• Smart Structures and Systems
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• 제29권1호
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

• 대한원격탐사학회지
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• 제39권4호
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• International Journal of Quality Innovation
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• 제6권1호
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• pp.43-57
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• 2005

Action learning motivates students to solve open-ended problems by 'developing skills through doing'. This paper reviews the concept of action learning and discusses the adoption of action learning approach to teach operations management at universities. It presents the design and delivery of an action-learning course at City University of Hong Kong. The course incorporates classroom lectures, tutorials and an action-learning workshop. The experience gained proves that action learning facilitates student participation and teamwork and provides a venue of accelerating learning where enables students to handle dynamic problem situations more effectively. The paper concludes that adopting action-learning approach can help lecturers to enhance quality teaching in operations management courses, and provide an alternate means of effective paradigm other than traditional classroom teaching and/or computer-based training at universities.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2014년도 제49차 동계학술대회논문집 22권1호
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• pp.189-191
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• 2014

This paper provides an overview of the E-learning service education on the last decade, In the early 2000's the emphasis of educational technology was on interactive multimedia- stand alone packages on computer hard disks or portable memory, which integrated a range of media forms in the lately. Customers handle finding the best sources of content.The system then uses social signals such as those coming from Facebook, Twitter, LinkedIn, delicious as well as clicks and views. The SNS and network infrastructure is sufficiently mature that the focus should shift to how to use the technology most appropriately to facilitate learning. As we study environmental conditions of the traditional internet and the mobile internet users in some ways. In this paper, analyze the nature of learning, role of educational and suggest alternative policy, innovation of e-learning service and effective e-learning environment in developing technology.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• 대한물리치료과학회지
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• 제30권3호
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• pp.84-90
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• 2023

Background: This study aimed to investigate the effects of flipped learning education on academic motivation and class satisfaction in physical therapy students. Design: Cross-sectional study. Methods: Participants included 72 physical therapy students (experimental group=36, control group=36). In order to compare the effects of flipped learning education, flipped learning and lecture-style learning were provided in a class titled Acticities of Daily Living and Practice. An independent t-test was used to compare academic motivation and class satisfaction between two groups. Results: The flipped learning group showed a significantly higher level of academic motivation and class satisfaction compared to the traditional learning group (p<0.05). Conclusion: These results showed that flipped learning education is an effective learning strategy for improving the academic motivation and class satisfaction of physical therapy students.

• Journal of Ginseng Research
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• 제44권3호
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• pp.442-452
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• 2020

Backgroud: Sleep deprivation (SD) impairs learning and memory by inhibiting hippocampal functioning at molecular and cellular levels. Abnormal autophagy and apoptosis are closely associated with neurodegeneration in the central nervous system. This study is aimed to explore the alleviative effect and the underlying molecular mechanism of stem-leaf saponins of Panax notoginseng (SLSP) on the abnormal neuronal autophagy and apoptosis in hippocampus of mice with impaired learning and memory induced by SD. Methods: Mouse spatial learning and memory were assessed by Morris water maze test. Neuronal morphological changes were observed by Nissl staining. Autophagosome formation was examined by transmission electron microscopy, immunofluorescent staining, acridine orange staining, and transient transfection of the tf-LC3 plasmid. Apoptotic event was analyzed by flow cytometry after PI/annexin V staining. The expression or activation of autophagy and apoptosis-related proteins were detected by Western blotting assay. Results: SLSP was shown to improve the spatial learning and memory of mice after SD for 48 h, accomanied with restrained excessive autophage and apoptosis, whereas enhanced activation of phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin signaling pathway in hippocampal neurons. Meanwhile, it improved the aberrant autophagy and apoptosis induced by rapamycin and re-activated phosphoinositide 3-kinase/Akt/mammalian target of rapamycin signaling transduction in HT-22 cells, a hippocampal neuronal cell line. Conclusion: SLSP could alleviate cognitive impairment induced by SD, which was achieved probably through suppressing the abnormal autophagy and apoptosis of hippocampal neurons. The findings may contribute to the clinical application of SLSP in the prevention or therapy of neurological disorders associated with SD.

• 대한간호학회지
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• 제35권1호
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• pp.65-76
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• 2005

