• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.545-551
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• 2008

The MOCT(Ministry of Construction & Transportation) gave a order named as "The guideline for computerization and application of geotechnical investigation result" to an affiliated organization in March 2007. Today, pilot project of construction of geotechnical information database is in process to be stable for its system after applying this guideline, and discipline how to input investigated data for related users. We have developed standard for geotechnical investigation result-format, metadata for distribution of geotechnical information and to coordinate based on world geodetic system. Also, We had a introduce to status with respect to use the input system, collect a statistics of input contents. At a result, improvement items of input system is proposed. It was analyzed that most users put to practical use easily as a result of education for making use of on the spot of the developed GIIS. But There were problems with the GIIS as well as complexity of metadata formation, such as error of moving part of information window, and a part of recognition error of install program in accordance with computer OS circumstances. Particularly, to improve some parts of GIIS is needed, because of use of or KNHC (Korea National Housing Corporation)-specific format and difference of input process followed by MOCT's guideline. In this study, it is planning to make up for occurred problems, and improvements when operating and managing the Geotechnical Information DB center in 2008.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.95-103
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• 2023

This paper proposes an artificial intelligence convergence education program for teaching the main concept and principle of Support Vector Machines(SVM) at elementary schools. The developed program, based on Jeju's natural environment theme, explains the decision boundary and margin of SVM by vertical and parallel from 4th grade mathematics curriculum. As a result of applying the developed program to 3rd and 5th graders, most students intuitively inferred the location of the decision boundary. The overall performance accuracy and rate of reasonable inference of 5th graders were higher. However, in the self-evaluation of understanding, the average value was higher in the 3rd grade, contrary to the actual understanding. This was due to the fact that junior learners had a greater tendency to feel satisfaction and achievement. On the other hand, senior learners presented more meaningful post-class questions based on their motivation for further exploration. We would like to find effective ways for artificial intelligence convergence education for elementary school students.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.529-538
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• 2023

At the present time when the need for universal artificial intelligence education is expanding and job changes are being made, research and discussion on artificial intelligence liberal arts education for non-majors in universities who experience artificial intelligence as part of their job is insufficient. Although artificial intelligence education courses for non-majors are being operated, they are mainly operated as theory-oriented education on the concepts and principles of artificial intelligence. In order to understand the general concept of artificial intelligence for non-majors, it is necessary to proceed with experiential learning in parallel. Therefore, this study designs artificial intelligence experiential education learning contents of difficulty that can reduce the burden of artificial intelligence classes with interest in learning by considering the characteristics of non-majors. After, we will examine the learning effect of experiential education using App Inventor and the Orange artificial intelligence platform. As a result of analysis based on the learning-related data and survey data collected through the creation of AI-related projects by teams, positive changes in the perception of the need for AI education were found, and AI literacy skills improved. It is expected that it will serve as an opportunity for instructors to lay the groundwork for designing a learning model for artificial intelligence experiential education learning.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1371-1388
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• 2023

Floods are becoming more severe and frequent due to global warming-induced climate change. Water disasters are rising in Korea due to severe rainfall and wet seasons. This makes preventive climate change measures and efficient water catastrophe responses crucial, and synthetic aperture radar satellite imagery can help. This research created 1,423 water body learning datasets for individual water body regions along the Han and Nakdong waterways to reflect domestic water body properties discovered by Sentinel-1 satellite radar imagery. We created a document with exact data annotation criteria for many situations. After the dataset was processed, U-Net, a deep learning model, analyzed water body detection results. The results from applying the learned model to water body locations not involved in the learning process were studied to validate soil water body monitoring on a national scale. The analysis showed that the created water body area detected water bodies accurately (F1-Score: 0.987, Intersection over Union [IoU]: 0.955). Other domestic water body regions not used for training and evaluation showed similar accuracy (F1-Score: 0.941, IoU: 0.89). Both outcomes showed that the computer accurately spotted water bodies in most areas, however tiny streams and gloomy areas had problems. This work should improve water resource change and disaster damage surveillance. Future studies will likely include more water body attribute datasets. Such databases could help manage and monitor water bodies nationwide and shed light on misclassified regions.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• Journal of Ginseng Research
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• v.48 no.1
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• pp.77-88
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• 2024

