• Economic and Environmental Geology
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• v.56 no.5
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• pp.533-545
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• 2023

In this study, an investigation was conducted on the features, events, and processes (FEP) that could impact the long-term safety of the natural barriers constituting high-level radioactive waste geological repositories. The FEP list was developed utilizing the IFEP list 3.0 provided by the Nuclear Energy Agency (NEA) as foundational data, supplemented by geological investigations and research findings from leading countries in this field. A total of 49 FEPs related to the performance of the natural barrier were identified. For each FEP, detailed definitions, classifications, impacts on long-term safety, significance in domestic conditions, and feasibility of quantification were provided. Moreover, based on the compiled FEP list, three scenarios that could affect the long-term safety of the disposal facility were developed. Geological factors affecting the performance of the natural barrier in each scenario were selected and their relationships were visualized. The constructed FEP list and the visualization of interrelated factors in various scenarios are anticipated to provide essential information for selecting and organizing factors that must be considered in the development of mathematical models for quantitatively evaluating the long-term safety of deep geological repositories. In addition, these findings could be effectively utilized in establishing criteria related to the key performance of natural barriers for the confirmation of repository sites.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.407-418
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• 2023

This study conducted an analysis of terrain change indicators in major development projects in Korea, examining the correlation between terrain change indicators to derive foundational terrain change metrics based on different land use and slope types. The aim is to contribute to sustainable development by enhancing the efficiency of land utilization and landscaping, while minimizing environmental impacts in future development endeavors. Additionally, to apply the research findings in practical contexts, domestic regulations related to terrain were surveyed, and the compatibility and usability between these regulations and research analysis results were discussed. Based on this, the study seeks to explore strategies for more accurate and useful utilization of terrain change indicators in future research. As a result, in the tourism development, terrain changes predominantly occur in the order of flat land, hillly land, and mountain land, with the analysis indicating higher terrain changes in undulating hilly and mountainous lands compared to flat land. Furthermore, in industrial complex development, very steep (20°-30°) and extreme (30°-40°) slopes; in urban development projects, steep slope (15°-20°); in athletic service facility and tourist development, steep (15°-20°) and very steep (20°-30°) exhibit higher average terrain change indicators compared to other slope categories. The findings of our study can contribute to the formulation of strategies aimed at minimizing terrain disturbance in future domestic development projects and serve as foundational data for environmental impact assessments.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1125-1134
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• 2023

Out of the total 17,000 reservoirs in Korea, 13,600 small agricultural reservoirs do not have hydrological measurement facilities, making it difficult to predict water storage volume and appropriate operation. This paper examined univariate and multivariate long short-term memory (LSTM) modeling to predict the storage rate of agricultural reservoirs using remote sensing and artificial intelligence. The univariate LSTM model used only water storage rate as an explanatory variable, and the multivariate LSTM model added n-day accumulative precipitation and date of year (DOY) as explanatory variables. They were trained using eight years data (2013 to 2020) for Idong Reservoir, and the predictions of the daily water storage in 2021 were validated for accuracy assessment. The univariate showed the root-mean square error (RMSE) of 1.04%, 2.52%, and 4.18% for the one, three, and five-day predictions. The multivariate model showed the RMSE 0.98%, 1.95%, and 2.76% for the one, three, and five-day predictions. In addition to the time-series storage rate, DOY and daily and 5-day cumulative precipitation variables were more significant than others for the daily model, which means that the temporal range of the impacts of precipitation on the everyday water storage rate was approximately five days.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1119-1126
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• 2023

This study measured the social value of social economy enterprises in Incheon Metropolitan City using the Social Value Index (SVI) developed by the Korea Social Enterprise Promotion Agency. The results showed that the social value orientation of the business activities of SSEs averaged 9.3 out of 15 points, and their innovation efforts were 8.0 out of 10 points. The average monetary and non-monetary social contribution efforts of SSEs was 5.1 out of 10. When comparing the average sales and social value scores by industry, the manufacturing sector shows that social enterprises have higher average sales and social value orientation of business activities, but lower social return efforts. Social work facility management and business support services have high average sales, but low social value orientation of business activities and efforts to make monetary or non-monetary social contributions. On the other hand, education services; arts, sports, and leisure-related services; and publishing, video, broadcasting, communication, and information services have lower average revenues but higher social value orientation of business activities. These SVI indicators are well utilized by local governments, but not yet by the central government. In the future, governments and public institutions should reflect the differences between sectors when formulating policies for social enterprises.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.