Objective: The aim of this study is to explore the variation in perceptions about problem-based learning(PBL) according to the level of academic achievement and learning attitude in the nursing students of a junior college (3-year program). Method: Students (n=39) learned the respiratory and cardiac system with seven PBL packages and group-based learning for a semester in 2002. Students were asked to write reflective journals that focused on their learning perception after an experience with each learning package. A total of 208 journals were used for analysis. Result: Students positively perceived that PBL making them increase their sense of responsibility for learning and felt satisfaction with the learning process, and had a confidence in the use of clinical nursing interventions. On the other hand, they negatively perceived that PBL was a burden because it took more time than traditional learning tasks, and they experienced an anxiety about regular tests and felt conflicts and diffidences in the learning process. The negative perceptions were expressed more often from students with a low academic achievement and low learning attitude compared to others. Conclusion: Students perceived the PBL as effective in understanding the learning concepts in the clinical practice environment. PBL need to be supplemented by feedback-based lecture and facilitative strategies for academically low-achieved students.Objective: The aim of this study is to explore the variation in perceptions about problem-based learning(PBL) according to the level of academic achievement and learning attitude in the nursing students of a junior college (3-year program). Method: Students (n=39) learned the respiratory and cardiac system with seven PBL packages and group-based learning for a semester in 2002. Students were asked to write reflective journals that focused on their learning perception after an experience with each learning package. A total of 208 journals were used for analysis. Result: Students positively perceived that PBL making them increase their sense of responsibility for learning and felt satisfaction with the learning process, and had a confidence in the use of clinical nursing interventions. On the other hand, they negatively perceived that PBL was a burden because it took more time than traditional learning tasks, and they experienced an anxiety about regular tests and felt conflicts and diffidences in the learning process. The negative perceptions were expressed more often from students with a low academic achievement and low learning attitude compared to others. Conclusion: Students perceived the PBL as effective in understanding the learning concepts in the clinical practice environment. PBL need to be supplemented by feedback-based lecture and facilitative strategies for academically low-achieved students.

• 대한화학회지
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• 제44권6호
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• pp.600-610
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• 2000

본 연구에서는 전통적인 입자모델 교수 전략의 문제점을 분석하여, 잘 변화하지 않는 학습자의 신념체계인 연속적인 물질관을 입자적인 물질관으로 변화시킬 수 있는 새로운 입자모델 교수 전략으로 반성적 사고를 촉진시키는 입자모델 교수 전략을 고안하였다. 고안된 반성적 사고를 촉진시키는 입자모델 교수 전략을 초등학교 예비교사를 대상으로 처치한 후, 반성적 사고를 촉진시키는 입자모델 교수 전략의 효과를 전통적인 입자모델 교수전략과 비교 분석하였다. 전통적인 입자모델 교수전략의 문제점으로는 1) 입자모델을 실제물과 연결시켜 생각하지 못하는 문제점, 2) 입자모델을 학습할 때 무조건 암기하는 학습전략을 사용하는 경향이 높은점, 3) 입자모델을 수없이 학습한 후에도 연속적 물질관을 입자적 물질관으로 변화시키지 못하는 학생의 비율이 매우 놓은점, 4) 물질을 학습할 때 과학적 상황으로 학습하므로 물질에 대한 사고가 과학적 상황에 제한되어 있어 일상적 상황으로 연관시키지 못하는 문제점 등이 있다. 전통적인 과학적 상황의 입자모델 교수전략과 반성적 사고를 촉진시키는 일상적 상황의 입자모델 교수전략의 효과를 비교한 결과 일상적 상황의 입자모델 교수전략이 개념이해측면, 흥미측면, 입자적 물질관 형성정도, 기억측면에서 더 효과적인 것으로 나타났다. 본 연구결과로부터 얻을 수 있는 과학교육에의 중요한 시사점은 일상적 상황의 입자모델 교수전략 방법이 과학을 과학적 상황(실험실 상황)에서만 국한되어 사고하는 것이 아니라, 학교 과학 수업시간에 학습한 과학을 일상 상황과 연관시켜 사고할 수 있도록 해 준다는 것이다.

• 대한수학교육학회지:수학교육학연구
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• 제23권3호
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• pp.373-388
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• 2013

본 논문은 2명의 수학 영재 지도 교사가 갖고 있는 (1)교육 내용으로서의 수학, (2)교육 방법으로서의 수학 교수 학습, 그리고 (3)영재 교육(대상자, 목표/방향, 교사의 역할)에 대한 신념에 따라 수학영재교실에서의 사회 수학적 규범은 어떠한 양태로 나타나는지를 분석하는 것을 목적으로 한다. 이를 위해 충분한 영재지도 경력을 갖고 있으면서 신념의 범주가 다른 두 교사(이하 A교사, B교사라 함)를 선정하여 그들의 수업을 비교 분석하였다. 수학은 '전통', 수학교수는 '혼합', 수학학습은 '전통'적 신념을 가진 A교사는 영재교육에서 수학영재아들을 성취 수준이 높은 자율적 탐구자로 보고 자신은 조력자라고 생각하고 있었다. 수학은 '비전통', 수학교수는 '비전통', 수학학습은 '비전통'적 신념을 가진 B교사는 영재교육에서 수학영재아들을 성취 수준이 높지 않은 자율적 탐구자로 보고 자신은 안내자라고 생각하고 있었다. A교사의 수업에서는 문제 해결의 다양한 규칙과 답을 중요시하며 어려운 문제의 해결을 가치롭게 여기는 사회수학적 규범이 나타났고, B교사의 수업에서는 일반적인 정답보다는 문제 해결의 과정에서 드러나는 수학적 설명과 정당화를 가치롭게 여기는 사회수학적 규범이 나타났다. 그리고 그들의 서로 다른 신념에 따른 수업의 양태와 그 수업에 참여한 학생들의 반응을 통해 수학영재교육에 주는 몇 가지 시사점을 확인할 수 있었다.