• 반도체디스플레이기술학회지
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• 제18권2호
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• pp.1-5
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• 2019

As the number of IOT devices is growing rapidly, various 'see-thru connection' techniques have been reported for efficient communication with them. In this paper, we propose a deep learning based IOT device recognition system for interaction with these devices. The overall system consists of a TensorFlow based deep learning server and two Android apps for data collection and recognition purposes. As the basic neural network model, we adopted Google's inception-v3, and modified the output stage to classify 20 types of IOT devices. After creating a data set consisting of 1000 images of 20 categories, we trained our deep learning network using a transfer learning technology. As a result of the experiment, we achieve 94.5% top-1 accuracy and 98.1% top-2 accuracy.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.346-347
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• 2021

기계학습을 위한 패턴인식을 위해서는 학습데이터의 양이 많을수록 그 성능이 향상된다. 하지만 일상에서 검출해내야하는 패턴의 종류 및 정보가 항상 많은 양의 학습데이터를 확보할 수는 없다. 따라서 일반적인 기계학습을 위해 적은데이터셋을 의미있게 부풀릴 필요가 있다. 본 연구에서는 기계학습을 수행할 수 있도록 데이터를 증강시키는 기법에 관해 연구한다. 적은데이터셋을 이용하여 기계학습을 수행하는 대표적인 방법이 전이학습(transfer learning) 기법이다. 전이학습은 범용데이터셋으로 기본적인 학습을 수행한 후 목표데이터셋을 최종 단계에 대입함으로써 결과를 얻어내는 방법이다. 본 연구에서는 ImageNet과 같은 범용데이터셋으로 학습시킨 학습모델을 증강된 데이터를 이용하여 특징추출셋으로 사용하여 원하는 패턴에 대한 검출을 수행한다.

• The Journal of Asian Finance, Economics and Business
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• 제6권1호
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• pp.9-19
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• 2019

A recent survey of Australian directors conducted by the Financial Reporting Council found that directors require a detailed understanding of technical accounting issues. With the aim of understanding learner difficulties in learning and applying higher learning material relevant to directors, this study explores the transfer pricing topic taught as a case presentation in an undergraduate accounting program at an Australian university. Before intervention with improvements, this study invited 25 students to take part in the study after they had learned the topic and been given one week to understand it. By adopting a transfer pricing problem presented in their essential reading and interviewing those students to gain further insights, the study found that learners experienced conceptual difficulties at various stages in attempting to learn. Intervention to ease learning difficulties was addressed through instructor training. The intervention improvements included using guided workbooks to develop a better understanding of concepts among learners, and representing the problem at hand with diagrams. After intervention with improvements, this study repeated the same procedures with 25 students who had not taken part in the previous study and found that interventions increased the learning. Results have implications for most directors, who are novices to the detailed technical accounting issues of transfer pricing.

• 한국전자통신학회논문지
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• 제18권3호
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• pp.413-420
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• 2023

오래전부터 연구자들은 CBIR에 대한 많은 연구로 인해 이미지 검색 분야에 우수한 결과를 제시하였다. 그러나 이미지에 대한 이러한 검색 결과와 사람이 인식하는 결과 사이에 의미적 격차는 여전히 존재한다. 적은 수의 이미지를 사용하여 사람이 인식하는 수준의 이미지를 분류하는 것은 아직까지 어려운 문제이다. 따라서 본 논문은 이미지 검색에서 사람과 검색 시스템의 이미지의 의미적 격차를 최소화하기 위해 딥 러닝 기반의 전이 학습을 이용한 이미지 분류 모델을 제안한다. 실험 결과, 학습 모델의 손실률은 0.2451%, 정확도는 0.8922%로 제안한 이미지 분류 방법의 구현은 원하는 목표를 달성할 수 있었다. 그리고 딥 러닝에서 CNN의 전이 학습 모델 방법이 새로운 데이터를 추가하여 이미지 데이터베이스를 구축하는데 효과적인 결과를 확인할 수 있었다.

