• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.499-507
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• 2023

This study conducted an empirical analysis on a land purchase requisition policy in Bukhansan National Park to draw the efficacy, limitations and implications of this policy. A logistic regression analysis was conducted to identify factors that affected the landowners' decision on applying for land purchase requisition using the government's records on acquisition of private lands in the park since 2006 when this policy began to be implemented. Results illustrate that the probability that a landowner applied for purchase requisition increased if the land was classified as forest, if a large proportion of the land was designated as the nature conservation district, if it was located farther from park boundary, and if it had higher appraised value per square meter. These results indicate that as the landowners had less chance to utilize their lands, they more likely apply for purchase requisition. These results also imply that the government can achieve a high conservation performance level if private lands are acquire by the land acquisition requisition policy. The logistic regression model also predict that 401m² of the private lands in Bukhansan National Park will likely be purchase-requested in future. Despites its usefulness in mitigating landowners' complaints in national parks, the land purchase requisition policy has not been widely utilized. Based on these empirical results, this study provides policy implications to facilitate the ulitization of this policy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.173-188
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• 2024

With the advancement of autonomous vehicle (AV) technology, autonomous driving on real roads has become feasible. However, there are challenges in achieving complete autonomy due to perceptual blind areas, which occur when the AV's sensory range or capabilities are limited or impaired by surrounding objects or environmental factors. This study aims to analyze AV accident patterns and safety issues of perceptual blind area that may occur in urban areas, with the goal of developing test scenarios for Level 4+ autonomous driving. It utilized AV accident data from the California Department of Motor Vehicles (DMV) to compare accident patterns and characteristics between AVs and conventional vehicles based on activation status of autonomous mode. It also categorized AV disengagement data to identify types and real-world cases of disengagements caused by perceptual blind areas. The analysis revealed that AVs exhibit different accident types due to their safe driving maneuvers, and three types of perceptual blind area scenarios were identified. The findings of this study serve as crucial foundational data for developing Level 4+ autonomous driving test scenarios, enabling the design of efficient strategies to mitigate perceptual blind areas in various scenarios. This, in turn, is expected to contribute to the effective evaluation and enhancement of AV driving safety on real roads.

• Membrane Journal
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• v.34 no.2
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• pp.154-162
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• 2024

Anion exchange membranes (AEMs) are essential components in water electrolysis systems, serving to physically separate the generated hydrogen and oxygen gases while enabling the selective transport of hydroxide ions between electrodes. Key characteristics sought in AEMs include high ion conductivity and robust chemical and mechanical stability in alkaline. In this study, quaternized Poly(terphenyl piperidinium)/cellulose nanocrystals (qPTP/CNC) mixed matrix membrane was fabricated. The polymer matrix, PTP, was synthesized via super-acid polymerization, known for its excellent ion conductivity and alkaline durability. The qPTP/CNC membrane showed a dense and uniform morphology without significant voids or large aggregates at the polymer-nanoparticle interface. The qPTP/CNC membrane containing 2 wt% CNC demonstrated a high ion exchange capacity of 1.90 mmol/g, coupled with low water uptake (9.09%) and swelling ratio (5.56%). Additionally, the qPTP/CNC membrane showed significantly lower resistance and superior alkaline stability (384 hours at 50℃ in 1 M KOH) compared to the commercial FAA-3-50 membrane. These results highlight the potential of hydrophilic additive CNC in enhancing ion conductivity and alkaline durability of ion exchange membranes.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.137-148
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• 2024

The purpose of this study is to determine how the use of retailtech technology affects consumers' purchase intention. Furthermore, this study aims to investigate the mediating effects of technology usefulness and ease of use on this influence relationship and whether experiential marketing moderates consumers' purchase intention. The survey was conducted from August 1, 2023 to September 30, 2023, and a total of 257 people participated in the study. For statistical analysis, hierarchical regression analysis, three-stage mediation regression analysis, and hierarchical three-stage controlled regression analysis were conducted to test the hypothesis. The results of the study are as follows. First, it was confirmed that big data-AI utilization, mobile-SNS utilization, live commerce utilization, and IoT utilization affect purchase intention in retail technology utilization. Second, technology usefulness has a mediating effect on IoT utilization, mobile-SNS utilization, and big data-AI utilization. Third, perceived ease of use of technology mediated the effects of IoT utilization, mobile-SNS utilization, live-commerce utilization, and big data-AI utilization. Fourth, escapist experience has a moderating effect on mobile SNS utilization and live commerce utilization. Fifth, esthetic experience has a moderating effect on mobile-SNS utilization and big data-AI utilization. Through this study, we hope that the domestic distribution industry will contribute to national competitiveness by securing the competitive advantage of companies by utilizing new technologies in entering the global market.

• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.301-310
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• 2024

The spatial and temporal non-uniform distribution of precipitation makes water management difficult. Due to climate change, nonuniform distribution of precipitation is worsening, and droughts and floods are occurring frequently. Additionally, the intensity of droughts and floods is intensifying, making existing water management systems difficult. From June 2022 to June 2023, most of the water storage rates of major dams in the Yeongsan river and Seomjin river basin were below 30%. In the case of Juam dam, which is the most dependent on water use in the basin, the water storage rate fell to 20.3%, the lowest ever. Pyeongnim dam recorded the lowest water storage rate of 27.3% on May 4, 2023. Due to a lack of precipitation starting in the spring of 2022, Pyeongnim dam was placed at a drought concern level on June 19, 2022, and entered the severe drought level on August 21. Pyeongrim dam and Suyangje(dam) have different operating institutions. Nevertheless, the low water level was not reached at Pyeongnim dam through organic linkage operation in a drought situation. Pyeongnim dam was able to stably supply water to 63,000 people in three counties. In order to maximize the use of limited water resources, we must review ways to move water smoothly between basins and water sources, and prepare for water shortages caused by climate change by establishing a consumer-centered water supply system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.79-88
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• 2024

We used actual field data to analyze from a traffic engineering perspective how AI and edge computing technologies affect the reduction of traffic accidents. By providing object information from 20m behind with AI object recognition, the driver secures a response time of about 3.6 seconds, and with edge technology, information is displayed in 0.5 to 0.8 seconds, giving the driver time to respond to intersection situations. In addition, it was analyzed that stopping before entering the intersection is possible when speed is controlled at 11-12km at the 10m point of the intersection approach and 20km/h at the 20m point. As a result, it was shown that traffic accidents can be reduced when the high object recognition rate of AI technology, provision of real-time information by edge technology, and the appropriate speed management at intersection approaches are executed simultaneously.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.28-34
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• 2024

Since e-learning is conducted based on the learner's autonomy, motivation to continuously participate is crucial for success in e-learning. As the number of adult learners participating in lifelong education increases, it is necessary to study learner participation and the motivating factors. Drawing upon the Expectancy-Value Theory and Self-Regulated Learning Theory, this study analyzed the influence of motivational factors (value, costs, cognitive regulation, and scheduling) on learner participation. An e-learning program was implemented on MoodleCloud, and learners completed a survey before going through the program. Regression analysis was conducted using the survey response data along with the participation score, calculated using the log data. The results of the analysis demonstrated that value and scheduling significantly influenced learner participation, with gender differences found in value. This means that as adult learners perceive higher value in the e-learning program and possess better scheduling skills, they are more likely to participate. These findings can be utilized in developing teaching and learning strategies for both learners and instructors, ultimately helping to prevent dropout in e-learning.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.1-9
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• 2024

Bridges, which are construction structures, have increased from 584 to 38,405 since the 1970s. However, as the number of bridges increases, the number of bridges with a service life of more than 30 years increases to 21,737 (71%) by 2030, resulting in fatal accidents due to basic human resource maintenance of facilities. Accordingly, the importance of bridge safety inspection and maintenance measures is increasing, and the need for decision-making support for supervisors who manage multiple bridges is also required. Currently, the safety inspection and maintenance method of bridges is to write down damage, condition, location, and specifications on the exterior survey map by hand or to record them by taking pictures with a camera. However, errors in notation of damage or defects or mistakes by supervisors are possible, typos, etc. may reduce the reliability of the overall safety inspection and diagnosis. To improve this, this study visualizes damage data recorded in the BIM model in an AR environment and proposes a maintenance plan for bridges with a small number of people through maintenance decision-making support for supervisors.