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Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting
Kim, Kyungsang,Wu, Dufan,Gong, Kuang,Dutta, Joyita,Kim, Jong Hoon,Son, Young Don,Kim, Hang Keun,El Fakhri, Georges,Li, Quanzheng IEEE 2018 IEEE transactions on medical imaging Vol.37 No.6
<P>Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) method and trained the network using full-dose images as the ground truth and low dose images reconstructed from downsampled data by Poisson thinning as input. Since most published deep networks are trained at a predetermined noise level, the noise level disparity of training and testing data is a major problem for their applicability as a generalized prior. In particular, the noise level significantly changes in each iteration, which can potentially degrade the overall performance of iterative reconstruction. Due to insufficient existing studies, we conducted simulations and evaluated the degradation of performance at various noise conditions. Our findings indicated that DnCNN produces additional bias induced by the disparity of noise levels. To address this issue, we propose a local linear fitting function incorporated with the DnCNN prior to improve the image quality by preventing unwanted bias. We demonstrate that the resultant method is robust against noise level disparities despite the network being trained at a predetermined noise level. By means of bias and standard deviation studies via both simulations and clinical experiments, we show that the proposed method outperforms conventional methods based on total variation and non-local means penalties. We thereby confirm that the proposed method improves the reconstruction result both quantitatively and qualitatively.</P>
LVRT/HVRT System 모델 개발 전략에 관한 연구
김병기(Byungki Kim),유경상(Kyungsang Ryu),남양현(Yanghyun Nam),김찬수(Chansoo Kim),고희상(Heesang ko),김미성(Misung Kim),박재범(Jaebum Park),김대진(Daejin Kim) 대한전기학회 2021 대한전기학회 학술대회 논문집 Vol.2021 No.7
본 논문에서는 대용량 풍력발전기를 대상으로 일정기준이상 연계운전 능력을 평가할 수 있는 10MW급 이상의 LVRT(Low voltage Ride Through)/HVRT(Low voltage Ride Through)기능 모의가 가능한 시험장비의 개발 방안을 제안한다. 즉 RLC 방식의 단점을 보완한 단권변압기를 기반 탭 변환 방식시험장치의 권선비 조정 모델을 제안한다.. 상기에서 제안한 설계 및 운영 방안을 바탕으로 구현한축소형 LVRT/HVRT 시험장비의 성능시험을 통해 본 논문에서 제시한 설비의 유용성을 확인하였다.
장연주(Yeonju Jang),최승윤(SeongYune Choi),강윤지(Yunji Kang),김경상(Kyungsang Kim),김원유(Wonyou Kim),김정남(Jeongnam Kim),김학인(Hakin-In Kim),손지원(Son-Jee Won),장병철(Byeong-Cheol Jang),김한성(Han-Sung Kim),박광현(Kwang-Hyun Par 한국컴퓨터교육학회 2021 한국컴퓨터교육학회 학술발표대회논문집 Vol.25 No.1(A)
인공지능(AI) 교육에 대한 전 세계적 열기가 뜨거운 가운데, 우리나라에서는 AI 인재를 양성하기 위한 교육정책을 제시하였고 교육현장과 학계에서는 초등학생들부터 시작하는 AI 교육과정과 교육 자료를 제시하고 있다. 하지만 초등학교에서 교사들이 수업에 직접 활용할 수 있는 교수학습자료는 매우 부족한 실정이다. 따라서 본 연구에서는 초등 교사들이 효과적으로 AI 교육을 할 수 잇도록 프로그램을 개발하는 것을 목적으로 한다. 본 연구에서 개발한 교육 프로그램은 이론과 실습을 포괄하는 구성이며 AI의 윤리적인 이슈까지 포괄할 수 있도록 내용을 구성하였다. 학생들은 본 프로그램을 통하여 AI를 체험하고, AI의 원리에 대해 학습하며 블록코딩을 통한 실습을 할 수 있으며 교사들이 현장에서 바로 활용할 수 있도록 지도안과 활동 예시들을 함께 제공하였다.
Kim, Kyungsang,Lee, Taewon,Seong, Younghun,Lee, Jongha,Jang, Kwang Eun,Choi, Jaegu,Choi, Young Wook,Kim, Hak Hee,Shin, Hee Jung,Cha, Joo Hee,Cho, Seungryong,Ye, Jong Chul Published for the American Association of Physicis 2015 Medical physics Vol.42 No.9
<P>In digital breast tomosynthesis (DBT), scatter correction is highly desirable, as it improves image quality at low doses. Because the DBT detector panel is typically stationary during the source rotation, antiscatter grids are not generally compatible with DBT; thus, a software-based scatter correction is required. This work proposes a fully iterative scatter correction method that uses a novel fast Monte Carlo simulation (MCS) with a tissue-composition ratio estimation technique for DBT imaging.</P>
Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data
KyungSang Lee,Hyewon Lee,Woojae Myung,GilYoung Song,Kihwang Lee,Ho Kim,Bernard J. Carroll,DohKwan Kim 대한신경정신의학회 2018 PSYCHIATRY INVESTIGATION Vol.15 No.4
Objective-Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. Methods-The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Results-Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. Conclusion-These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.
Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty
Kyungsang Kim,Jong Chul Ye,Worstell, William,Jinsong Ouyang,Rakvongthai, Yothin,El Fakhri, Georges,Quanzheng Li IEEE 2015 IEEE transactions on medical imaging Vol.34 No.3
<P>Spectral computed tomography (CT) is a promising technique with the potential for improving lesion detection, tissue characterization, and material decomposition. In this paper, we are interested in kVp switching-based spectral CT that alternates distinct kVp X-ray transmissions during gantry rotation. This system can acquire multiple X-ray energy transmissions without additional radiation dose. However, only sparse views are generated for each spectral measurement; and the spectra themselves are limited in number. To address these limitations, we propose a penalized maximum likelihood method using spectral patch-based low-rank penalty, which exploits the self-similarity of patches that are collected at the same position in spectral images. The main advantage is that the relatively small number of materials within each patch allows us to employ the low-rank penalty that is less sensitive to intensity changes while preserving edge directions. In our optimization formulation, the cost function consists of the Poisson log-likelihood for X-ray transmission and the nonconvex patch-based low-rank penalty. Since the original cost function is difficult to minimize directly, we propose an optimization method using separable quadratic surrogate and concave convex procedure algorithms for the log-likelihood and penalty terms, which results in an alternating minimization that provides a computational advantage because each subproblem can be solved independently. We performed computer simulations and a real experiment using a kVp switching-based spectral CT with sparse-view measurements, and compared the proposed method with conventional algorithms. We confirmed that the proposed method improves spectral images both qualitatively and quantitatively. Furthermore, our GPU implementation significantly reduces the computational cost.</P>