• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.111-121
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• 2024

Recently, thanks to the development of artificial intelligence technologies such as image recognition, research on unmanned aerial vehicles is being actively conducted. In particular, related research is increasing in the field of military drones, which costs a lot to foster professional pilot personnel, and one of them is the study of an intelligent framework for autonomous mission performance of reconnaissance drones. In this study, we tried to design an intelligent framework for unmanned aerial vehicles using the methodology of designing an intelligent framework for service robots. For the autonomous mission performance of unmanned aerial vehicles, the intelligent framework and unmanned aerial vehicle module must be smoothly linked. However, it was difficult to provide interworking for drones using periodic message protocols with model-based interfaces of intelligent frameworks for existing service robots. First, the message model lacked expressive power for periodic message protocols, followed by the problem that interoperability of asynchronous data exchange methods of periodic message protocols and intelligent frameworks was not provided. To solve this problem, this paper proposes a message model extension method for message periodic description to secure the model's expressive power for the periodic message model, and proposes periodic and asynchronous data exchange methods using the extended model to provide interoperability of different data exchange methods.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.49-62
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• 2023

Due to social and cultural changes and the growth of aging people living as a single because of aging, lonely deaths are steadily increasing, and each local government has begun to define them as a social problem. The legal basis began to be established. In order to explore policy measures to prevent lonely deaths, this study examined cases of lonely death prevention policies using smart digital information technology (AI, IOT), which is being promoted by Daegu Metropolitan City to promote non-face-to-face policies to prevent lonely deaths. Policies related to lonely deaths are divided into two axes: lonely death prevention projects and post-excavation support projects. In order to operate these businesses efficiently, the provision of non-face-to-face services through artificial intelligence and the Internet of Things is recognized as a new service delivery system, so the importance and necessity of non-face-to-face services is increasing. It is time that multifaceted changes and preparations are needed, such as establishing a system to expand the non-face-to-face industry at the national level. In order to respond to another national disaster situation in the future, the non-face-to-face smart care system is being expanded in various welfare policies such as preventing lonely deaths. It will have to be activated.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.33-39
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• 2023

This study is a performance evaluation of a blood sugar monitoring system that combines a PPG sensor, which is an evaluation device for blood glucose monitoring, and a DNN algorithm when monitoring capillary blood glucose. The study is a researcher-led clinical trial conducted on participants from September 2023 to November 2023. PPG-BGMS compared predicted blood sugar levels for evaluation using 1-minute heart rate and heart rate variability information and the DNN prediction algorithm with capillary blood glucose levels measured with a blood glucose meter of the standard personal blood sugar management system. Of the 100 participants, 50 had type 2 diabetes (T2DM), and the average age was 67 years (range, 28 to 89 years). It was found that 100% of the predicted blood sugar level of PPG-BGMS was distributed in the A+B area of the Clarke error grid and Parker(Consensus) error grid. The MARD value of PPG-BGMS predicted blood glucose is 5.3 ± 4.0%. Consequentially, the non-blood-based PPG-BGMS was found to be non-inferior to the instantaneous blood sugar level of the clinical standard blood-based personal blood glucose measurement system.

• Journal of the Korean Society of Radiology
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• v.84 no.5
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• pp.1080-1090
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• 2023

Purpose This study aimed to compare the volume and normative percentiles of brain volumetry in the Korean population using quantitative brain volumetric MRI analysis tools NeuroQuant^Ⓡ (NQ) and DeepBrain^Ⓡ (DB), and to evaluate whether the differences in the normative percentiles of brain volumetry between the two tools is related to cranial shape. Materials and Methods In this retrospective study, we analyzed the brain volume reports obtained from NQ and DB in 163 participants without gross structural brain abnormalities. We measured threedimensional diameters to evaluate the cranial shape on T1-weighted images. Statistical analyses were performed using intra-class correlation coefficients and linear correlations. Results The mean normative percentiles of the thalamus (90.8 vs. 63.3 percentile), putamen (90.0 vs. 60.0 percentile), and parietal lobe (80.1 vs. 74.1 percentile) were larger in the NQ group than in the DB group, whereas that of the occipital lobe (18.4 vs. 68.5 percentile) was smaller in the NQ group than in the DB group. We found a significant correlation between the mean normative percentiles obtained from the NQ and cranial shape: the mean normative percentile of the occipital lobe increased with the anteroposterior diameter and decreased with the craniocaudal diameter. Conclusion The mean normative percentiles obtained from NQ and DB differed significantly for many brain regions, and these differences may be related to cranial shape.

