• Korean Journal of Radiology
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• 제24권11호
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• pp.1061-1080
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• 2023

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

• 한국정보컨버전스학회논문지
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• 제5권2호
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• pp.59-65
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• 2012

국내 전문대학 우수교수학습센터의 홈페이지를 분석하였다. 우수교수학습센터란 한국전문대학교육협의회에서 2010년부터 3년간 선정한 9개의 교수학습센터를 말한다. 대체로 홈페이지의 구성은 센터소개, 교수지원, 학습지원, 이러닝지원, 서비스, 매체지원, 자료실, 커뮤니티 등으로 구성되어있으며, 전문대학 우수교수학습센터 홈페이지의 유사점과 차이점 및 특이점을 홈페이지 메뉴 및 활성화 정도에 따라 확인하고 분석한다. 현재 활성화를 위해 노력하고 있는 전문대학 교수학습센터들의 홈페이지가 가져야 할 기능들과 방향을 제시하고자 한다.

• 한국환경과학회지
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• 제19권8호
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• pp.1057-1066
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• 2010

An ecological learning center is defined as a place which can establish the correct relationship between human and environment. Human can learn ecosystem and importance of environment throughout observation of nature and participation in program of ecological learning center. The most important aspects of ecological learning center planning are to reflect on user's demand and preservation of ecosystem. The prime goals of this study is to analyze user's characteristics in the Young Wheol Mulmurigol Ecological Learning Center. The second goal of this study is to find out the satisfaction model based on user's perceived performance of each program and facility in the center. For this study, questionnaire survey with 204 individuals was completed. The data from the questionnaire were analyzed statistical method by SPSS. There are several significant results from the study as following First, this ecological learning center as a newly operating facility is used not for educational purpose but for resting and relaxation purpose. It is due to that the most of users in this center are package tourists with historic scenes. Second, user's perceived performance evaluated by 23 attributions of programs and facilities, and these attributions could be classified by 5 factors such as environment friendly design, educational function, preservation of environment, provision of various bio-top and provision of resting area. Third, the user satisfaction model indicates that user satisfaction is depended on various factors such as preservation of environment, provision of various bio-top, provision of resting area. Among these factor affecting the satisfaction, provision of various bio-top is the most influence on user satisfaction.

• Journal of Korean Neurosurgical Society
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• 제66권1호
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

• 실천공학교육논문지
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• 제15권1호
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• pp.199-208
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• 2023

기존의 일학습병행 사업 성과 분석은 주로 거시적 차원에서 제도 자체의 효과성을 진단하는 방향으로 전개되어 왔다. 본 연구에서는 일학습병행의 운영 주체 관점에서 공동훈련센터 전담인력의 조직성과에 초점을 둔 진단조사를 시행하고 그 결과를 분석하여 공동훈련센터 및 전담인력을 둘러싸고 산재하는 조직관리상의 다양한 이슈들을 확인하였다. 연구결과 특히 일학습병행 유형과 전담인력 고용형태에 따라 전담인력이 공동훈련센터를 바라보는 인식 및 태도가 차이남을 확인하였다. 일학습병행 유형 중에서는 전반적으로 IPP형 일학습병행에서 전담인력의 조직성과가 낮게 나타난 반면, 산학일체형 도제학교의 경우 상대적으로 높게 나타났다. 고용형태는 특히 프로젝트 계약직에 대한 제도적 개선의 필요성이 도출되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.

• 한국정보시스템학회지:정보시스템연구
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• 제14권2호
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• pp.75-99
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• 2005

