• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.49-62
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• 2024

Reckless development of the underground by rapid urbanization causes inspection delay on replacement of existing structure and installation new facilities. However, frequent accidents occur due to deviation in construction design planned by inaccurate location information of underground structure. Meanwhile, the electrical resistivity survey, knowns as non-destructive method, is based on the difference in the electric potential of electrodes to measure the electrical resistance of ground. This method is significantly advanced with multi-electrode and deep learning for analyzing strata. However, there is no study to quantitatively assess change in electrical resistance according to geometric conditions of structures. This study evaluates changes in electrical resistance through geometric parameters of electrodes and structure. Firstly, electrical resistance numerical module is developed using generalized mesh occurring minimal errors between theoretical and numerical resistance values. Then, changes in resistances are quantitatively compared on geometric parameters including burial depth, diameter of structure, and distance electrode and structure under steady current condition. The results show that higher electrical resistance is measured for shallow depth, larger size, and proximity to the electrode. Additionally, electric potential and current density distributions are analyzed to discuss the measured electrical resistance around the terminal electrode and structure.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.255-283
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• 2024

In this study, as a preliminary research to effectively support data-driven R&D of researchers, we analyzed the academic information and data requirements for researchers to discover new types of academic information and datasets, and to propose directions for academic information services. To achieve the research objectives, we conducted an exploratory case study involving five researchers and administered an online survey among ScienceON users to glean insights into data-driven R&D behaviors and information/data requirements. As a result, researchers relatively referred to academic papers, datasets and software information from academic papers or conference materials. Moreover, the methods and pathways for acquiring data, as well as the types of data, varied across different subject areas. Researchers often faced challenges in data-driven R&D due to difficulties in locating and accessing necessary datasets or software such as learning models. Therefore it has been analyzed that for future support of data-driven R&D, there is a need to systematically construct datasets by subject. Additionally, it is considered necessary to extract and summarize dataset and related software information in conjunction with academic papers.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.112-118
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• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.1-14
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• 2023

This raises the question of how competitive advantage can be created, prompting firms to enhance their capacity for change. In this context, the role of knowledge creation becomes increasingly vital. This research aims to explore the role of intellectual capital and how to improve knowledge cration ability through absorptive capacity framework. It examines the links among knowledge acquisition, learning of new knowledge, knowledge creation, intellectual capital, and competitive advantage, drawing from both internal and external sources. The study focuses on small and medium-sized supplier firms in Korea, with data collected from 15 industries, totaling 106 responses. The research model employs structural equation modeling (SEM) and utilizes AMOS 22 for analysis. As anticipated, all hypotheses were supported. The study provides robust evidence that absorptive capacity is a pivotal factor in cultivating suppliers' competitive advantage. Furthermore, it posits that intellectual capital should be viewed as a criucial component of suppliers' knowledge stock, significantly enhancing the impact of absorptive capacity on their competitive edge. Future studies should aim to validate the research model in different international settings or across multinational corporations to enhance its generalizabulity.

• The Mathematical Education
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• v.63 no.1
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• pp.19-33
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• 2024

This study investigates longitudinal patterns in middle school students' mathematics interest and achievement using panel data from the 4th to 6th year of the Gyeonggi Education Panel Study. Results from the multivariate growth mixture model confirmed the existence of heterogeneous characteristics in the longitudinal trajectory of students' mathematics interest and achievement. Students were classified into four latent classes: a low-level class with weak interest and achievement, a high-level class with strong interest and achievement, a middlelevel-increasing class where interest and achievement rise with grade, and a middle-level-decreasing class where interest and achievement decline with grade. Each class exhibited distinct patterns in the change of interest and achievement. Moreover, an examination of the correlation between intercepts and slopes in the multivariate growth mixture model reveals a positive association between interest and achievement with respect to their initial values and growth rates. We further explore predictive variables influencing latent class assignment. The results indicated that students' educational ambition and time spent on private education positively affect mathematics interest and achievement, and the influence of prior learning varies based on its intensity. The perceived instruction method significantly impacts latent class assignment: teacher-centered instruction increases the likelihood of belonging to higher-level classes, while learner-centered instruction increases the likelihood of belonging to lower-level classes. This study has significant implications as it presents a new method for analyzing the longitudinal patterns of students' characteristics in mathematics education through the application of the multivariate growth mixture model.

