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Lee, Sung-Hyun,Park, Junbeom,Kim, Hye-Rim,Lee, Taeseon,Lee, Jaegeun,Im, Yong-O.,Lee, Cheol-Hun,Cho, Hyunjung,Lee, Hyeseon,Jun, Chi-Hyuck,Ahn, Yu-Chan,Lee, In-Beum,Lee, Kun-Hong Elsevier 2016 Carbon Vol.100 No.-
<P>The optimum synthesis conditions for carbon nanotube (CNT) fibers were investigated using the Design of Experiment (DOE) technique. Direct spinning processes are governed by a variety of experimental factors: the methane flow rate, ferrocene flow rate, sulfur flow rate, hydrogen flow rate, water flow rate, and reaction temperature. The process was optimized in two stages that addressed first the Fractional Factorial Design (FFD) and then the Response Surface Methodology (RSM). Results from each experiment were classified according to a 6-step rating system: nothing(1), black gas(2), dust(3), ribbon or film(4), fiber(5), or continuous fiber(6). In the first step, three major factors (methane, sulfur, temperature) were identified as important among the six experimental factors tested using FFD. The effects of the major factors and the interactions were analyzed through the main effect plot and the interaction plot. In the second step, the experimental conditions were optimized using a model equation derived from Box-Behnken design experiments. Finally, the CNT fibers were continuously synthesized under the optimum conditions. The synthesized CNT fibers mainly consisted of single-walled CNTs (SWCNTs) 1.2 -3.8 nm in diameter. The I-G/I-D ratio of the CNT fibers was 48. This work provides a useful methodology for synthesizing the CNT fibers. (C) 2016 Elsevier Ltd. All rights reserved.</P>
Evaluation of Digital PCR as a Technique for Monitoring Acute Rejection in Kidney Transplantation
Lee, Hyeseon,Park, Young-Mi,We, Yu-Mee,Han, Duck Jong,Seo, Jung-Woo,Moon, Haena,Lee, Yu-Ho,Kim, Yang-Gyun,Moon, Ju-Young,Lee, Sang-Ho,Lee, Jong-Keuk Korea Genome Organization 2017 Genomics & informatics Vol.15 No.1
Early detection and proper management of kidney rejection are crucial for the long-term health of a transplant recipient. Recipients are normally monitored by serum creatinine measurement and sometimes with graft biopsies. Donor-derived cell-free deoxyribonucleic acid (cfDNA) in the recipient's plasma and/or urine may be a better indicator of acute rejection. We evaluated digital PCR (dPCR) as a system for monitoring graft status using single nucleotide polymorphism (SNP)-based detection of donor DNA in plasma or urine. We compared the detection abilities of the QX200, RainDrop, and QuantStudio 3D dPCR systems. The QX200 was the most accurate and sensitive. Plasma and/or urine samples were isolated from 34 kidney recipients at multiple time points after transplantation, and analyzed by dPCR using the QX200. We found that donor DNA was almost undetectable in plasma DNA samples, whereas a high percentage of donor DNA was measured in urine DNA samples, indicating that urine is a good source of cfDNA for patient monitoring. We found that at least 24% of the highly polymorphic SNPs used to identify individuals could also identify donor cfDNA in transplant patient samples. Our results further showed that autosomal, sex-specific, and mitochondrial SNPs were suitable markers for identifying donor cfDNA. Finally, we found that donor-derived cfDNA measurement by dPCR was not sufficient to predict a patient's clinical condition. Our results indicate that donor-derived cfDNA is not an accurate predictor of kidney status in kidney transplant patients.
배전 선로 부하예측 모델의 신뢰성 평가를 위한 비교 검증 시스템
Lee, Haesung,Lee, Byung-Sung,Moon, Sang-Keun,Kim, Junhyuk,Lee, Hyeseon 한국전력공사 2021 KEPCO Journal on electric power and energy Vol.7 No.1
Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.
Prediction of Hypertension Complications Risk Using Classification Techniques
Lee, Wonji,Lee, Junghye,Lee, Hyeseon,Jun, Chi-Hyuck,Park, Il-Su,Kang, Sung-Hong Korean Institute of Industrial Engineers 2014 Industrial Engineeering & Management Systems Vol.13 No.4
Chronic diseases including hypertension and its complications are major sources causing the national medical expenditures to increase. We aim to predict the risk of hypertension complications for hypertension patients, using the sample national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques, such as logistic regression, linear discriminant analysis, and classification and regression tree to predict the hypertension complication onset event for each patient. The performance of these three methods is compared in terms of accuracy, sensitivity and specificity. The result shows that these methods seem to perform similarly although the logistic regression performs marginally better than the others.
Haptic Assistance for Memorization of 2-D Selection Sequences
Hojin Lee,Gabjong Han,In Lee,Sunghoon Yim,Kyungpyo Hong,Hyeseon Lee,Seungmoon Choi IEEE 2013 IEEE transactions on human-machine systems Vol.43 No.6
<P>This paper investigates the effect of haptic feedback on the learning of a 2-D sequential selection task, used as an abstraction of complex industrial manual assembly tasks. This mnemonic-motor task requires memorization of the selection order of points scattered on a 2-D plane and reproduction of this order using entire arm movements. Four information presentation methods, visual information only, visual information + enactment, visual information + haptic guidance, and visual information + haptic disturbance, are considered. The latter three methods provide different levels of haptic kinesthetic feedback to the trainee. We carried out a user study to assess the quantitative performance differences of the four training methods using a custom-built visuo-haptic training system. Experimental results showed the relative advantages and disadvantages of each information presentation method for both short-term and long-term memorization. In particular, training with only visual information was the best option for short-term memory, while training also with haptic disturbance was the most effective for long-term memory. Our findings have implications to designing a training method that is suitable for given training requirements.</P>
Risk Prediction of Hypertension Complicates using Classification Techniques
Wonji Lee(이원지),Junghye Lee(이정혜),Hyeseon Lee(이혜선),Chi-Hyuck Jun(전치혁),Il-su Park 대한산업공학회 2014 대한산업공학회 춘계학술대회논문집 Vol.2014 No.5
A hypertension complications is one of the sources causing the national medical expenditures to increase. We aim to score the risk of hypertension complications for hypertension patients, using national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques such as logistic regression, linear discriminant analysis and classification and regression tree to score the risk of hypertension complication onset and also compare the performance of those methods. These three methods seem to perform similarly although the logistic regression performs better than others marginally. This study is meaningful in that the database used is a representative sample for the whole nation.