• Journal of Intelligence and Information Systems
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• 제29권4호
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

• Journal of Korea Foundry Society
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• 제43권6호
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• pp.302-309
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• 2023

In general, castings often have complex shapes and significant variations in thickness within a single product, making grid generation for simulations challenging. Casting flows involve multiphase flows, requiring the tracking of the boundary between air and molten metal. Additionally, considerable time is spent calculating pressure fields due to density differences in a numerical analysis. For these reasons, the Cartesian grid system has traditionally been used in mold filling simulations. However, orthogonal grids fail to represent shapes accurately, leading to a momentum loss caused by the stair-like grid patterns on curved and sloped surfaces. This can alter the flow of molten metals and result in incorrect casting process designs. To address this issue, simulations in the Cartesian grid system involve creating a large number of grids to represent shapes more accurately. Alternatively, the Cut Cell method can be applied to address the problems arising from the Cartesian grid system. In this study, analysis results based on the number of grid in the Cartesian grid system for a casting flow analysis were compared with results obtained using the Cut Cell method. Casting flow simulations of actual products during various casting processes were also conducted, and these results were analyzed with and without applying the Cut Cell method.

• Journal of Korean Tunnelling and Underground Space Association
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• 제25권6호
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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 Plant Biotechnology
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• 제50권
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• pp.56-62
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• 2023

Transgenic lilies have been obtained using Agrobacterium tumefaciens (AGL1) with the plant scale explants, followed by DL-phosphinothricin (PPT) selection. In this study, scales of lily plants cv. "red flame" were transformed with the pCAMBIA3301 vector containing the gus gene as a reporter and the blpR gene as a selectable marker, as well as a gene of interest showing herbicide tolerance, both driven by the CaMV 35S promoter. Using a 20-minute infection time and a 5-day cultivation period, factors that optimized and demonstrated a high transformation efficiency were achieved. With these conditions, approximately 22-27% efficiency was observed for Agrobacterium-mediated transformation in lilies. After transformation with Agrobacterium, scales of lilies were transferred to MS medium without selective agents for 2 weeks. They were then placed on selection MS medium containing 5 mg/L PPT for a month of further selection and then cultured for another 4-8 weeks with a 4-week subculture regime on the same selection medium. PPT-resistant scales with shoots were successfully rooted and regenerated into plantlets after transferring into hormone-free MS medium. Also, most survived putatively transformed plantlets indicated the presence of the blpR gene by PCR analysis and showed a blue color indicating expression of the gus gene. In conclusion, when 100 scales of lily cv. "red flame" are transformed with Agrobacterium, approximately 22-27 transgenic plantlets can be produced following an optimized protocol. Therefore, this protocol can contribute to the lily breeding program in the future.

• Journal of the Korean Recycled Construction Resources Institute
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• 제12권1호
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• pp.63-71
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• 2024

Through the development of construction technology, various construction projects such as redevelopment projects, undergrounding of roads, expansion of subways, and metro railways are being carried out. However, this has led to an increase in the number of construction projects in existing urban centers and neighborhoods, resulting in an increase in the number of damages and disputes between neighboring buildings and residents, as well as an increase in safety accidents due to the aging of existing buildings. In this study, digital data was applied to a graphics program to objectify the progress of cracks by comparing the creation of cracks and the increase in length and width through photographic images and presenting the degree of cracks numerically. Through the application of the program, the error caused by the subjective judgment of crack change, which was mentioned as a shortcoming of the existing field survey, was solved. It is expected that the program can be used universally in the building diagnosis process by improving its reliability if supplemented and improved in the process of use. As a follow-up study, it is necessary to apply the extraction algorithm of the digital graphic data program to calculate the length and width of the crack by itself without human intervention in the preprocessing work and to check the overall change of the building.

