Search

Your search keyword '"Min, Junhong"' showing total 693 results
Start Over Author "Min, Junhong"
693 results on '"Min, Junhong"'

16. Image-guided in situ cancer vaccination with combination of multi-functional nano-adjuvant and an irreversible electroporation technique

Author

Han, Jun-Hyeok, Lee, Yun Young, Shin, Ha Eun, Han, Jieun, Kang, Jeon Min, James Wang, Chi-Pin, Park, Jung-Hoon, Kim, Se-Na, Yoon, Jong-Hwi, Kwon, Ho-Keun, Park, Dae-Hwan, Park, Tae-Eun, Choy, Young Bin, Kim, Dong-Hyun, Kim, Tae-Hyung, Min, Junhong, Kim, Ik-Hwan, Park, Chun Gwon, Han, Dong Keun, and Park, Wooram

Published
2022
Full Text
View/download PDF

23. Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction

Author

Kang, Eunhee, Min, junhong, and Ye, Jong Chul

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally complex because of the repeated use of the forward and backward projection. Inspired by this success of deep learning in computer vision applications, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture are not fully recovered, which was unfamiliar to some radiologists. To cope with this problem, here we propose a direct residual learning approach on directional wavelet domain to solve this problem and to improve the performance against previous work. In particular, the new network estimates the noise of each input wavelet transform, and then the de-noised wavelet coefficients are obtained by subtracting the noise from the input wavelet transform bands. The experimental results confirm that the proposed network has significantly improved performance, preserving the detail texture of the original images.

Published
2017

30. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction

Author

Kang, Eunhee, Min, Junhong, and Ye, Jong Chul

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to the potential risk of inducing cancers, radiation dose of X-ray CT should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts usually occur due to photon starvation, beamhardening, etc, which decrease the reliability of diagnosis. Thus, high quality reconstruction from low-dose X-ray CT data has become one of the important research topics in CT community. Conventional model-based denoising approaches are, however, computationally very expensive, and image domain denoising approaches hardly deal with CT specific noise patterns. To address these issues, we propose an algorithm using a deep convolutional neural network (CNN), which is applied to wavelet transform coefficients of low-dose CT images. Specifically, by using a directional wavelet transform for extracting directional component of artifacts and exploiting the intra- and inter-band correlations, our deep network can effectively suppress CT specific noises. Moreover, our CNN is designed to have various types of residual learning architecture for faster network training and better denoising. Experimental results confirm that the proposed algorithm effectively removes complex noise patterns of CT images, originated from the reduced X-ray dose. In addition, we show that wavelet domain CNN is efficient in removing the noises from low-dose CT compared to an image domain CNN. Our results were rigorously evaluated by several radiologists and won the second place award in 2016 AAPM Low-Dose CT Grand Challenge. To the best of our knowledge, this work is the first deep learning architecture for low-dose CT reconstruction that has been rigorously evaluated and proven for its efficacy., Comment: Will appear in Medical Physics (invited paper); 2016 AAPM low-dose CT Grand Challenge 2nd Place Award

Published
2016
Full Text
View/download PDF

38. Robust design optimization of spin algorithm to reduce spinning time and vibration in washing machine.

Author

In, Daekil, Lim, Dae-Guen, Min, Junhong, Kim, Seung-Wook, Seo, Hyunseok, Kim, Sungmin, Choi, Dong-Hoon, and Cheong, Cheolung

Subjects
OPTIMIZATION algorithms, ROBUST optimization, WASHING machines, VIBRATION (Mechanics), MONTE Carlo method, SPIN-spin interactions
Abstract

This study focuses on optimizing the spin algorithm of washing machines to reduce spin time and vibration, two crucial performance metrics often in a trade-off relationship due to structural limitations. We developed a simulator for the spin process that accounts for the uncertainty associated with the clothes' position in the drum. Using this simulator, we designed a robust optimization algorithm to minimize spin time while maintaining acceptable vibration levels. The variation in the mass and position of laundry within the drum primarily affects vibration performance during the spin process. We formulated the unknown unbalance caused by the laundry as test-implementable unbalance parameters (UBP), represented as random variables. The uncertainties in the laundry's unbalance for repeated experiments were estimated by the distribution of these UBPs. Experiments were conducted at parametrically designed points to measure vibrations and internal sensor values for control in specific revolutions per minute (RPM)ranges. These data were used to create approximate models. Monte Carlo simulation was then applied with UBP distributions obtained from the clothing experiments to meta-models, and the simulator was equipped with the spin algorithm. This process allowed us to predict the distribution of the maximum tub vibration displacement and spin time. A robust optimization algorithm was subsequently applied to the simulation model to derive the optimal spin algorithm. The proposed methodology yielded substantial improvements: a 25% reduction in the mean value of maximum displacement and a 90% reduction in the mean value of spin time compared to initial values. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results