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Computational Methods for Patient-Specific Perfusion Simulations of Coronary Arteries
Hyun Jin Kim(김현진),L. Papamanolis,C. Jaquet,M. Sinclair,M. Schaap,I. Danad,P. van Diemen,P. Knaapen,L. Najman,H. Talbot,C. A. Taylor,I. E. Vignon-Clementel 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.4
Patient-specific computational simulations of blood flow are utilized to diagnose and predict treatment outcomes of coronary artery disease. The computational simulations, however, are limited when estimating perfusion in the myocardium as multiscale vessels from arteries to capillaries need to be developed. We propose a multiscale patient-specific computational model framework to simulate blood flow from large coronary arteries to myocardial tissues. Patient vasculatures were segmented from coronary computed tomography angiography data and then extended from the image-based model down to the arteriole level using a space-filling synthetic forest of arterial trees. Blood flow is modelled by coupling a 1-D model of the coronary arteries to a single-compartment Darcy myocardium model. Simulated results for 5 patients with non-obstructive coronary artery disease are compared to [<sup>15</sup>O]H<sub>2</sub>O PET exam data for both resting and hyperemic conditions. Results on a patient with a severe disease demonstrate coronary artery disease can predict myocardial regions with perfusion deficit. This multiscale computational model of simulating blood flow from the epicardial coronary arteries to the left ventricle myocardium will be further validated and applied to human data.
다양한 센서 기반의 침입체 탐지, 분류 및 추적 알고리즘 개발
김원철(Wonchul Kim),임진홍(Jinhong Lim),김태완(Taewan Kim),손영동(Youngdong Son),김현진(H. Jin Kim),김진영(Jinyoung Kim),홍수연(Sooyoun Hong),김한동(Handong Kim) 제어로봇시스템학회 2017 제어·로봇·시스템학회 논문지 Vol.23 No.11
Surveillance is one of the major applications in wireless sensor network areas, and it is important to detect, classify and localize the targets. In this paper, we divide the research into two sections: (1) detecting and classifying the targets and (2) localizing them. To detect and classify multiple moving targets, we use acoustic and seismic sensors, and we analyze raw data from the sensors in both time and frequency domains. In this process, we must decide which features are useful for the classification to improve the performance and make it work in real time. Thus, we exploit Weibull likelihood and short-time Fourier transform (STFT) to extract the features as a sampling method. Then, we implement a support vector machine (SVM) and a neural network to classify the type of targets based on those features. Using the suggested algorithms, the proposed classifiers provide more accurate performance than the method that analyzes the raw data from only the frequency or time domain. For localization, Gaussian Process Regression (GPR) is used to estimate the relative location that corresponds to the received signal strength indication (RSSI) data. We also demonstrate the simultaneous localization with the process of detection and classification in real time. Finally, experimental results validate the suggested algorithm.
확률 모델을 이용한 분산형 능동적 정보 수집 알고리즘 연구
김다빈(Dabin Kim),김현진(H. Jin Kim) 제어로봇시스템학회 2021 제어·로봇·시스템학회 논문지 Vol.27 No.2
Motion strategies for multiple robots that actively acquire information in a dynamic environment have been widely studied. However, the existing active information gathering algorithms are restricted by the assumption of linear target dynamics or completely known models. In this study, we formulate the active information gathering problem with the belief distribution of the desired target information with unknown underlying dynamics. The reward function is derived based on the mutual information of the measurement and belief distribution, and it can be efficiently computed under the Gaussian assumption on the belief distribution. Moreover, a decentralized path planner is designed to maximize the reward function, which scales well in terms of both the numbers of agents and targets. We apply the proposed planner to an active target tracking scenario and validate the performance and scalability through a numerical simulation.
김창현(Changhyeon Kim),장영석(Youngseok Jang),김준하(Junha Kim),한영수(Youngsoo Han),김현진(H. Jin Kim) 제어로봇시스템학회 2022 제어·로봇·시스템학회 논문지 Vol.28 No.2
Visual navigation technology enables the pose of a robot to be estimated and the surrounding environment to be perceived using a vision sensor mounted on the robot. This technology is essential to autonomous driving systems in unmanned mobile vehicles and has been actively researched in visual odometry (VO) and visual simultaneous localization and mapping (vSLAM). Generally, the vision-based navigation algorithms perform data association and pose estimation under the assumption that the brightness of surrounding environments does not change over time and that the scene obtained from vision sensors is static. However, in realistic industrial sites or urban environments, the brightness of the environment varies, and dynamic objects such as workers and cars are present. These conditions may lead to a decline in the reliability and performance of visual navigation. Research on robust visual navigation under environmental variations, such as illumination changes and dynamic circumstances, has sought to solve this problem. This study proposes a state-of-the-art robust visual navigation system that is robust to illumination changes and dynamic environments. Moreover, our analysis and classification is based on the methodology used in each robust visual navigation.