• The Mathematical Education
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• v.63 no.2
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• pp.187-207
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• 2024

In this study, we explored the feasibility of automatically scoring descriptive assessment items using GPT-4 based ChatGPT by comparing and analyzing the scoring results between teachers and GPT-4 based ChatGPT. For this purpose, three descriptive items from the permutation and combination unit for first-year high school students were selected from the KICE (Korea Institute for Curriculum and Evaluation) website. Items 1 and 2 had only one problem-solving strategy, while Item 3 had more than two strategies. Two teachers, each with over eight years of educational experience, graded answers from 204 students and compared these with the results from GPT-4 based ChatGPT. Various techniques such as Few-Shot-CoT, SC, structured, and Iteratively prompts were utilized to construct prompts for scoring, which were then inputted into GPT-4 based ChatGPT for scoring. The scoring results for Items 1 and 2 showed a strong correlation between the teachers' and GPT-4's scoring. For Item 3, which involved multiple problem-solving strategies, the student answers were first classified according to their strategies using prompts inputted into GPT-4 based ChatGPT. Following this classification, scoring prompts tailored to each type were applied and inputted into GPT-4 based ChatGPT for scoring, and these results also showed a strong correlation with the teachers' scoring. Through this, the potential for GPT-4 models utilizing prompt engineering to assist in teachers' scoring was confirmed, and the limitations of this study and directions for future research were presented.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.2
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• pp.147-156
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• 2024

The sustainable management and enhancement of marine resources are becoming increasingly important issues worldwide. This study was conducted in response to these challenges, focusing on the development and performance comparison of fish detection and classification models as part of a deep learning-based technique for assessing the effectiveness of marine resource enhancement projects initiated by the Korea Fisheries Resources Agency. The aim was to select the optimal model by training various sizes of YOLOv8-Seg models on a fish image dataset and comparing each performance metric. The dataset used for model construction consisted of 36,749 images and label files of 12 different species of fish, with data diversity enhanced through the application of augmentation techniques during training. When training and validating five different YOLOv8-Seg models under identical conditions, the medium-sized YOLOv8m-Seg model showed high learning efficiency and excellent detection and classification performance, with the shortest training time of 13 h and 12 min, an of 0.933, and an inference speed of 9.6 ms. Considering the balance between each performance metric, this was deemed the most efficient model for meeting real-time processing requirements. The use of such real-time fish detection and classification models could enable effective surveys of marine resource enhancement projects, suggesting the need for ongoing performance improvements and further research.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.199-226
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• 2024

In response to the era of transformation necessitating the introduction of Artificial Intelligence (AI) and digital technologies, educational innovation is undertaken with the implementation of AI digital textbooks in Mathematics, English, and Information subjects by 2025 in Korea. Within this context, this study analyzed the perceptions and needs of elementary school teachers regarding mathematics AI digital textbook. Based on a survey conducted in November 2023, involving 132 elementary school teachers across the country, the analysis revealed that the majority of elementary school teachers had a low perception of the introduction and need for mathematics AI digital textbooks. However, some recognized the potential for personalized learning and effective teaching support. Furthermore, among the core technologies of the AI digital textbook, teachers highly valued the necessity of learning diagnostics and teacher reconfiguration functions and had the most positive perception of their usefulness in math lessons, while their perception of interactivity was relatively low. These findings suggest the need for changing teachers' perceptions through professional development and information provision to ensure the successful adoption and use of mathematics AI digital textbooks. Specifically, providing concrete and practical ways to use the AI digital textbook, exploring alternatives to digital overload, and continuing development and research on core technologies.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.303-320
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• 2024

This study presents the development and performance evaluation of a custom GPT-based chatbot tailored to provide solutions following Polya's problem-solving stages. A beta version of the chatbot was initially deployed to assess its mathematical capabilities, followed by iterative error identification and correction, leading to the final version. The completed chatbot demonstrated an accuracy rate of approximately 89.0%, correctly solving an average of 57.8 out of 65 image-based problems from a 6th-grade elementary mathematics textbook, reflecting a 4 percentage point improvement over the beta version. For a subset of 50 problems, where images were not critical for problem resolution, the chatbot achieved an accuracy rate of approximately 91.0%, solving an average of 45.5 problems correctly. Predominant errors included problem recognition issues, particularly with complex or poorly recognizable images, along with concept confusion and comprehension errors. The custom chatbot exhibited superior mathematical performance compared to the general-purpose ChatGPT. Additionally, its solution process can be adapted to various grade levels, facilitating personalized student instruction. The ease of chatbot creation and customization underscores its potential for diverse applications in mathematics education, such as individualized teacher support and personalized student guidance.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.85-100
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• 2024

ESG management refers to corporate management that takes into account environmental, social, and governance factors, while digital transformation goes beyond the mere automation or digitization of existing tasks to drive an innovative change in the essence of work and the way value is created. Therefore, digital transformation can help companies achieve ESG goals and implement sustainable business practices, establishing a complementary relationship between digital transformation and ESG management for corporate sustainability and growth. This relationship maximizes the synergy of integrating digital transformation with ESG management, enabling companies to utilize resources efficiently and prevent redundant investments, ultimately enhancing sustainable management performance. In this study, we propose the simultaneous promotion of business process reengineering (BPR), in which both digital transformation and ESG management are integrated. This is because the collection, analysis, and decision-making processes related to various data for promoting ESG management must be organically integrated with digital transformation technologies. Therefore, we analyzed each ESG management objective presented in the K-ESG guidelines and identified the corresponding digital transformation technologies through expert interviews and a review of prior research. The K-ESG guidelines serve as a useful ESG diagnostic system that enables companies to identify improvement tasks and manage performance based on goals through self-assessment of ESG levels. By developing a model based on the K-ESG guidelines for the integrated promotion of digital transformation and ESG management, companies can simultaneously improve ESG performance and drive digital innovation, reducing redundant investments and trial-and-error while utilizing diverse resources efficiently. This study provides practical and academic implications by developing a concrete and actionable new research model for researchers and businesses.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.