Background: Lung inflammation occurs in many lung diseases, but has limited effective therapeutics. Ginseng and its derivatives have anti-inflammatory effects, but their unstable physicochemical and metabolic properties hinder their application in the treatment. Panaxadiol (PD) is a stable saponin among ginsenosides. Inhalation administration may solve these issues, and the specific mechanism of action needs to be studied. Methods: A mouse model of lung inflammation induced by lipopolysaccharide (LPS), an in vitro macrophage inflammation model, and a coculture model of epithelial cells and macrophages were used to study the effects and mechanisms of inhalation delivery of PD. Pathology and molecular assessments were used to evaluate efficacy. Transcriptome sequencing was used to screen the mechanism and target. Finally, the efficacy and mechanism were verified in a human BALF cell model. Results: Inhaled PD reduced LPS-induced lung inflammation in mice in a dose-dependent manner, including inflammatory cell infiltration, lung tissue pathology, and inflammatory factor expression. Meanwhile, the dose of inhalation was much lower than that of intragastric administration under the same therapeutic effect, which may be related to its higher bioavailability and superior pharmacokinetic parameters. Using transcriptome analysis and verification by a coculture model of macrophage and epithelial cells, we found that PD may act by inhibiting TNFA/TNFAR and IL7/IL7R signaling to reduce macrophage inflammatory factor-induced epithelial apoptosis and promote proliferation. Conclusion: PD inhalation alleviates lung inflammation and pathology by inhibiting TNFA/TNFAR and IL7/IL7R signaling between macrophages and epithelial cells. PD may be a novel drug for the clinical treatment of lung inflammation.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• Journal of the Korean Chemical Society
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• v.68 no.3
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• pp.160-175
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• 2024

The purpose of this study is to examine the effects of a Chemistry I class based on an artificial intelligence (AI) classification model. To achieve this, the research investigated the development and application of a class utilizing an AI classification model in Chemistry I classes conducted at D High School in Gyeongbuk during the first semester of 2023. After selecting the curriculum content and AI tools, and determining the curriculum-AI integration education model as well as AI hardware and software, we developed detailed activities for the program and applied them in actual classes. Following the implementation of the classes, it was confirmed that students' self-efficacy improved in three aspects: chemistry concept formation, AI value perception, and AI-based maker competency. Specifically, the chemistry classes based on text and image classification models had a positive impact on students' self-efficacy for chemistry concept formation, enhanced students' perception of AI value and interest, and contributed to improving students' AI and physical computing abilities. These results demonstrate the positive impact of the Chemistry I class based on an AI classification model on students, providing evidence of its utility in educational settings.

• Journal of The Korean Association For Science Education
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• v.27 no.1
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• pp.59-69
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• 2007

The aim of this study was to investigate teachers' perceptions on MBL (microcomputer-based laboratory) experiment training program for teachers, the expecting effects of MBL experiment and application of MBL experiment after conducting MBL experiment training for science classes in schools. This study showed that most of the teachers who participated in the training program thought that the MBL experiment training program was very useful and instructive. Many teachers considered that MBL experiments using a computer could decrease time spent in the experiment by accurate and fast data collection and analysis. They also thought that the reduced time could be used more effectively in the analysis of experimental data and discussion activities leading to correct concept formation as well as in the development of graphical analysis and science process skills. However, they thought that MBL experiments were ineffective in learning how to operate experiment apparatus. This study also revealed that most teachers intended to apply MBL experiments in real classrooms context right after the training course and they pointed out many obstacles in introducing MBL experiments into their classrooms such as a budget to purchase equipment, poor laboratory conditions, and few MBL experiment training opportunities. In order to apply MBL experiment into the real classrooms, further changes were suggested as follows; development of technologies to reduce unit cost of equipment for MBL experiments, production and supply of many kinds of sensors, development of MBL experiment materials, and expansion of the training program for teachers.