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.141-153
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• 2024

The 2022 revised science curriculum aims to develop the ability to solve scientific problems arising in daily life and society based on convergent thinking stimulated through participation in research activities using artificial intelligence (AI). Therefore, we developed a science-AI convergence education program that combines the science curriculum with artificial intelligence and employed it in convergence classes for high school students. The aim of the science-AI convergence class was for students to qualitatively understand the movement of a damped pendulum and build an AI model to predict the position of the pendulum using the block coding platform KNIME. Individual in-depth interviews were conducted to understand and interpret the learners' experiences. Based on Giorgi's phenomenological research methodology, we described the learners' learning processes and changes, challenges and limitations of the class. The students collected data and built the AI model. They expected to be able to predict the surrounding phenomena based on their experimental results and perceived the convergence class positively. On the other hand, they still perceived an with the unfamiliarity of platform, difficulty in understanding the principle of AI, and limitations of the teaching method that they had to follow, as well as limitations of the course content. Based on this, we discussed the strengths and limitations of the science-AI convergence class and made suggestions for science-AI convergence education. This study is expected to provide implications for developing science-AI convergence curricula and implementing them in the field.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.55-58
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• 2024

최근 각 기업의 AI 면접시스템 도입이 증가하고 있으며, AI 면접에 대한 실효성 논란 또한 많은 상황이다. 본 논문에서는 AI 면접 과정에서 지원자를 평가하는 방식을 시각, 음성, 자연어처리 3영역에서 구현함으로써, 면접 지원자를 다방면으로 분석 방법론의 적절성에 대해 평가하고자 한다. 첫째, 시각적 측면에서, 면접 지원자의 감정을 인식하기 위해, 합성곱 신경망(CNN) 기법을 활용해, 지원자 얼굴에서 6가지 감정을 인식했으며, 지원자가 카메라를 응시하고 있는지를 시계열로 도출하였다. 이를 통해 지원자가 면접에 임하는 태도와 특히 얼굴에서 드러나는 감정을 분석하는 데 주력했다. 둘째, 시각적 효과만으로 면접자의 태도를 파악하는 데 한계가 있기 때문에, 지원자 음성을 주파수로 환산해 특성을 추출하고, Bidirectional LSTM을 활용해 훈련해 지원자 음성에 따른 6가지 감정을 추출했다. 셋째, 지원자의 발언 내용과 관련해 맥락적 의미를 파악해 지원자의 상태를 파악하기 위해, 음성을 STT(Speech-to-Text) 기법을 이용하여 텍스트로 변환하고, 사용 단어의 빈도를 분석하여 지원자의 언어 습관을 파악했다. 이와 함께, 지원자의 발언 내용에 대한 감정 분석을 위해 KoBERT 모델을 적용했으며, 지원자의 성격, 태도, 직무에 대한 이해도를 파악하기 위해 객관적인 평가지표를 제작하여 적용했다. 논문의 분석 결과 AI 면접의 다면적 평가시스템의 적절성과 관련해, 시각화 부분에서는 상당 부분 정확도가 객관적으로 입증되었다고 판단된다. 음성에서 감정분석 분야는 면접자가 제한된 시간에 모든 유형의 감정을 드러내지 않고, 또 유사한 톤의 말이 진행되다 보니 특정 감정을 나타내는 주파수가 다소 집중되는 현상이 나타났다. 마지막으로 자연어처리 영역은 면접자의 발언에서 나오는 말투, 특정 단어의 빈도수를 넘어, 전체적인 맥락과 느낌을 이해할 수 있는 자연어처리 분석모델의 필요성이 더욱 커졌음을 판단했다.

• Journal of The Korean Association For Science Education
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• v.27 no.7
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• pp.631-638
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• 2007

This study investigated the influences of a student-centered analogical instruction using physical analogies upon students' conception and application, retention of conception and application, perceptions of science classroom environment, and perceptions of analogical instruction. Six classes of seventh graders (N=208) at a middle school in Seoul were assigned to the control, the teacher-centered analogy (TCA), and the student-centered analogy (SCA) groups. They were taught about 'three states of matter' and 'motion of molecules' for 8 class hours. Analysis of the results revealed that the scores of the SCA group were significantly higher than those of the other groups in the conception test, retention test of conception and application, and perception test of science classroom environment. The scores of the SCA group in the application test were also higher than those of the other groups, but there was a significant difference only between the control and the SCA groups. The TCA group performed significantly better than the control group only in the test of the retention of application. In addition, the students in the SCA group exhibited more positive perceptions of the analogical instruction than those in the TCA group. Educational implications are discussed.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.919-929
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• 2007

Some Korean terminologies related to the structures of vascular tissues in plant morphology, written differently depending upon textbooks and dictionaries, were analysed to propose properly expressed Korean terminologies. A total of 14 university textbooks such as general biology, plant biology, and plant morphology were selected and investigated. The terminologies on the xylem structures, i.e., apotracheal parenchyma, paratracheal parenchyma, tylose, and tangential (longitudinal) section; and on the pit structures i.e., simple pit, bordered pit, aspirated bordered pit, and pit aperture; and on the stelar structures, i.e., haplostele, actinostele, plectostele, and solenostele were examined. The definition and etymology of the terminologies were traced in 4 textbooks of plant anatomy and 2 dictionaries of biology and botany written in English. And then reasonably expressed Korean terminologies, mostly written in Chinese characters, were suggested. The terminologies were compared with those that appeared in the Iwanami dictionary of biology published in Japan. It was expected that the results would contribute to promote mutual understanding between teachers and students in learning plant biology.