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Selective CO adsorption using sulfur-doped Ni supported by petroleum-based activated carbon
Sunil Kwon,Youngwoo You,Hyungseob Lim,Jinhee Lee,Tae-Sun Chang,Yejin Kim,Hyunjoo Lee,Beom-Sik Kim 한국공업화학회 2020 Journal of Industrial and Engineering Chemistry Vol.83 No.-
Carbon monoxide (CO) is an important platform compound that can be transformed into variousfinechemicals. However, CO manufactured by the conventional methods contains many other gases such ashydrogen, methane, nitrogen and carbon dioxide, and additional separation processes are required toutilize CO as a raw material. The current state-of-the-art techniques for separating CO have problems ofhigh energy consumptions. There are needs for an alternate separation process. In this work, wedeveloped a Ni-based adsorbent supported by petroleum-based activated carbon (PAC, BET > 1300 m2/g). The affinity and capacity for CO were evaluated by CO isotherm and CO temperature-programmeddesorption. The selectivity to CO was evaluated by the breakthrough test of multicomponent gases (10%H2, 10% CO, 1% CH4, and 1% CO2 with He balance). Ni/PAC was doped with sulfur to increase the COadsorption activity, and the sulfur-doped Ni/PAC (Ni/PACS) showed outstanding CO adsorption capacity,which is 9 times higher than that of the sulfur-free Ni/carbon adsorbent (Ni/PAC). The Ni/PACS couldrecover the CO to over 99% purity from the multicomponent gases. The sulfur-doped Ni/carbon adsorbentshowed high affinity, high capacity, and high selectivity for CO separation.
딥러닝 모델을 이용한 휴대용 무선 초음파 영상에서의 경동맥 내중막 두께 자동 분할 알고리즘 개발
최자영,김영재,유경민,장영우,정욱진,김광기,Choi, Ja-Young,Kim, Young Jae,You, Kyung Min,Jang, Albert Youngwoo,Chung, Wook-Jin,Kim, Kwang Gi 대한의용생체공학회 2021 의공학회지 Vol.42 No.3
Measuring Intima-media thickness (IMT) with ultrasound images can help early detection of coronary artery disease. As a result, numerous machine learning studies have been conducted to measure IMT. However, most of these studies require several steps of pre-treatment to extract the boundary, and some require manual intervention, so they are not suitable for on-site treatment in urgent situations. in this paper, we propose to use deep learning networks U-Net, Attention U-Net, and Pretrained U-Net to automatically segment the intima-media complex. This study also applied the HE, HS, and CLAHE preprocessing technique to wireless portable ultrasound diagnostic device images. As a result, The average dice coefficient of HE applied Models is 71% and CLAHE applied Models is 70%, while the HS applied Models have improved as 72% dice coefficient. Among them, Pretrained U-Net showed the highest performance with an average of 74%. When comparing this with the mean value of IMT measured by Conventional wired ultrasound equipment, the highest correlation coefficient value was shown in the HS applied pretrained U-Net.