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Daeyoung Kim
KAIST
270Publications
25H-index
2,320Citations
Publications 270
Newest
Published in The Journal of Supercomputing 2.16
Hoang Minh Nguyen2
Estimated H-index: 2
(KAIST),
Gaurav Kalra (KAIST), Daeyoung Kim25
Estimated H-index: 25
(KAIST)
Cloud computing has been developed as a means to allocate resources efficiently while maintaining service-level agreements by providing on-demand resource allocation. As reactive strategies cause delays in the allocation of resources, proactive approaches that use predictions are necessary. However, due to high variance of cloud host load compared to that of grid computing, providing accurate predictions is still a challenge. Thus, in this paper we have proposed a prediction method based on Long...
Seung Ju Cho , Tae Joon Jun2
Estimated H-index: 2
+ -3 AuthorsDaeyoung Kim25
Estimated H-index: 25
Nowadays, Deep learning techniques show dramatic performance on computer vision area, and they even outperform human. This is a problem combined with the safety of artificial intelligence, which has recently been studied a lot. These attack have shown that they can fool models of image classification, semantic segmentation, and object detection. We point out this attack can be protected by denoise autoencoder, which is used for denoising the perturbation and restoring the original images. We exp...
Published on Jun 1, 2019in Pattern Recognition 5.90
Inpyo Hong1
Estimated H-index: 1
(KAIST),
Youngbae Hwang , DaeyoungKim6
Estimated H-index: 6
(KAIST)
Abstract Image denoising is a fundamental task in computer vision and image processing domain. In recent years, the task has been tackled with deep neural networks by learning the patterns of noises and image patches. However, because of the high diversity of natural image patches and noise distributions, a huge network with a large amount of training data is necessary to obtain a state-of-the-art performance. In this paper, we propose a novel ensemble strategy of exploiting multiple deep neural...
Published on May 1, 2019
Wondeuk Yoon1
Estimated H-index: 1
(KAIST),
Indal Choi (LG Electronics), DaeyoungKim6
Estimated H-index: 6
(KAIST)
Today, Internet of Things (IoT) technology is applied to everywhere providing tremendous amounts of IoT service such as home control, facility management, and social public services. The GS1, a non-profit international standard organization, standardized an Object Name Service (ONS) which enables users to manage and discover services in the midst of tremendous amounts of service. However, it has a vulnerability in security and fault tolerance of providing service, because the ONS operates based ...
Published on Apr 11, 2019in The Journal of Supercomputing 2.16
Hoang Minh Nguyen2
Estimated H-index: 2
(KAIST),
Gaurav Kalra (KAIST)+ 2 AuthorsDaeyoungKim6
Estimated H-index: 6
(KAIST)
Workload prediction is an essential prerequisite to allocate resources efficiently and maintain service level agreements in cloud computing environment. However, the best solution for a prediction task may not be a single model due to the challenge of varied characteristics of different systems. Thus, in this work, we propose an ensemble model, namely ESNemble, based on echo state network (ESN) for workload time series forecasting. ESNemble consists of four main steps, including features selecti...
Tae Joon Jun2
Estimated H-index: 2
(KAIST),
S.G. Kang2
Estimated H-index: 2
(UOU: University of Ulsan)
+ -3 AuthorsYoung-Hak Kim93
Estimated H-index: 93
(UOU: University of Ulsan)
Acute coronary syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including feed-forward neural network (FNN), K-nearest...
Tae Joon Jun2
Estimated H-index: 2
,
Youngsub Eom1
Estimated H-index: 1
+ 4 AuthorsDaeyoungKim6
Estimated H-index: 6
In this paper, we proposed Transferable Ranking Convolutional Neural Network (TRk-CNN) that can be effectively applied when the classes of images to be classified show a high correlation with each other. The multi-class classification method based on the softmax function, which is generally used, is not effective in this case because the inter-class relationship is ignored. Although there is a Ranking-CNN that takes into account the ordinal classes, it cannot reflect the inter-class relationship...
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