• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.450-463
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• 2023

The objective of this study is to derive the core habitat of the Kirengeshoma koreana Nakai utilizing Habitat Suitability Index (HSI) and Maximum Entropy (MaxEnt) models. Expert-based models have been criticized for their subjective criteria, while statistical models face difficulties in on-site validation and integration of expert opinions. To address these limitations, both models were employed, and their outcomes were overlaid to derive the core habitat. Five variables were identified through a comprehensive literature review and spatial analysis based on appearance coordinates. The environmental variables encompass vegetation zone, forest type, crown density, annual precipitation, and effective soil depth. Through surveys involving six experts, importance rankings and SI (Suitability Index) scores were established for each variable, subsequently facilitating the creation of an HSI map. Using the same variables, the MaxEnt model was also executed, resulting in a corresponding map, which was merged to construct the definitive core habitat map. Out of 16 observed locations of K. koreana, 15 were situated within the identified core habitat. Furthermore, an area historically known to host K. koreana but not verified in the present, Mt. Yeongchwi, was found to lack a core habitat. These findings suggest that the developed models exhibit a high degree of accuracy and effectively reflect the current ecological landscape.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.75-83
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• 2024

This study examined the causes and remedies of bid-rigging in the construction industry through a survey of procurement practitioners. The study identified potential problems from the business, construction, and bidding environments, and proposed improvements to the procurement and bidding systems to address these problems. The study found that transparency, fairness, and diversity are important factors in reducing bid-rigging. These factors can be achieved through a variety of measures, such as expanding bidding systems, strengthening fairness standards, and increasing the diversity of participating companies. The study also found that unfair subcontracting regulations are a problem that needs to be addressed. There were differences in the perceptions of the causes of bid-rigging between the general and specialized construction groups. However, there was no difference in the perceptions of improvements to the procurement system between the two groups. This suggests that a consistent solution to bid-rigging can be found. The study's findings are expected to contribute to the resolution and prevention of bid-rigging in the construction industry.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.31-37
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• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

• Journal of Korean Neurosurgical Society
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• v.67 no.2
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• pp.237-248
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• 2024

Objective : Postoperative data on Cushing's disease (CD) are equivocal in the literature. These discrepancies may be attributed to different series with different criteria for remission and variable follow-up durations. Additional data from experienced centers may address these discrepancies. In this study, we present the results obtained from 96 endoscopic transsphenoidal surgeries (ETSSs) for CD conducted in a well-experienced center. Methods : Pre- and postoperative data of 96 ETSS in 87 patients with CD were included. All cases were handled by the same neurosurgical team between 2014 and 2022. We obtained data on remission status 3-6 months postoperatively (medium-term) and during the latest follow-up (long-term). Additionally, magnetic resonance imaging (MRI) and pathology results were obtained for each case. Results : The mean follow-up duration was 39.5±3.2 months. Medium and long-term remission rates were 77% and 82%, respectively. When only first-time operations were considered, the medium- and long-term remission rates were 78% and 82%, respectively. The recurrence rate in this series was 2.5%. Patients who showed remission between 3-6 months had higher long-term remission rates than did those without initial remission. Tumors >2 cm and extended tumor invasion of the cavernous sinus (Knosp 4) were associated with lower postoperative remission rates. Conclusion : Adenoma size and the presence/absence of cavernous sinus invasion on preopera-tive MRI may predict long-term postoperative remission. A tumor size of 2 cm may be a supporting criterion for predicting remission in Knosp 4 tumors. Further studies with larger patient populations are necessary to support this finding.

