• 품질경영학회지
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• 제50권3호
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• Investigative Magnetic Resonance Imaging
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• 제25권4호
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• pp.293-299
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• 2021

Purpose: To accelerate magnetic resonance fingerprinting (MRF) by developing a flexible deep learning reconstruction method. Materials and Methods: Synthetic data were used to train a deep learning model. The trained model was then applied to MRF for different organs and diseases. Iterative reconstruction was performed outside the deep learning model, allowing a changeable encoding matrix, i.e., with flexibility of choice for image resolution, radiofrequency coil, k-space trajectory, and undersampling mask. In vivo experiments were performed on normal brain and prostate cancer volunteers to demonstrate the model performance and generalizability. Results: In 400-dynamics brain MRF, direct nonuniform Fourier transform caused a slight increase of random fluctuations on the T2 map. These fluctuations were reduced with the proposed method. In prostate MRF, the proposed method suppressed fluctuations on both T1 and T2 maps. Conclusion: The deep learning and iterative MRF reconstruction method described in this study was flexible with different acquisition settings such as radiofrequency coils. It is generalizable for different in vivo applications.

• Archives of Plastic Surgery
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• 제50권2호
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• pp.200-209
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• 2023

Background As the coronavirus disease 2019 virus made its way throughout the world, there was a complete overhaul of our day-to-day personal and professional lives. All aspects of health care were affected including academics. During the pandemic, teaching opportunities for resident training were drastically reduced. Consequently, medical universities in many parts across the globe implemented online learning, in which students are taught remotely and via digital platforms. Given these developments, evaluating the existing mode of teaching via digital platforms as well as incorporation of new models is critical to improve and implement. Methods We reviewed different online learning platforms used to continue regular academic teaching of the plastic surgery residency curriculum. This study compares the four popular Web conferencing platforms used for online learning and evaluated their suitability for providing plastic surgery education. Results In this study with a response rate of 59.9%, we found a 64% agreement rate to online classes being more convenient than normal classroom teaching. Conclusion Zoom was the most user-friendly, with a simple and intuitive interface that was ideal for online instruction. With a better understanding of factors related to online teaching and learning, we will be able to deliver quality education in residency programs in the future.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.476-483
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• 2022

The novel coronavirus pandemic has had a significant impact on society and everyday life. The pandemic imposed a global shutdown leading to many challenges such as the suspension of academic programs at universities. The result of this suspension contributed to the rapid overnight migration of educational activities from traditional face-to-face learning to a virtual environment which until then was unfamiliar to both instructors and students. This study identified the experiences faced by built environment higher education instructors in KwaZulu-Natal, South Africa during this sudden switch to online teaching and learning. This pilot study employed a quantitative research approach to survey instructor experiences on online teaching and learning during a global pandemic. The data was computed and analyzed using IBM Statistical Package for Social Sciences (SPSS) version 27. Descriptive statistics were used to analyze the data collected. The study sample comprised of 20 higher education instructors in the region of the KwaZulu Natal province in South Africa. Findings from the study revealed that instructors faced adaptive challenges with rapidly having to redesign and remodel the mode of academic course delivery and assessments to suit an online platform. Additionally, instructors observed that students faced technological challenges such as connectivity and navigating the online learning management system platforms. The challenges identified by instructors and students can be effectively transformed to opportunities for future learning under the 'new normal'.

• Journal of information and communication convergence engineering
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• 제22권2호
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• pp.165-171
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• 2024

In this study, we present a novel approach for enhancing chest X-ray image classification (normal, Covid-19, edema, mass nodules, and pneumothorax) by combining contrastive learning and machine learning algorithms. A vast amount of unlabeled data was leveraged to learn representations so that data efficiency is improved as a means of addressing the limited availability of labeled data in X-ray images. Our approach involves training classification algorithms using the extracted features from a linear fine-tuned Momentum Contrast (MoCo) model. The MoCo architecture with a Resnet34, Resnet50, or Resnet101 backbone is trained to learn features from unlabeled data. Instead of only fine-tuning the linear classifier layer on the MoCopretrained model, we propose training nonlinear classifiers as substitutes for softmax in deep networks. The empirical results show that while the linear fine-tuned ImageNet-pretrained models achieved the highest accuracy of only 82.9% and the linear fine-tuned MoCo-pretrained models an increased highest accuracy of 84.8%, our proposed method offered a significant improvement and achieved the highest accuracy of 87.9%.