• Journal of the Korea Institute of Building Construction
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• 제24권1호
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• pp.157-168
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• 2024

The frequent occurrence of structural failures at building construction sites in Korea has underscored the critical role of rigorous oversight in the inspection and management of construction projects. As mandated by prevailing regulations and standards, onsite supervision by designated supervisors encompasses thorough documentation of construction quality, material standards, and the history of any reconstructions, among other factors. These reports, predominantly consisting of unstructured data, constitute approximately 80% of the data amassed at construction sites and serve as a comprehensive repository of quality-related information. This research introduces the SL-QPA model, which employs text mining techniques to preprocess supervision reports and establish a sentiment dictionary, thereby enabling the quantification of quality performance. The study's findings, demonstrating a statistically significant Pearson correlation between the quality performance scores derived from the SL-QPA model and various legally defined indicators, were substantiated through a one-way analysis of variance of the correlation coefficients. The SL-QPA model, as developed in this study, offers a supplementary approach to evaluating the quality performance of building construction projects. It holds the promise of enhancing quality inspection and management practices by harnessing the wealth of unstructured data generated throughout the lifecycle of construction projects.

• The Mathematical Education
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• 제63권2호
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• pp.165-186
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• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• Journal of the Korean Society of Radiology
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• 제82권1호
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• pp.99-115
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• 2021

Various sequences have been developed for MRI to aid in the radiologic diagnosis. Among the various MR sequences, susceptibility-weighted imaging (SWI) is a high-spatial-resolution, three-dimensional gradient-echo MR sequence, which is very sensitive in detecting deoxyhemoglobin, ferritin, hemosiderin, and bone minerals through local magnetic field distortion. In this regard, SWI has been used for the diagnosis and treatment of various neurologic disorders, and the improved image quality has enabled to acquire more useful information for radiologists. Here, we explain the principle of various signals on SWI arising in neurological disorders and provide a retrospective review of many cases of clinically or pathologically proven disease or components with distinctive imaging features of various neurological diseases. Additionally, we outline a short and condensed overview of principles of SWI in relation to neurological disorders and describe various cases with characteristic imaging features on SWI. There are many different types diseases involving the brain parenchyma, and they have distinct SWI features. SWI is an effective imaging tool that provides complementary information for the diagnosis of various diseases.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제23권2호
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• pp.119-131
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• 2024

Understanding accurate traffic performance is crucial for ensuring efficient highway operation and providing a sustainable mobility environment. On the other hand, an immediate and precise estimation of highway traffic performance faces challenges because of infrastructure and technological constraints, data processing complexities, and limitations in using integrated big data. This paper introduces a framework for estimating traffic performance by analyzing real-time data sourced from toll collection systems and dedicated short-range communications used on highways. In particular, this study addresses the data errors arising from segmented information in data, influencing the individual travel trajectories of vehicles and establishing a more reliable Origin-Destination (OD) framework. The study revealed the necessity of trip linkage for accurate estimations when consecutive segments of individual vehicle travel within the OD occur within a 20-minute window. By linking these trip ODs, the daily average highway traffic performance for South Korea was estimated to be248,624 thousand vehicle kilometers per day. This value shows an increase of approximately 458 thousand vehicle kilometers per day compared to the 248,166 thousand vehicle kilometers per day reported in the highway operations manual. This outcome highlights the potential for supplementing previously omitted traffic performance data through the methodology proposed in this study.

• Journal of The Korean Society of Grassland and Forage Science
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• 제44권1호
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• pp.30-39
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• 2024

The current survey form of grassland has not played its role in managing the grassland effectively because of the ambiguous questionnaire items and the absence of method and time of the investigation. Therefore, this study was conducted to clarify and add the items for effective of grassland management. The survey form of grassland was regulated in Article 16 of the Enforcement Rules of the Grassland Act (Survey on Grasslands Management Status, etc.). The five contents that needed improvement were the grassland owner, the survey timing and method of established grassland, grassland used livestock, grassland usage condition, and grassland grade. In the improved survey form of grassland, the grassland owner was changed to the grassland manager. In addition, 'Survey by each land' was added to the improved survey form in which the managers can survey each land. The dimension of each grassland establishment method was deleted in the grassland establishment time and method. In the livestock using grassland, the number of livestock and the area where livestock are used were added, and the number of other livestock was added, too. The grassland grade was decided as the combined score by three evaluation categories; grassland usage, the ratio of forage production in grassland and kinds of forage. The high, middle, and low grades were over 8, 6~7, and under 5 points in the combined score, respectively. The results show that the changed survey form of grassland can make grassland management more efficient by materializing the subject of grassland management and the survey terminology and methods.