• Smart Media Journal
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• v.13 no.2
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• pp.52-61
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• 2024

In the shipyard, aerial images are acquired at regular intervals using Unmanned Aerial Vehicles (UAVs) for the management of external storage yards. These images are then investigated by humans to manage the status of the storage yards. This method requires a significant amount of time and manpower especially for large areas. In this paper, we propose an automated management technology based on a semantic segmentation foundation model to address these challenges and accurately assess the status of external storage yards. In addition, as there is insufficient publicly available dataset for external storage yards, we collected a small-scale dataset for external storage yards objects and equipment. Using this dataset, we fine-tune an object detector and extract initial object candidates. They are utilized as prompts for the Segment Anything Model(SAM) to obtain precise semantic segmentation results. Furthermore, to facilitate continuous storage yards dataset collection, we propose a training data generation pipeline using SAM. Our proposed method has achieved 4.00%p higher performance compared to those of previous semantic segmentation methods on average. Specifically, our method has achieved 5.08% higher performance than that of SegFormer.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.50 no.1
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• pp.27-34
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• 2024

Objectives: Surgical intervention for removal of an impacted third molar can lead to significant pain and swelling. Corticosteroids show promise for mitigating postoperative sequelae across various surgical contexts. The use of corticosteroids following minor oral surgery, though controversial, has already been proven effective. However, little research has explored peroral prescription of corticosteroids despite its convenience for outpatients and for non-surgeons like implantologists and periodontists and others who don't have access to needle injections. The aim of this study was to address a void in the literature by comparing the effects of two styles of preoral administration of prednisolone after surgical removal of the mandibular third molar and to determine which style minimizes postoperative sequelae. Materials and Methods: A randomized, split-mouth clinical study was conducted to investigate the efficacy of two different styles of preoral prednisolone in mitigating postoperative sequelae following surgical extraction of impacted mandibular third molars. Fifteen participants were enrolled in the study. Random selection was used to determine the prescription style for the right and left mandibular arch. Group A included those who received a single dose of prednisolone 25 mg, while group B received prednisolone 5 mg postoperatively for a period of three days (5 mg three times/day on the first postoperative day, 5 mg twice/day on the second postoperative day; 5 mg once/day on the third postoperative day). Results: There was a significant difference in the distance between the corner of the mouth and tragus, which decreased with the time interval with respect to group B when compared to group A. Conclusion: The present study showed that a three-day tapered dose of prednisolone postoperatively was more effective in reducing post-extraction sequelae than a single-dose regimen.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.41-49
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• 2024

The advancement of artificial intelligence on a global scale is significantly transforming life. In the field of education, there is a strong emphasis on actively utilizing AI and fostering creatively integrated talents with diverse knowledge. In alignment with this trend, there is a paradigm shift in AI education across primary, middle, high school, as well as university and graduate education. Leading AI schools and specialized high schools are dedicated to enhancing students' AI capabilities, while universities integrate AI into software courses or establish new AI departments to nurture talent. In AI-integrated education graduate programs, national efforts are underway to educate instructors from various disciplines on applying AI technology to the curriculum. In this context, specialized high schools are also restructuring their departments to cultivate technological talent in AI, tailored to students' characteristics and career paths. While the current education focuses primarily on the fundamental concepts and technologies of AI, there is a need to address the aspect of developing practical problem-solving skills. Therefore, this research aims to compare and analyze essential educational courses in AI-leading schools, AI-integrated high schools, AI high schools, university AI departments, and AI-integrated education graduate programs. The goal is to propose the necessary educational courses for AI education in specialized high schools, with the expectation that a more advanced curriculum in AI education can be established in specialized high schools through this effort.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.

• Smart Media Journal
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• v.13 no.5
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• pp.52-61
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• 2024

The onset of the COVID-19 pandemic has accelerated social transformations across various nations. These changes, particularly prominent in the corporate and industrial sectors, have necessitated a shift towards increased remote activities, fundamentally altering societal structures. Within this context, the concept of the Metaverse, a virtual world existing since the early 2000s but previously underrecognized, began to gain widespread recognition. In South Korea, major tech companies such as Naver, Kakao, and Coupang have long normalized remote working, with new employee orientations also taking place on Metaverse platforms. Beyond the IT sector, institutions requiring large gatherings, such as schools, have adopted the Metaverse for hosting major events like welcome ceremonies and informational sessions. This phenomenon suggests that the Metaverse is not merely a transient social trend but is gradually integrating into the daily lives of the general populace, serving as a significant social connector. This study explores the potential of Metaverse-enabled design thinking and methodologies to address the Environmental, Social, and Governance (ESG) challenges faced by Korean society. Specifically, the research focuses on developing solutions for social issues related to pets in Korea.