• 한국방사선학회논문지
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• 제14권1호
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• pp.39-44
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• 2020

본 연구에서는 CNN과 빅데이터 기술을 이용한 Deep Learning을 통해 흉부 X-ray 영상 분류 및 정확성 연구에 대하여 알아보고자 한다. 총 5,873장의 흉부 X-ray 영상에서 Normal 1,583장, Pneumonia 4,289장을 사용하였다. 데이터 분류는 train(88.8%), validation(0.2%), test(11%)로 분류하였다. Convolution Layer, Max pooling layer pool size 2×2, Flatten layer, Image Data Generator로 구성하였다. Convolution layer가 3일 때와 4일 때 각각 filter 수, filter size, drop out, epoch, batch size, 손실함수 값을 설정하였다. test 데이터로 Convolution layer가 4일 때, filter 수 64-128-128-128, filter size 3×3, drop out 0.25, epoch 5, batch size 15, 손실함수 RMSprop으로 설정 시 정확도가 94.67%였다. 본 연구를 통해 높은 정확성으로 분류가 가능하였으며, 흉부 X-ray 영상뿐만 아니라 다른 의료영상에서도 많은 도움이 될 것으로 사료된다.

• 대한수학교육학회지:학교수학
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• 제16권3호
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• pp.633-657
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• 2014

본 연구는 상황학습 기반 수업이 초등학교 6학년 학생들의 수학학습에 미치는 영향을 검증하고자 하는데 주요한 목적이 있다. 이를 위해서 본 연구는 실험집단에는 상황학습 기반 수업을 적용하였으며 비교집단에는 교과서 중심의 일반적 수학 수업을 적용하였다. 본 연구는 두 집단의 수학 학업성취도와 수학적 태도에 어떠한 차이가 있는지를 알아보았으며, 또한 수업 상황에 대한 질적 분석을 통해서 수업에서의 교사의 역할과 학생들의 수업 참여 방식을 살펴봄으로써 상황학습 기반 수업의 교육적 효과를 검증하였다. 그 결과, 첫째, 상황학습 기반 수업은 학생들의 수학 학업성취도와 수학적 태도에 긍정적인 영향을 미침을 알 수 있었다. 둘째, 상황학습 기반 수업에서 교사는 학습 촉진자의 역할을 하는 것으로 나타났다. 셋째, 상황학습 기반 수업에서 학생들은 수업 상황 중에 서로에게 도움의 대상, 배움의 대상이 됨으로써 문제해결을 위한 협력적 태도와 적극적인 노력을 보였다.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 정보교육학회논문지
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• 제21권2호
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• pp.231-245
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• 2017

본 연구를 통해 자석 및 자기장과 관련된 웹기반 과학학습 시뮬레이션들의 현황을 살펴보고, 시뮬레이션의 내용과 전략 및 디자인 측면에서 적절성을 평가하였다. 연구를 위해 과학학습 시뮬레이션 평가 기준을 고안하였으며, 초등교사 8명이 참여하여 자석 및 자기장 관련 시뮬레이션 14종을 평가 기준에 맞추어 평가하고 각 시뮬레이션의 특징을 기술하였다. 평가 결과를 바탕으로 시뮬레이션들을 상 그룹과 하 그룹으로 분류하였고, 상 그룹의 시뮬레이션에서 강점과, 하 그룹의 시뮬레이션에서 보완할 점들을 교수학습 내용, 교수학습 전략, 화면구성, 기술의 측면에 따라 분석하고 도출하였다. 연구 결과를 근거로 교수학습에 효과적인 자석 및 자기장 주제의 웹기반 시뮬레이션 개선을 위한 방안을 논의하였다.

• 영재교육연구
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• 제18권2호
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• pp.343-363
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• 2008

본 연구는 과학영재교육원에 선발된 과학영재아동 297명을 대상으로 이들의 창의성과 동기와의 관련성을 분석하였다. 연구 결과 첫째, 본 연구대상 과학영재아동은 창의적 특성과 학습동기가 높고 도전적인 상황에서 회피하기보다 적극적으로 대처하는 경향을 보였지만 외부적 동기인 경쟁동기도 높은 것으로 나타났다. 둘째, 과학영재 여학생의 창의성 점수가 남학생보다 높았으며, 반면 남학생이 여학생보다 경쟁동기가 더 높았다. 세째, 과학영재아동의 창의적 동기는 학습동기와는 정적상관을, 회피동기와는 부적인 상관을, 그리고 경쟁동기와는 상관을 보이지 않았다. 따라서 영재아 지도에 있어서 경쟁이나 비교보다 자발적인 동기와 흥미를 갖는 과제를 학습하도록 하여야 함을 시사한다.