• PNF and Movement
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• 제11권2호
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• pp.77-85
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• 2013

Purpose : The purpose of this study is to investigate the effects school business hospital-based integrated health education on learning transfer factor and level. Methods : This study conducted a questionnaire survey of 60 students at D college using metastatic diagnostic tool who took the integrated health education curriculum, statistical analysis utilized the SPSS 17.0 for window version. Results : On comparison of the details 5 clauses, 29 questions using LTSI, this study found that the integrated health education based on the school business hospital is effective for learning transfer. Conclusion : What the integrated health education based on clinic practice system at D college to overcome the limitations of health and medical line is effective for learning transfer and it will be useful to cultivate professional.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.420-437
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• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• 경영정보학연구
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• 제12권3호
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• pp.19-48
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• 2010

본 연구는 기존의 e-러닝에 관한 개념적 연구 결과 및 실증적 연구들을 바탕으로 대학 e-러닝 교육의 최우선 목표라고 할 수 있는 학습자에게 학습의 흥미를 지속시키고, 학습효과를 극대화 할 수 있는 다양한 요인들을 유형화하여, 이를 실종적으로 검정하였다. 또한 전반적으로 어떠한 요인이 e-러닝 학습효과에 많은 영향을 미칠 수 있는지에 대해서 함께 분석하였다. 그리고 e-러닝 학습효과라는 결과 요인을 하나의 단일적으로 측정하였던 기존의 많은 연구에서 학습만족 및 학습전이, 그리고 학습추천 등 크게 3가지로 나누어 세부적으로 분석을 실시하였다. 이와 같은 연구목적을 달성하기 위하여 e러닝 학습효과에 유의한 영향을 미친다고 가정한 요인을 크게 학습내용과 교수설계, 사용자 편의성 등 3개의 요인으로 유형화하여 설정하였다. 그리고 학습대용 요인은 학습주제와 목표, 지식정보, 일관성과 적절성 등 3개의 세부적 요인으로 구성하였고, 교수설계 요인은 흥미와 공감성, 상호작용, 내용제시, 설명전략 등 4개의 세부적 요인으로 구성하였다. 마지막으로 사용자 편의성 요인은 화면구성, 내용 및 진도확인 등 2개의 세부적 요인으로 구성하였다. 분석결과, 학습내용과 교수설계, 그리고 사용자 편의성 등 3개의 요인 모두 e-러닝 학습효과(학습만족, 학습전이, 학습추천)에 유의한 영향용 미치는 것으로 분석되었다. 세부적으로 e-러닝 학습만족에 있어서는 학습내용 요인의 학습주제와 목표가 가장 많은 영향을 미치는 것으로 나타났다. 이는 학습만족을 높이기 위해선 학습주제와 목표는 학습자를 기준으로 하여 설정해야 할 것이며, 평가가 가능한 학습목표로 선정해야 하는 것이 가장 중요하다는 것을 알 수 있다. 학습전이에 있어서는 교수설계 요인의 내용제시가 가장 많은 영향을 미치는 것으로 나타났다. 이는 학습전이를 높이기 위해선 강의내용의 상호관계와 제시순서가 학습을 촉진할 수 있도록 체계적으로 구조화시켜 학습자에게 접근이 가능하도록 해야 하는 것이 가장 중요하다는 것을 알 수 있다. 그리고 학습추천에 있어서는 교수설계 요인의 흥미와 공감성이 가장 많은 영향을 미치는 것으로 나타났다. 이는 교수자가 시의적절한 미디어를 잘 활용하여 학습자의 흥미를 최대한 유도시키며, 적시에 활용할 수 있도록 학습자가 공감을 가질 수 있게 강의를 진행하는 것이 가장 중요하다는 것을 알 수 있다.

• 디지털산업정보학회논문지
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• 제13권1호
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• pp.213-227
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• 2017

The recent entry into information society as well as the development and universalization of the Internet through rapid development of ICT technology produced a new educational method called PMP learning. PMP learning overcomes restrictions of previous education methods in terms of time and space and allows the learners to customize their learning environments according to their leads, providing voluntary education that centers on the learners. This study aims to verify the causal relationship in academic achievement of PMP learners through the theory of basic psychological desire, learning satisfaction, and learning metastasis. In order to accomplish this, a study model which applies perceived autonomy, perceived competence, and perceived relationship, which are major variables of the theory of basic psychological desire, was presented. For practical verification of the study model, survey analysis was conducted for students of R High School in Hamyang. Through this, the study aims to provide basic materials for improving the academic achievement of learners in PMP learning. It also plans to suggest educational effects that can be obtained by supporting intrinsic motivation of learners.