• 컴퓨터교육학회논문지
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• 제10권2호
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• pp.65-75
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• 2007

e-러닝 콘텐츠 및 시스템을 효과적으로 개발할 수 있게 하기 위한 기술 표준안으로서 개발 중인 SCORM은 전세계적으로 가장 많은 e-러닝 관련 업체에서 폭 넓게 수용하고 있는 구현 참조 모델이다. 최근들어 게임을 학습에 활용하고자 하는 노력에 대한 관심이 고조되고 있는 상황에서 SCORM을 개발한 ADL에서는 게임 기반 학습에 대한 기초 연구를 수행하고 있는 실정이다. 그러나 ADL은 SCORM 명세에 대한 연구와 게임 기반 학습에 대한 연구를 분리하여 따로 진행하고 있고 대부분의 SCORM 콘텐츠에 대한 연구는 고전적 훈련 및 교육 방법에 대한 웹 기반 온라인화에 초점을 두고 있으므로, 게임 데이터 요소를 적용한 SCORM 콘텐츠를 개발하기 위하여 SCORM 명세의 구체적인 어떠한 부분을 어떻게 활용할 것인가에 대한 연구는 매우 미흡한 실정이다. 본 연구에서는 게임 데이터 요소를 SCORM 데이터 모델에 적용하는 구체적 방안에 대한 연구를 수행하고 이를 바탕으로 SCORM을 게임 기반 학습 콘텐츠 개발에 적용하는 실제 사례를 제시한다.

• Smart Structures and Systems
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• 제22권2호
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• 한국멀티미디어학회논문지
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• 제21권7호
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• pp.779-786
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• 2018

In this paper, we are applied a nerve network to allow for the reflection of data learning methods in their overall forms by using cluster data rather than data learning by the stages and then selected a nerve network model and analyzed its variables through learning by the cluster. The CkLR algorithm was proposed to analyze the reaction variables of clustering outcomes through an approach to the initialization of K-means clustering and build a model to assess the prediction rate of clustering and the accuracy rate of prediction in case of new data inputs. The performance evaluation results show that the accuracy rate of test data by the class was over 92%, which was the mean accuracy rate of the entire test data, thus confirming the advantages of a specialized structure found in the proposed learning nerve network by the class.

• 한국BIM학회 논문집
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• 제10권2호
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• pp.21-28
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• 2020

As the number of aging bridges increases, more studies are being conducted on developing effective and reliable methods for the assessment and maintenance of bridges. With the advancement in new sensing systems and data learning techniques through AI technology, there is growing interests in how to evaluate bridges using these advanced techniques. This paper presents a CNN(Convolution Neural Network) deep learning based technique for evaluating the damage existence and for estimating the damage location in PSC bridges using static strain data. Simulation studies were conducted to investigate the proposed method with error analysis. Damage was simulated as the reduction in the stiffness of a finite element. A data learning model was constructed by applying the CNN technique as a type of deep learning. The damage status and its location were estimated using data set built through simulation. It was assumed that the strain gauges were installed in a regular interval under the PSC bridge girders. In order to increase the accuracy in evaluating damage, the squared error between the intact and measured strains are computed and applied for training the data model. Considering the damage occurring near the supports, the results of error analysis were compared according to whether strain data near the supports were included.

• 소성∙가공
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• 제31권4호
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• 인터넷정보학회논문지
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• 제24권6호
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• pp.1-11
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• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.

• Journal of Korea Artificial Intelligence Association
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• 제2권1호
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• pp.7-14
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• 2024

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

• 컴퓨터교육학회논문지
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• 제6권3호
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• pp.75-85
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• 2003

본 연구는 SCORM 기반 학습 객체의 메타데이타 생성 즉 Asset, SCO, Contents Aggregation과 Contents Package에 대한 메타데이터를 생성하는 시스템(MetaGene)을 개발한다. SCORM 을 지원하는 LMS내 API 어댑터와 인터페이스를 위한 학습 객체 내에 API 활성화 함수를 내장시키고, 데이터 모델을 기반으로 학습 과정을 트래킹 하는 코드도 포함 시킨다. 또한 학습 객체들이 LMS에 전송되게 PIF(Package Interchange File)로 패키징 시킨다. MetaGene에 생성된 학습객체의 메타데이터와 컨텐츠 패키지의 manifest file을 $SCORM^{(TM)}$ Conformance Testsuite을 이용하여 유효성을 검증한다.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• 한국멀티미디어학회논문지
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• 제21권4호
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• pp.464-470
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• 2018

With the introduction of the e-learning since 2000, the place of the education has not been limited to off-line, but the range of it has become broader in online. The e-learning market has evolved steadily over time. With the advent of the term "Edu-tech", which means a combination of education and technology, various IT technologies have incorporated education. Particularly, the Korean education market collects patterns by computerizing the learning history in classes taught according to curriculums. Because of that environment, various personalized learning services have been developed which maximize the effect of the learning. These services have qualitative differences depending on how many data is accumulated and algorithms are developed for the precise analysis. The purpose of this study is to recognize and data-ize OMR marking by the most suitable method to convert analog data into digital data without harming the Korean education system.