• Title/Summary/Keyword: use for learning

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Automatic Parking Enforcement of Electric Kickboards Based on Deep Learning Technique (딥러닝 기반의 전동킥보드 자동 주차 단속)

  • Park, Jisu;So, Sun Sup;Eun, Seongbae
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.326-328
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    • 2021
  • The use of shared electric kickboards that can move quickly within a short distance at a relatively low price is increasing significantly. In this paper, we propose a system for recognizing incorrect parking of an abandoned shared kickboard by applying deep learning-based object recognition technology. In this paper, a model similar to CNN was created separately considering the characteristics of the experimental data, and it was shown that a recognition rate of 60% was obtained through the experiment.

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Evaluation of a Deblur Deep Learning Model for Image Registration Collected from Robots and Drones (로봇 및 드론 센서로 수집한 이미지 정합을 위한 Deblur 딥러닝 모델 평가)

  • Lee, Hye-min;Kwon, Hye-min;Moon, Hansol;Lee, Chang-kyo;Seo, Jeongwook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.153-155
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    • 2022
  • Recently, we are using robots and drones to collect images. However, as the robot or drone is shaken by external influences, pre-processing technology to register images is required. Therefore, in this paper, we use autonomous robots, drones dataset and improve the quality of shaken image data through the Deblur deep learning model. We confirmed through the experimental results that the shaken images were registered and evaluated the model.

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The Effects of Group Composition of Self-Regulation on Project-based Group Performance

  • LEE, Hyeon Woo
    • Educational Technology International
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    • v.11 no.2
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    • pp.105-121
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    • 2010
  • Collaborative learning encourages the use of high-level cognitive strategies, critical thinking, and interpersonal relationships. Despite these advantages, most instructors reveal the difficulties of using project-based collaborative learning; a common problem is the failure of the group to work effectively together. Thus, this study attempted to provide practical advice on group composition with self-regulation. In a college course, 31 groups with 129 students were asked to discuss and prepare the final presentation material and present it together as a collaborative work. All students' self-regulation skills were measured at the beginning of the semester, and the collective self-regulation was computed as an average of the individual scores of each group. The results of regression analysis indicate that the group's collective self-regulation shows a highly significant positive effect on group performance and satisfaction, as self-regulation predicts individual academic performance. The results also show that there is a significant positive relationship between students' self-regulation and participation in group work.

Deep Learning-Based Defect Detection in Cu-Cu Bonding Processes

  • DaBin Na;JiMin Gu;JiMin Park;YunSeok Song;JiHun Moon;Sangyul Ha;SangJeen Hong
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.2
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    • pp.135-142
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    • 2024
  • Cu-Cu bonding, one of the key technologies in advanced packaging, enhances semiconductor chip performance, miniaturization, and energy efficiency by facilitating rapid data transfer and low power consumption. However, the quality of the interface bonding can significantly impact overall bond quality, necessitating strategies to quickly detect and classify in-process defects. This study presents a methodology for detecting defects in wafer junction areas from Scanning Acoustic Microscopy images using a ResNet-50 based deep learning model. Additionally, the use of the defect map is proposed to rapidly inspect and categorize defects occurring during the Cu-Cu bonding process, thereby improving yield and productivity in semiconductor manufacturing.

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Named entity recognition using transfer learning and small human- and meta-pseudo-labeled datasets

  • Kyoungman Bae;Joon-Ho Lim
    • ETRI Journal
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    • v.46 no.1
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    • pp.59-70
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    • 2024
  • We introduce a high-performance named entity recognition (NER) model for written and spoken language. To overcome challenges related to labeled data scarcity and domain shifts, we use transfer learning to leverage our previously developed KorBERT as the base model. We also adopt a meta-pseudo-label method using a teacher/student framework with labeled and unlabeled data. Our model presents two modifications. First, the student model is updated with an average loss from both human- and pseudo-labeled data. Second, the influence of noisy pseudo-labeled data is mitigated by considering feedback scores and updating the teacher model only when below a threshold (0.0005). We achieve the target NER performance in the spoken language domain and improve that in the written language domain by proposing a straightforward rollback method that reverts to the best model based on scarce human-labeled data. Further improvement is achieved by adjusting the label vector weights in the named entity dictionary.

