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Lee, Jihong,Kim, Kyung Hwan,Lee, Ok Suk,Choi, Tae-Lim,Lee, Hee-Seung,Ihee, Hyotcherl,Sohn, Jeong-Hun American Chemical Society 2016 Journal of organic chemistry Vol.81 No.17
<P>As a guide for selective reactions toward either Z- or E-alkene in a metathesis reaction, the relative preference of metathesis Ru catalysts for each stereoisomer was determined by a method using time-dependent fluorescence quenching. We found that Ru-1 prefers the Z-isomer over the E-isomer, whereas Ru-2 prefers the E-isomer over the Z-isomer. The Z/E-alkene preference of the catalysts precisely predicted the Z/E isomeric selectivity in the metathesis reactions of diene substrates possessing combinations of Z/E-alkenes. For the diene substrates, the rate order of the reactions using Ru-1 was Z,Z-1,6-diene > Z,E-1,6-diene > E,E-1,6-diene, while the completely opposite order of E,E-1,6-diene > Z,E-1,6-diene > Z,Z-1,6-diene was exhibited in the case of Ru-2.</P>
Learning-based Localization of AUV with Outlier Sensor Data
Gwonsoo Lee,Phil-Yeob Lee,Ho Sung Kim,Hansol Lee,Jihong Lee 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
This paper describes localization of autonomous underwater vehicles(AUV) in case of some navigation sensor data are an outlier. In that situation, using existing navigation algorithms causes problems in long-range localization including integration. All the integration-based existing navigation algorithms can estimate exact position, only if the integration is performed under the assumption that the position of previous step is correct. Therefore, even if an outlier sensor data occurs in a short period of time, problems in localization will continue. Also, outlier sensor data related to heading (direction of AUV) causes bigger problems. In this work, we propose a localization method through learning that is used in a situation with outlier sensor data. To do so, a learning model is designed by fully connected model and is trained through partly contaminated real sea data. For Training, displacement between subsequent GPS data is taken as reference. As a result, average Euclidean error in incremental displacements of the existing navigation algorithm result and the reference is 0.45m. On the other hand, the proposed learning-based localization shows the average Euclidean error of 0.24m. This result doesn’t mean that the localization method through learning is accurate than the existing navigation algorithm. However, if there are outlier sensor data related to heading, we can conclude that using our proposed method shows more accurate localization.
Lee, Min-Hye,Lee, Seung-Hoon,Kim, Jihong,Lee, Sol Yi,Lim, Dae-Hee,Hwang, Kyoungtae,Hwang, Hansu,Jung, Yong Chae,Noh, Yong-Young,Kim, Dong-Yu Elsevier 2017 Carbon Vol.125 No.-
<P>The demand for high-purity semiconducting single-walled carbon nanotubes (sc-SWNTs) has increased considerably with the aim of utilizing their superior properties in a range of future applications. Among post-sorting methods, conjugated polymers have been regarded as one of the candidates to selectively isolate sc-SWNTs with uniform electrical properties. Herein, we demonstrate the ability to selectively sort two types of SWNTs by the two polymers PCTV18T and PC12TV18T, which have different alkyl side-chain densities. PC12TV18T, with a high alkyl chain density, shows great sorting ability for both high-pressure carbon monoxide and plasma-torch-grown SWNTs with a weight ratio of almost 1:1 in toluene solution. In addition, it is found that PC12TV18T selectively sorts sc-SWNTs with the high purity. The chirality and diameters of the enriched sc-SWNTs are further confirmed by Raman spectroscopy and photoluminescence excitation/emission mapping. Finally, we fabricate bottom gate/bottom contact thin-film transistors using the enriched sc-SWNTs as semiconductors to verify the electrical performance. Devices with well-percolated sc-SWNT networks displayed p-dominant properties with average charge-carrier mobilities of 2.05 cm(2) V-1 S-1 and 9.87 cm(2) V-1 S-1 and on/off current ratios of approximately 10(4) and 10(5) for high-pressure carbon monoxide and plasma-torch-grown SWNTs, respectively. (C) 2017 Elsevier Ltd. All rights reserved.</P>