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서울 대기 중 PM<sub>2.5</sub> 내 OC와 EC로부터 SOC 추정방법의 비교 평가
유하영,김기애,김용표,정창훈,신혜정,문광주,박승명,이지이,Yoo, Ha Young,Kim, Ki Ae,Kim, Yong Pyo,Jung, Chang Hoon,Shin, Hye Jung,Moon, Kwang Ju,Park, Seung Myung,Lee, Ji Yi 한국입자에어로졸학회 2020 Particle and Aerosol Research Vol.16 No.1
The organic carbon in the ambient particulate matter (PM) is divided into primary organic carbon (POC) and secondary organic carbon (SOC) by their formation way. To regulate PM effectively, the estimation of the amount of POC and SOC separately is one of important consideration. Since SOC cannot be measured directly, previous studies have evaluated determination of SOC by the EC tracer method. The EC tracer method is a method of estimating the SOC value from calculating the POC by determining (OC/EC)pri which is the ratio of the measured values of OC and EC from the primary combustion source. In this study, three different ways were applied to OC and EC concentrations in PM<sub>2.5</sub> measured at Seoul for determining (OC/EC)pri: 1) the minimum value of OC/EC ratio during the measurement period; 2) regression analysis of OC vs. EC to select the lower 5-20% OC/EC ratio; 3) determining the OC/EC ratio which has lowest correlation coefficient value (R<sup>2</sup>) between EC and SOC which is reported as minimum R squared method (MRS). Each (OC/EC)pri ratio of three ways are 0.35, 1.22, and 1.77, respectively from the 1 hourly data. We compared the (OC/EC)pri ratio from 1hourly data with 24 hourly data and revealed that (OC/EC)pri estimated from 24 hourly data had twice larger than 1hourly data due to the low time resolution of sampling. We finally confirmed that the most appropriate value of (OC/EC)pri is that calculated by a regression analysis of 1 hourly data and estimated SOC amounts at PM<sub>2.5</sub> of the Seoul atmosphere.
유하영(Ha Young Yoo),정진주(Jin Joo Jung),최은주(Eun Ju Choi),강성태(Sung Tae Kang) 한국식품과학회 2010 한국식품과학회지 Vol.42 No.4
본 연구에서는 시판중인 일부 채소류의 중금속 함량을 조사해 국외기준과 비교하고 그 안전성을 검증해보고자 하였다. 전국에서 재배되어 시중에서 유통되고 있는 채소류 15종 243건을 시료로 하였다. 시료의 분석방법은 습식분해법 중 microwave 법을 이용하였으며 납, 카드뮴, 비소, 크롬, 구리, 망간, 아연을 유도결합 플라즈마 분광기(inductively coupled plasma spectrometer, ICP)를 이용하여 분석하였다. 조사결과 채소류의 평균 함량(최소-최대함량, ㎎/㎏)은 Pb, 0.0026(ND-0.0313); Cd, 0.0017(ND-0.0280), As, 0.0005(ND-0.0332); Cr, 0.0156(0.0010-0.1798); Cu, 0.3767(0.0556-1.3980); Mn, 3.0214(0.0182-26.4100); Zn, 3.5796(0.830-14.46)으로 나타났다. 본 연구를 통해 얻어진 결과들 중 납, 카드뮴, 비소는 기존 연구결과들과 비슷하거나 낮은 수준이었으며 크롬, 구리, 망간, 아연은 비슷한 수준으로 나타났다. 또한 조사한 품목의 주간 섭취량을 FAO/WHO에서 안전성 평가를 위해 설정한 PTWI와 PMTDI 기준과 비교한 결과 그 수준이 매우 낮아 우리나라에서 재배되는 이들 채소류로부터 섭취하는 중금속 양은 안전한 수준으로 판단되었다. 본 연구결과는 아직 세부기준이 설정되지 않는 채소류의 중금속 기준을 설정하는데 있어 참고 자료로 활용될 수 있을 것으로 기대된다. This study estimated the heavy metal contents of vegetables grown in Korea (n=234). The samples were digested using the microwave method. The contents of heavy metals (Pb, Cd, As, Cr, Cu, Mn, and Zn) were determined using inductively-coupled plasma spectrometry (ICP). The average values of heavy metals in vegetables were as follows [mean (minimum-maximum), ㎎/㎏)]; Pb 0.0026 (ND-0.0313), Cd 0.0017 (ND-0.0280), As 0.0005 (ND-0.0332), Cr 0.0156 (0.0010-0.1798), Cu 0.3767 (0.0556-1.3980), Mn 3.0214 (0.0182-26.4100), and Zn 3.5796 (0.8300-14.4600). The heavy metal contents of vegetables available on the domestic market were almost the same as or lower than those reported in other studies. Further, the weekly average intake of heavy metals was lower than the Provisional Tolerable Weekly Intake (PTWI) or the Provisional Maximum Tolerable Daily Intake (PMTDI), which was established by the FAO/WHO. Our results can be utilized as a reference to establish specific standards for various vegetables in Korea.
NeRF 기법에서 사용되는 UV Position Map 생성을 위한 Auto Encoder와 Variational Auto Encoder 비교에 관한 연구
김홍직(Hong-Jik Kim),이희열(Hee-Yeol Lee),라승탁(Seung-Tak Ra),김정윤(Jeong-Yoon Kim),오승진(Seung-Jin Oh),김기범(Gi-Beom Kim),유하영(Ha-Young Yoo),이태윤(Tae-Yoon Lee),오준혁(Jun-Hyeok Oh),이승호(Seung-Ho Lee) 대한전자공학회 2022 대한전자공학회 학술대회 Vol.2022 No.11
In this paper, Auto Encoder and Variational Auto Encoder were compared in generating UV Position Map, which is one of the important factors for 3D face reconstruction. Both models were trained from the same MNIST data, and as a result of training, the performance of Variational Auto Encoder was better. This seems to be the effect of the reparameterization trick that Auto Encoder does not have. Since the encoder extracts the mean and variance of the input data and uses them, the decoder knows the distribution information of the input data, so more sophisticated images can be created. Through this, by using the flow field of the continuous UV position map generated by VAE, it can be added as a new input to NeRF, and a novel view with more natural and various angles can be created than that of the existing NeRF.