• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• Journal of The Korean Association For Science Education
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• v.29 no.8
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• pp.793-811
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• 2009

The purpose of this study is to understand how a teacher's teaching can be changed while he or she teaches the same contents in different classes. The qualitative research method was used in this study. Data were collected from classroom observations, several in-depth interviews, and stimulated-recall interviews after each class. All the data were transcribed and analyzed interpretively, and then, the results of the analysis were checked by each participating teacher. The results are as follows: First, changes appeared in each class in terms of the teaching items, tools, sequence, and time, even though the same teacher taught the same contents. It showed that the teacher's teaching practice changed immediately and intuitively in class. Second, teachers tried to implement "exploratory teaching" or "move-testing teaching" to address the emerging problems during their teaching. They then reflected on and modified their own teaching. This type of change, which happened during the teaching practice, can be an example of "Reflection-in-practice." Thus, the results of this study can provide helpful insights into how teachers might adapt and reflect in their teaching. It suggests that teachers need to recognize their subconscious teaching changes and learn "Reflection-in-practice."

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.77-84
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• 2024

In the fourth industrial-revolution era, the construction industry is transitioning from traditional methods to digital processes. This shift has been challenging owing to the industry's employment of diverse processes and extensive human resources, leading to a gradual adoption of digital technologies through trial and error. One critical area of focus is the safety management at construction sites, which is undergoing significant research and efforts towards digitization and automation. Despite these initiatives, recent statistics indicate a persistent occurrence of accidents and fatalities in construction sites. To address this issue, this study utilizes large-scale language-model artificial intelligence to analyze big data from a construction safety-management information network. The findings are integrated into on-site models, which incorporate real-time updates from detailed design models and are enriched with location information and spatial characteristics, for enhanced safety management. This research aims to develop a big-data-driven safety-management platform to bolster facility and worker safety by digitizing construction-site safety data. This platform can help prevent construction accidents and provide effective education for safety practices.

• 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 IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.41-51
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• 2024

In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.

• Journal of the Korean Society of Earth Science Education
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• v.17 no.1
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• pp.34-48
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• 2024

With the global rise in interest in competency-based education, the Ministry of Education of the Republic of Korea outlined six core competencies in the 2015 revised curriculum, essential for future society's 'creative and convergent talent'. This study introduces an HTE-STEAM constellation education program designed to develop the core competencies outlined in the 2015 revised curriculum and address the limitations of hands-on astronomy education. The program's effectiveness was assessed through a pilot test. The program was implemented at G Library, an out-of-school education site in Cheongju-si, Chungcheongbuk-do, targeting students from 3rd to 6th grade. The study's results include: First, the HTE-STEAM program significantly impacted all aspects of the STEAM attitude test except for 'self-concept', particularly influencing 'science and engineering career choice', 'consideration', and 'communication'. Thus, it has led to positive outcomes in the cultivation of future society's core competencies, including 'creative thinking skills', 'communication skills', and 'community skills'. Secondly, the HTE-STEAM constellation education program, despite covering the challenging concept of spacetime, was deemed easy by many students. Observations of students applying the spatial concepts they learned by using teaching aids suggest that the program was effective in enhancing students' understanding of the spatial structure of the sky and the universe. Additionally, this program aligns with the 2022 curriculum's updated standards for understanding the sky's spatial structure. Consequently, the HTE-STEAM constellation education program positively cultivates future society's core competencies and serves as a valuable complement to night observation practices in schools.

• Atmosphere
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• v.34 no.2
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• pp.203-216
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• 2024

The National Institute of Meteorological Sciences in Korea has developed the Weather Modification Hybrid Rocket (WMHR), an advanced system that offers enhanced stability and cost-effectiveness over conventional solid-fuel rockets. Designed for precise operation, the WMHR enables accurate control over the ejection altitude of pyrotechnics by modulating the quantity of oxidizer, facilitating specific cloud seeding at various atmospheric layers. Furthermore, the rate of descent for pyrotechnic devices can be adjusted by modifying parachute sizes, allowing for controlled dispersion time and concentration of seeding agents. The rocket's configuration also supports adjustments in the pyrotechnic device's capacity, permitting tailored seeding agent deployment. This innovation reflects significant technical progression and collaborations with local manufacturers, in addition to efforts to secure testing sites and address hybrid rocket production challenges. Notable outcomes of this project include the creation of a national framework for weather modification technology utilizing hybrid rockets, enhanced cloud seeding methods, and the potential for broader meteorological application of hybrid rockets beyond precipitation augmentation. An illustrative case study confirmed the WMHR's operational effectiveness, although the impact on cloud seeding was limited by unfavorable weather conditions. This experience has provided valuable insights and affirmed the system's potential for varied uses, such as weather modification and deploying high-altitude meteorological sensors. Nevertheless, the expansion of civilian weather rocket experiments in Korea faces challenges due to inadequate infrastructure and regulatory limitations, underscoring the urgent need for advancements in these areas.