge of firms equipped with greater resources, as it introduces substantial operational burdens due to expensive eco-friendly fuel adoption and regulatory compliance. To diverge from the traditional competitive landscape, this paper aims to explore innovative maritime finance models enabling domestic firms to gain competitive advantages on a global scale. Employing analogical reasoning and modeling as a research method, this paper demonstrates that maritime firms can leverage the sustainability transition by aligning sustainable maritime operations with ETS (Emission Trading Schemes). Expanding on this novel approach, the paper delves into potential connections between CCM (Compliance Carbon Market), VCM (Voluntary Carbon Market), and digital asset exchanges. This newly proposed digital/net-zero maritime ecosystem holds the potential to significantly impact the shipping, shipbuilding, and ship finance industries, positioning Busan as a sustainable maritime finance hub. This study holds significance as pioneering research that may stimulate subsequent case-based studies and offer strategic guidance to market participants and policymakers as the maritime industry moves towards a net-zero transition

• Journal of Animal Science and Technology
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• v.49 no.5
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• pp.549-558
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• 2007

C4B and BAT2, assigned to the SLA class III region, were recently reported on relation with human diseases. The primers for RT-PCR and RACE-PCR for CDS analysis of these genes of pig were designed by aligning the CDSs of humans and mice from GenBank. After we amplified and sequenced with these primers and cDNAs, the full-length CDSs of pig were determined. The CDS lengths of C4B and BAT2 were shown as 5226 bp and 6501 bp. In addition, the identities of nucleotide sequences with human and mouse were 76% to 87%, and the identities of amino acids were 72% to 90%. After we carried out the alignment with determined CDSs in this study and pig genomic sequences from GenBank, the primers for cSNP detection in genome were designed in intron regions that flanked one or more exons. Then, we amplified and directly sequenced with genomic DNAs of six pig breeds. Four cSNPs from C4B and three 3 cSNPs from BAT2 were identified. In addition, amino acid substitution occurred in six cSNP positions except for C4248T of C4B. By the Multiplex-ARMS method, we genotyped seven cSNPs with DNA samples used for direct sequencing. We verified that this result was the same as that analyzed using direct sequencing. To demonstrate recrudescence, we performed both direct sequencing and Multiplex-ARMS on two randomly selected DNA samples. The genotype of each sample showed the same result from both methods. Therefore, seven cSNPs were identified from C4B and BAT2 and could be used as the basic data for haplotype analysis of SLA class III region. Moreover, the Multiplex-ARMS method should be powerful for genotyping of genes assigned to the whole SLA region for the xenograft study.

• Journal of Animal Science and Technology
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• v.50 no.6
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• pp.753-762
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• 2008

The primers for RT-PCR and RACE-PCR were designed by aligning the pig genomic sequence and the human complement factor B(CFB) coding sequence(CDS) from the GenBank. Each PCR product was amplified in pig cDNA and sequencing was carried out. The CDS length of pig CFB gene was determined to be 2298 bp. In addition, the pig CDS was more longer than human and mouse orthologs because of insertion and deletion. The identities of porcine nucleotide sequences with those of human and mice were 84% and 80%, and the identities of amino acids were 79% to 77%, respectively. Three complement control protein(CCP) domains, one Von Willebrand factor A(VWFA) domain and a serine protease domain, that are revealed typically in mammals, were found in the pig CFB gene. Based on the CDSs determined, the primers were designed in intron regions for amplification of entire length of exons. In amplification and direct sequencing with genomic DNAs of six pig breeds, three cSNPs(coding single nucleotide polymorphisms) were identified and verified as missense mutations. Using the Multiplex-ARMS method, we genotyped and verified the mutations identified from direct sequencing. To demonstrate recrudescence, we performed both direct sequencing and Multiplex-ARMS with two randomly selected DNA samples. The genotype of each sample exhibited the same results using both methods. Therefore, three cSNPs were identified from pig CFB gene and that can be used for haplotype analysis of the swine leukocyte antigen(SLA) class III region. Moreover, the results indicate that the Multiplex-ARMS method should be powerful for genotyping of genes in the SLA region.