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Max Contribution: An Online Approximation of Optimal Resource Allocation in Delay Tolerant Networks
Kyunghan Lee,Jaeseong Jeong,Yung Yi,Hyungsuk Won,Injong Rhee,Song Chong IEEE 2015 IEEE transactions on mobile computing Vol.14 No.3
<P>In this paper, a joint optimization of link scheduling, routing and replication for delay-tolerant networks (DTNs) has been studied. The optimization problems for resource allocation in DTNs are typically solved using dynamic programming which requires knowledge of future events such as meeting schedules and durations. This paper defines a new notion of approximation to the optimality for DTNs, called snapshot approximation where nodes are not clairvoyant, i.e., not looking ahead into future events, and thus decisions are made using only contemporarily available knowledges. Unfortunately, the snapshot approximation still requires solving an NP-hard problem of maximum weighted independent set (MWIS) and a global knowledge of who currently owns a copy and what their delivery probabilities are. This paper proposes an algorithm, Max-Contribution (MC) that approximates MWIS problem with a greedy method and its distributed online approximation algorithm, Distributed Max-Contribution (DMC) that performs scheduling, routing and replication based only on locally and contemporarily available information. Through extensive simulations based on real GPS traces tracking over 4,000 taxies and 500 taxies for about 30 days and 25 days in two different large cities, DMC is verified to perform closely to MC and outperform existing heuristically engineered resource allocation algorithms for DTNs.</P>
FisheyeNet: 딥러닝을 활용한 어안렌즈 왜곡 보정
이홍재(Hongjae Lee),원재성(Jaeseong Won),이다은(Daeun Lee),이성배(Seongbae Rhee),김규헌(Kyuheon Kim) 한국방송·미디어공학회 2021 한국방송공학회 학술발표대회 논문집 Vol.2021 No.6
Fisheye 카메라로 촬영된 영상은 일반 영상보다 넓은 시야각을 갖는 장점으로 여러 분야에서 활용되고 있다. 그러나 fisheye 카메라로 촬영된 영상은 어안렌즈의 곡률로 인하여 영상의 중앙 부분은 팽창되고 외곽 부분은 축소되는 방사 왜곡이 발생하기 때문에 영상을 활용함에 있어서 어려움이 있다. 이러한 방사 왜곡을 보정하기 위하여 기존 영상처리 분야에서는 렌즈의 곡률을 수학적으로 계산하여 보정하기도 하지만 이는 각각의 렌즈마다 왜곡 파라미터를 추정해야 하기 때문에, 개별적인 GT (Ground Truth) 영상이 필요하다는 제한 사항이 있다. 이에 본 논문에서는 렌즈의 종류마다 GT 영상을 필요로 하는 기존 기술의 제한 사항을 극복하기 위하여, fisheye 영상만을 입력으로 하여 왜곡계수를 계산하는 딥러닝 네트워크를 제안하고자 한다. 또한, 단일 왜곡계수를 왜곡모델로 활용함으로써 layer 수를 크게 줄일 수 있는 경량화 네트워크를 제안한다.
Kim, Yu Kyong,Lee, Juyoung,Oh, Jaeseong,Rhee, Su-jin,Shin, Seung Han,Yoon, Seo Hyun,Lee, SeungHwan,Kim, Han-Suk,Yu, Kyung-Sang American Society for Microbiology 2019 Antimicrobial Agents and Chemotherapy Vol.63 No.6
<P>Fluconazole is an antifungal agent with reported evidence for its prophylactic effect against systemic fungal infection in preterm infants. The aim of this study was to build a population pharmacokinetic model to evaluate the pharmacokinetic characteristics of intravenous and oral fluconazole in preterm infants with the current prophylactic fluconazole dosing regimen.</P><P>Fluconazole is an antifungal agent with reported evidence for its prophylactic effect against systemic fungal infection in preterm infants. The aim of this study was to build a population pharmacokinetic model to evaluate the pharmacokinetic characteristics of intravenous and oral fluconazole in preterm infants with the current prophylactic fluconazole dosing regimen. A pharmacokinetic model was developed using 301 fluconazole concentrations from 75 preterm infants with a baseline body weight (WT) ranging from 0.5 to 1.5 kg and an estimated glomerular filtration rate (eGFR) ranging from 12.9 to 58.5 ml/min/1.73 m<SUP>2</SUP>. Eligible infants received an intravenous or oral dose of 3 mg/kg of body weight of fluconazole, twice weekly with a ≥72-h dose interval, for 4 weeks. The model was qualified with basic goodness-of-fit diagnostics, visual predictive checks, and bootstrapping. The fluconazole pharmacokinetics was well described with a one-compartment linear model with a proportional residual error. The population clearance (CL) and volume of distribution (<I>V</I>) were derived as 0.0197 × (WT/1.00)<SUP>0.746</SUP> × (eGFR/25.0)<SUP>0.463</SUP> × exp(η) and 1.04 × WT × exp(η), respectively. Such covariate analyses augment the awareness of the need for personalized dosing in preterm infants. (This study has been registered at ClinicalTrials.gov under identifier NCT01683760.)</P>