• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.405-406
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• 2018

본 논문은 소표본 의료용 영상 분석의 정확도 향상을 위해 전이학습 모델을 feature extractor로 구축하여 학습시키는 방법을 연구하였으며 성능 평가를 위해 선학습모델로 AlexNet, ResNet, DenseNet을 사용하여 fine tuning 기법을 적용하였을 때와의 성능을 비교 분석하였다. 그 결과 실험에 사용된 3개의 모델에서 fine tuning 기법보다 향상된 정확도를 보임을 확인하였고, 또한 ImageNet으로 학습된 AlexNet, ResNet, DenseNet이 소표본 의료용 X-Ray 영상에 적용될 수 있음을 보였다.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.53-59
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• 2017

Optical character recognition (OCR) automatically recognizes text in an image. OCR is still a challenging problem in computer vision. A successful solution to OCR has important device applications, such as text-to-speech conversion and automatic document classification. In this work, we analyze character recognition performance using the current state-of-the-art deep-learning structures. One is the AlexNet structure, another is the LeNet structure, and the other one is the SPNet structure. For this, we have built our own dataset that contains digits and upper- and lower-case characters. We experiment in the presence of salt-and-pepper noise or Gaussian noise, and report the performance comparison in terms of recognition error. Experimental results indicate by five-fold cross-validation that the SPNet structure (our approach) outperforms AlexNet and LeNet in recognition error.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1257-1262
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• 2018

In this paper, a deep learning method is proposed to detect the fire effectively by using video of surveillance camera. Based on AlexNet model, classification performance is compared according to kernel size and stride of convolution layer. Dataset for learning and interfering are classified into two classes such as normal and fire. Normal images include clouds, and foggy images, and fire images include smoke and flames images, respectively. As results of simulations, it is shown that the larger kernel size and smaller stride shows better performance.

• Journal of radiological science and technology
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• v.43 no.5
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• pp.397-404
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• 2020

Coronavirus disease(COVID-19) is highly infectious disease that directly affects the lungs. To observe the clinical findings from these lungs, the Chest Radiography(CXR) can be used in a fast manner. However, the diagnostic performance via CXR needs to be improved, since the identifying these findings are highly time-consuming and prone to human error. Therefore, Artificial Intelligence(AI) based tool may be useful to aid the diagnosis of COVID-19 via CXR. In this study, we explored various Deep learning(DL) approach to classify COVID-19, other viral pneumonia and normal. For the original dataset and lung-segmented dataset, the pre-trained AlexNet, SqueezeNet, ResNet18, DenseNet201 were transfer-trained and validated for 3 class - COVID-19, viral pneumonia, normal. In the results, AlexNet showed the highest mean accuracy of 99.15±2.69% and fastest training time of 1.61±0.56 min among 4 pre-trained neural networks. In this study, we demonstrated the performance of 4 pre-trained neural networks in COVID-19 diagnosis with CXR images. Further, we plotted the class activation map(CAM) of each network and demonstrated that the lung-segmentation pre-processing improve the performance of COVID-19 classifier with CXR images by excluding background features.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.293-307
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• 2020

Every year, the floating macroalgae, green and golden tide, are massively detected at the Yellow Sea and East China Sea. After influx of them to the aquaculture facility or beach, it occurs enormous economic losses to remove them. Currently, remote sensing is used effectively to detect the floating macroalgae flowed into the coast. But it has difficulties to detect the floating macroalgae exactly because of the wavelength overlapped with other targets in the ocean. Also, it is difficult to distinguish between green and golden tide because they have similar spectral characteristics. Therefore, we tried to distinguish between green and golden tide applying the Deep learning method to the satellite images. To determine the network, the optimal training conditions were searched to train the AlexNet. Also, Gaofen-1 WFV images were used as a dataset to train and validate the network. Under these conditions, the network was determined after training, and used to confirm the test data. As a result, the accuracy of test data is 88.89%, and it can be possible to distinguish between green and golden tide with precision of 66.67% and 100%, respectively. It is interpreted that the AlexNet can be pick up on the subtle differences between green and golden tide. Through this study, it is expected that the green and golden tide can be effectively classified from various objects in the ocean and distinguished each other.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.417-423
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• 2017

Recently, many embedded devices that have the computing capability required for deep learning have become available; hence, many new applications using these devices are emerging. However, these embedded devices have an architecture different from that of PCs and high-performance servers. In this paper, we propose a method that improves the performance of deep-learning framework by considering the architecture of an embedded device that shares memory between the CPU and the GPU. The proposed method is implemented in Caffe, an open-source deep-learning framework, and is evaluated on an NVIDIA Jetson TK1 embedded device. In the experiment, we investigate the image recognition performance of several state-of-the-art deep-learning networks, including AlexNet, VGGNet, and GoogLeNet. Our results show that the proposed method can achieve significant performance gain. For instance, in AlexNet, we could reduce image recognition latency by about 33% and energy consumption by about 50%.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.53-57
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• 2018

A poisoning accident is often caused by a situation in which people eat poisonous mushrooms because they cannot distinguish between edible mushrooms and poisonous mushrooms. In this paper, we propose an automatic mushroom recognition system by using the convolutional neural network. We collected 1478 mushroom images of 38 species using image crawling, and used the dataset for learning the convolutional neural network. A comparison experiment using AlexNet, VGGNet, and GoogLeNet was performed using the collected datasets, and a comparison experiment using a class number expansion and a fine-tuning technique for transfer learning were performed. As a result of our experiment, we achieve 82.63% top-1 accuracy and 96.84% top-5 accuracy on test set of our dataset.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.63-68
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• 2022

In this study, we produced defect detection models using parallel convolution neural networks. If convolution neural networks are constructed parallel type, the model's detection accuracy will increase and detection time will decrease. We produced parallel-type defect detection models using 4 types of convolutional algorithms. The performance of models was evaluated using evaluation indicators. The model's performance is detection accuracy and detection time. We compared the performance of each parallel model. The detection accuracy of the model using AlexNet is 97 % and the detection time is 0.3 seconds. We confirmed that when AlexNet algorithm is constructed parallel type, the model has the highest performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.636-638
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• 2022

This paper is a study to apply CNN(Convolution Neural Network) to fundus images for identifying glaucoma. Fundus images are evaluated in the field of medical diagnosis detection, which are diagnosing of blood vessels and nerve tissues, retina damage, various cardiovascular diseases and dementia. For the experiment, using normal image set and glaucoma image set, two types of image set are classifed by using AlexNet. The result performs that glaucoma with abnormalities are activated and characterized in feature map.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.423-426
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• 2021

본 논문은 전역 스타일 토큰(Global Style Token)을 기준으로 하여 감정의 세기를 조절할 수 있는 방법을 소개한다. 기존의 전역 스타일 토큰 연구에서는 원하는 스타일이 포함된 참조 오디오(reference audio)을 사용하여 음성을 합성하였다. 그러나, 참조 오디오의 스타일대로만 음성합성이 가능하기 때문에 세밀한 감정 조절에 어려움이 있었다. 이 문제를 해결하기 위해 본 논문에서는 전역 스타일 토큰의 레퍼런스 인코더 부분을 잔여 블록(residual block)과 컴퓨터 비전 분야에서 사용되는 AlexNet으로 대체하였다. AlexNet은 5개의 함성곱 신경망(convolutional neural networks) 으로 구성되어 있지만, 본 논문에서는 1개의 신경망을 제외한 4개의 레이어만 사용했다. 청취 평가(Mean Opinion Score)를 통해 제시된 방법으로 감정 세기의 조절 가능성을 보여준다.