• ETRI Journal
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• 제46권2호
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.

• 한국음향학회지
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• 제35권2호
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• pp.143-148
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• 2016

깊은 신경망은 데이터의 특성을 효과적으로 나타낼 수 있는 방법으로 최근 많은 응용 분야에서 활용되고 있다. 하지만, 제한적인 양의 데이터베이스는 깊은 신경망을 훈련하는 과정에서 과적합 문제를 야기할 수 있다. 본 논문에서는 풍부한 양의 음성 혹은 음악 데이터를 이용한 전이학습을 통해 제한적인 양의 사운드 이벤트에 대한 깊은 신경망을 효과적으로 훈련하는 방법을 제안한다. 일련의 실험을 통해 제안하는 방법이 적은 양의 사운드 이벤트 데이터만으로 훈련된 깊은 신경망에 비해 현저한 성능 향상이 있음을 확인하였다.

• 대한임베디드공학회논문지
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• 제14권6호
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• pp.287-294
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• 2019

With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

• 한국정보통신학회논문지
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• 제24권5호
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• pp.674-677
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• 2020

The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1387-1395
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• 2018

Core algorithm of deep learning Convolutional Neural Network(CNN) shows better performance than other machine learning algorithms. However, if there is not sufficient data, CNN can not achieve satisfactory performance even if the classifier is excellent. In this situation, it has been proven that the use of transfer learning can have a great effect. In this paper, we apply two transition learning methods(freezing, retraining) to three CNN models(ResNet-50, Inception-V3, DenseNet-121) and compare and analyze how the classification performance of CNN changes according to the methods. As a result of statistical significance test using various evaluation indicators, ResNet-50, Inception-V3, and DenseNet-121 differed by 1.18 times, 1.09 times, and 1.17 times, respectively. Based on this, we concluded that the retraining method may be more effective than the freezing method in case of transition learning in image classification problem.

• 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.

• 한국농공학회논문집
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• 제61권3호
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• pp.55-65
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• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• 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.

• 한국컴퓨터정보학회논문지
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• 제28권11호
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• pp.1-11
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• 2023

유방암은 전 세계적으로 여성들 대다수에게 가장 두려워하는 질환이다. 오늘날 데이터의 증가와 컴퓨팅 기술의 향상으로 머신러닝(machine learning)의 효율성이 증대되어 암 검출 및 진단 등에 중요한 역할을 하고 있다. 딥러닝(deep learning)은 인공신경망(artificial neural network, ANN)을 기반으로 하는 머신러닝 기술의 한 분야로 최근 여러 분야에서 성능이 급속도로 개선되어 활용 범위가 확대되고 있다. 본 연구에서는 유방암 분류를 위해 전이학습(transfer learning) 기반 DNN(Deep Neural Network)과 SVM(support vector machine)의 구조를 결합한 DNN-SVM Hybrid 모형을 제안한다. 전이학습 기반 제안된 모형은 적은 학습 데이터에도 효과적이고, 학습 속도도 빠르며, 단일모형, 즉 DNN과 SVM이 가지는 장점을 모두 활용 가능토록 결합함으로써 모형 성능이 개선되었다. 제안된 DNN-SVM Hybrid 모형의 성능평가를 위해 UCI 머신러닝 저장소에서 제공하는 WOBC와 WDBC 유방암 자료를 가지고 성능실험 결과, 제안된 모형은 여러 가지 성능 척도 면에서 단일모형인 로지스틱회귀 모형, DNN, SVM 그리고 앙상블 모형인 랜덤 포레스트보다 우수함을 보였다.