• Smart Media Journal
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• v.13 no.2
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• pp.68-84
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• 2024

This paper describes the development of a system and algorithms for high-quality welfare services by recognizing behavior development indicators (activity, sociability, danger) in children aged 0 to 2 years old using action recognition technology. Action recognition targeted 11 behaviors from lying down in 0-year-olds to jumping in 2-year-olds, using data directly obtained from actual videos provided for research purposes by three nurseries in the Gwangju and Jeonnam regions. A dataset of 1,867 actions from 425 clip videos was built for these 11 behaviors, achieving an average recognition accuracy of 97.4%. Additionally, for real-world application, the Edge Video Analyzer (EVA), a behavior analysis device, was developed and implemented with a region-specific random frame selection-based PoseC3D algorithm, capable of recognizing actions in real-time for up to 30 people in four-channel videos. The developed system was installed in three nurseries, tested by ten childcare teachers over a month, and evaluated through surveys, resulting in a perceived accuracy of 91 points and a service satisfaction score of 94 points.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.3
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• pp.8-17
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• 2023

The current weapon system is operated as a complex weapon system with various standards and protocols applied, so there is a risk of failure in smooth information exchange during combined and joint operations on the battlefield. The interoperability of weapon systems to carry out precise strikes on key targets through rapid situational judgment between weapon systems is a key element in the conduct of war. Since the Korean military went into service, there has been a need to change the configuration and improve performance of a large number of software and hardware, but there is no verification system for the impact on interoperability, and there are no related test tools and facilities. In addition, during combined and joint training, errors frequently occur during use after arbitrarily changing the detailed operation method and software of the weapon/power support system. Therefore, periodic verification of interoperability between weapon systems is necessary. To solve this problem, rather than having people schedule an evaluation period and conduct the evaluation once, AI should continuously evaluate the interoperability between weapons and power support systems 24 hours a day to advance warfighting capabilities. To solve these problems, To this end, preliminary research was conducted to improve defense interoperability capabilities by applying natural language processing techniques (①Word2Vec model, ②FastText model, ③Swivel model) (using published algorithms and source code). Based on the results of this experiment, we would like to present a methodology (automated evaluation of interoperability requirements evaluation / level measurement through natural language processing model) to implement an automated defense interoperability evaluation tool without relying on humans.

• Journal of the Korean GEO-environmental Society
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• v.25 no.9
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• pp.5-14
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• 2024

In Korea, torrential rain frequency and intensity have surged over the past five years (2019-2023), breaking rainfall records. Due to insufficient observation facilities for rainfall and runoff data in small mountainous catchments, preparing for unexpected floods is challenging. This study examines the Bidogyo catchment in Goesan-gun, Chungcheongbuk-do, comparing design flood discharge calculated with optimized parameters versus standard guidelines. Using HEC-HMS and Q-GIS for model construction, five rainfall events were analyzed with data from the National Water Resources Management Information System. The time of concentration (Tc) and storage constant (K) were calculated using the Seokyeongdae formula and model optimization. Results showed that optimized parameters produced higher objective function values for flood events. The design flood discharge varied by -10.7% to 17.3% from the standard guidelines when using optimized parameters. Moreover, optimized parameters yielded flood discharges closer to observed values, highlighting limitations of the Seokyeongdae formula for all catchments. Further research aims to develop suitable parameter estimation methods for small mountainous catchments in Korea.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.484-491
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• 2023

Tomatoes in greenhouse are a widely cultivated horticultural crop worldwide, accounting for high production and production value. When greenhouse ventilation is minimized during low temperature periods, CO₂ enrichment is often used to increase tomato photosynthetic rate and yield. Plant-induced electrical signal (PIES) can be used as a technology to monitor changes in the biological response of crops due to environmental changes by using the principle of measuring the resistance value, or impedance, within the crop. This study was conducted to investigate the relationship between tomato growth data, vital response, and PIES resulting from CO₂ enrichment in greenhouse tomatoes. The growth of tomato treated with CO₂ enrichment in the morning was significantly better in all items except stem diameter compared to the control, and PIES values were also higher. The growth of tomato continuously applied with CO₂ was better in the treatment groups than control, and there was no significant difference in chlorophyll fluorescence and photosynthesis. However, PIES and SPAD values were higher in the CO₂ treatment group than control. CO₂ enrichment have a direct relationship with PIES, growth increased, and transpiration increased due to the increased leaf area, resulting in increased water absorption, which appears to be reflected in PIES, which measures vascular impedance. Through this, this study suggests that PIES can be used to monitor crops due to environmental changes, and that PIES is a useful method for non-destructively and continuously monitoring changes of crops.