Today, many universities are confronted with the changing education paradigm such as e-learning, Distance Education, Virtual University, This IT-based teaming paradigm shift is certainly a new opportunity or a threat to our universities. To overcome this problem the universities should think e-Learning as strategic weapon, such as many firms created competitive weapons from the information systems at the 1980s. So, e-Learning system can be a SIS(Strategic Information System) which supports university's future education strategies. To build a e-Learning system, not only many H/W and S/W resources but also expert personnels are required. An organization such as local university who is week at financial status can't himself plan the system. The Local University Community e-Learning Centers that support the demand of e-learning for their community are recommended. In order to operate these centers efficiently, the strategic roles of the e-Learning center should first be defined. To define the strategic roles, We classified the strategic roles of the e-Learning center into four dimensions, (1) to improve management efficiency, (2) to enhance educational service, (3) to acquire competitive advantages, (4) to build new education infrastructure, and each dimension has 5 or 6 measurement items. As result, to enhance the educational service was considered as the most significant factor among the four dimensions of strategic roles, and the infrastructure building was the next. We also tried to find the difference for each factor by the characteristics of responsor. The data showed that there was litter difference between the groups in evaluating the significance of strategic roles of e-learning centers. Through the strategic roles definition and analysis of expected role ratings, we could have recommended the direction and operation policies of the e-Loaming centers.

• 한국초등과학교육학회지:초등과학교육
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• 제38권1호
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• pp.43-54
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• 2019

In order to organize various places for science experience study, this study gathered and analyzed prior research on science experience study and various science experience perated in school. To that end, a total of 162 relevant prior studies of literature published from 2000 to 2016 were collected and 2,201 cases of science experience study conducted in 2015 were collected and analyzed. The place where the science experiential learning was done is divided into three areas of natural ecology, cultural history, facility experiential learning study, and the characteristics of participating subjects are examined. In terms of the number of articles published in the field of science-related experiential learning areas, 83 ecological experience study sites (51.2%), facilities institution experience study sites 56 (34.6%), and cultural history experience study books 23 (14.2%). Through this study, it was found out that research tendency to analyze science - related attitudes became prominent by setting study subjects using natural objects around and learning to play while playing and playing in nature. There was also an analysis by subjects of participation in science related experience learning centers. Cultural history experiential learning field was significantly lower than previous studies. In the lower grades, nature ecological experience learning was mainly performed. Combining the above findings, it can provide implications for the development of science-related experience activities. First, it is necessary to develop a technology-related experience learning center using local community resources. Second, it is necessary to expand the culture and history experience learning center related to science. Third, we need an education support center to support the expansion and operation of such a technology-related cultural history learning center.

• 전자공학회논문지C
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• 제34C권11호
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• pp.95-103
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• 1997

A robust center estimation tecnique of n-fold engineering parts is presented, which use self-organizing neural networks with generating and merging learning for training neural units. To estimate the center of the n-fold engineering parts using neural networks, the segmented boundaries of the interested part are approximated to strainght lines, and the temporal estimated centers by thecosine theorem which formed between the approximaged straight line and the reference point, , are indexed as (.sigma.-.theta.) parameteric vecstors. Then the entries of parametric vectors are fed into self-organizing nerual network. Finally, the center of the n-fold part is extracted by mean of generating and merging learning of the neurons. To accelerate the learning process, neural network uses an adaptive learning rate function to the merging process and a self-adjusting activation to generating process. Simulation results show that the centers of n-fold engineering parts are effectively estimated by proposed technique, though not knowing the error distribution of estimated centers and having less information of boundaries.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2005년도 춘계학술발표대회
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• pp.1001-1004
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• 2005

인터넷을 통한 정보화의 영향으로 교육 방법에도 큰 변화를 가져왔다. 교수자와 학습자간 오프라인으로 이루어졌던 교육이 온라인상에서 이루어지게 되었고, 양질의 원격 교육을 실천하려는 노력 과정에서 LMS(Learning Management System)는 많은 발전을 하게 되었다[3]. 하지만 잘 개발된 LMS 라 할 지라도 온라인 교육에서는 오프라인 교육과 같이 교수자와 학습자의 직접적인 커뮤니케이션을 통한 상호 의견 수렴이 어렵다[4]. 따라서 본 논문에서는 LMS 기능에 확장성과 이식성을 갖는 설문 시스템을 추가 함으로써 교수자와 학습자간의 원활한 커뮤니케이션을 지원하고자 한다. 또한 강의 점검, 교수전략 수립, 연구, 정책수립 및 사업추진을 위한 각종 조사에 활용하고자 한다. 본 논문의 설문 조사 시스템은 오픈 소스로 전국 대학 및 교육기관을 대상으로 무상 배포 중이며 그 활용을 검증 중이다[5].