A Study on the Application of Google Classroom for Problem-Based Learning (문제중심학습을 위한 구글크레스룸 활용 방안 연구)

  • Bayarmaa, Natsagdorj;Lee, Keunsoo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.7
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    • pp.81-87
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    • 2018
  • Problem-based learning (PBL) appears to be a superior and effective strategy to train competent and skilled practitioners and to promote long-term retention of knowledge and skills acquired during the learning experience. This study concerns the implementation of PBL in the online environment and face-to-face PBL. An online environment allows participants to communicate with one another, view presentations or videos, interact with other participants, and engage with resources in work groups. Nowadays, education is accessible everywhere with the use of digital devices. Educational institutions subscribe to GSuite for Education, and Google introduced its Google Classroom as an e-learning platform. This study aims to analyze Google Classroom and to design PBL for Mongolian students taking Korean courses. The main objective of this paper is to identify the usability and evaluation of Google Classroom. The result of this study will be a proposed e-learning platform for Dornod University, Mongolia, which is initially needed in the Natural Science and Business Department.

A Study on Data Curation of University Libraries for Improving Teaching and Learning Support (교수학습활동 지원 개선을 위한 대학도서관의 데이터 큐레이션 연구)

  • Lee, Jeong-Mee
    • Journal of the Korean Society for Library and Information Science
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    • v.54 no.1
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    • pp.175-195
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    • 2020
  • The purpose of this study is to propose a data curation service to support teaching and learning activities of university libraries. To this end, the concept of data curation has been summarized, and how to understand information services and teaching-learning information data curation of university libraries. As a traditional extension of library information services, the use of university library data curation services has been proposed. As a result of research, data curation suggests that it is appropriate to understand the data by focusing more on the utilization of data. The university library emphasized that the data curation service is needed through the establishment of an institutional repository of teaching-learning activity materials for the visible effects that contribute to the educational service of the parent university. Finally, the research was completed by presenting five levels of teaching and learning information data curation framework.

Development of Online Machine Learning Model for AHU Supply Air Temperature Prediction using Progressive Sampling and Normalized Mutual Information (점진적 샘플링과 정규 상호정보량을 이용한 온라인 기계학습 공조기 급기온도 예측 모델 개발)

  • Chu, Han-Gyeong;Shin, Han-Sol;Ahn, Ki-Uhn;Ra, Seon-Jung;Park, Cheol Soo
    • Journal of the Architectural Institute of Korea Structure & Construction
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    • v.34 no.6
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    • pp.63-69
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    • 2018
  • The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction ($5.4%{\rightarrow}1.3%$).

Vidyanusa Mathematic Learning Systems Based on Digital Game by Balanced Design Approach

  • Ramdania, Diena Rauda;Prihatmanto, Ary Setijadi;Kim, Myong Hee;Park, Man-Gon
    • Journal of Korea Multimedia Society
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    • v.19 no.3
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    • pp.603-611
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    • 2016
  • Educational games offer an opportunity to engage and inspire students to take an interest in every subject material in school. The "fun" obtain when playing games become a trigger for the use of games in learning. However, there are doubts whether the players actually learn while they are having fun. Vidyanusa is an Online Mathematics Education Game being developed by Crayonpedia Education Ecosystem in Indonesia. The learning goal of Vidyanusa is to engage junior high school students in learning mathematics. In this paper, we design the Vidyanusa game material Functions and Relations by using Balanced Design Approach. This approach has three models in succession; the Content Model outlines the purpose of the game, the Task Model maps out the mission, and the Evidence Model outlines student measurement. This paper will then discusses the quality of games produced in term of Usability factor for effective results and objective. The measurement of the game was carried out based on International Standard ISO/IEC 9126-1 FDIS about Software Quality Product.

Concept Analysis of Organizational Socialization (조직사회화에 대한 개념분석)

  • Kim, Moon-Shil;Choi, Soon-Ook
    • Journal of Korean Academy of Nursing Administration
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    • v.9 no.1
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    • pp.19-30
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    • 2003
  • Purpose : The concept of organizational socialization has been regarded as an abstract concept of organizational learning process and it has been used as strategy for the organizational goal attainment. New graduated nurses' organizational socialization has been a very important process for learning knowledge, skills and behaviors in hospital setting. This study was to analyze and clarify the meaning of the concept of Organizational Socialization. Method : This study use the process of Walker & Avant's concept analysis. Results : The critical attributes of organizational socialization were identified as : 1) Internalization of Value and belief of organization; 2) Learning of organizational rules and modes of living; 3) Improvement of job performance; 4) Maintenance of supportive relationship; 5) Formation of occupational identity. The antecedents of organizational socialization consist of those facts that 1) negative feeling of role; 2) difficulties of interpersonal relationship; 3) unskilled work performance. And the consequences of organizational socialization consist of those facts that 1) organizational commitment; 2) job satisfaction; 3) intention of leave work setting; 4) improvement of job performance; 5) improvement of decision making; 6) maintenance of supportive relationship. Conclusion : Through this concept analysis, the concept of organizational socialization is defined as internalization of value and belief of organization, learning of organizational rules and modes of living, improvement of job performance, maintenance of supportive relationship and formation of occupational identity.

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