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시맨틱 세그멘테이션 기반의 P&ID 심볼 탐지에 대한 연구
오상진(Sangjin Oh),이현(Hyun Lee),이정규(Jeongkyu Lee),이영환(Younghwan Lee),정은경(Eunkyung Jeong),이현식(Hyunsik Lee) (사)한국CDE학회 2020 한국CDE학회 논문집 Vol.25 No.4
Although the Fourth Industrial Revolution comes to reality rapidly, construction industry is going through a difficult time to adopt new technologies. Also, improving productivity is one of the most urgent issues for major construction companies. However, reading information and digitizing them from imaged drawings takes much time and it becomes a reason for low productivity. Thus, in this paper, we propose a method to recognize symbols in P&ID (piping and instrumentation diagram) using neural networks for Semantic Segmentation. First, crop a drawing into small patches and label on them 8 classes of symbol. Then, Train U-net and FCN with 2,500 patches with annotation. After training, results of recognition are displayed with color code on imaged drawings. Finally, we run tests with 5 new P&ID drawings and scored the performance of our recognition models.
차량 전장시스템의 개방형 소프트웨어 설계를 위한 개발 방법에 관한 연구
이상진(Sangjin Lee),손장경(Jangkyung Son),이성훈(Seonghun Lee),김명진(Myungjin Kim),이선봉(Seonbong Lee) 대한전자공학회 2007 대한전자공학회 학술대회 Vol.2007 No.11
AUTOSAR(AUTomotive Open System ARchitecture) is established an open standard for automotive electric/electronic architecture. It will serve as a basic infrastructure for the management of functions within both future applications and standard software modules. The software based on AUTOSAR standard has flexibility, scalability and improved quality. In this paper, we analyze the specifications of AUTOSAR authoring tools and present a development method for the tools.
Sukjin Choi,Sangjin Lee,Kideok Sim,Jeonwook Cho,Soogil Lee,Sang-Geun Lee,Kyu Won Lee,Sang Young Lee,Dong Ho Kim,Tae Kuk Ko IEEE 2010 IEEE transactions on applied superconductivity Vol.20 No.3
<P>In Korea, the superconducting power cable has been developed since 2001, with the basic specifications of 22.9 kV/50 MVA. The superconducting power cable can carry more than 2 to 5 times higher electric power compared with conventional ones. It is important to test the DC critical current related with its power capacity before applying to the real power grid. In 1995, several international standards organizations including IEC, decided to unify the use of statistical terms related with 'accuracy' or 'precision' in their standards. It was decided to use the word 'uncertainty' for all quantitative (associated with a number) statistical expressions. In this paper, we measured DC critical current of 22.9 kV/50 MVA superconducting power cable with several voltage tap and analyzed the uncertainty with these results. These analyzed results can be applied the standardization of the superconducting power cable.</P>
Lee, Beom Suk,Park, Kyeongsoon,Park, Sangjin,Kim, Gui Chul,Kim, Hyo Jung,Lee, Sangjoo,Kil, Heeseup,Oh, Seung Jun,Chi, Daeyoon,Kim, Kwangmeyung,Choi, KuiWon,Kwon, Ick Chan,Kim, Sang Yoon Elsevier 2010 Journal of controlled release Vol.147 No.2
<P><B>Abstract</B></P><P>The better understanding of polymeric nanoparticles as a drug delivery carrier is a decisive factor to get more efficient therapeutic response <I>in vivo</I>. Here, we report the non-invasive imaging of bare polymeric nanoparticles and drug-loaded polymeric nanoparticles to evaluate biodistribution in tumor bearing mice. To make nano-sized drug delivery carrier, glycol chitosan was modified with different degrees of hydrophobic N-acetyl histidine (NAcHis-GC-1, -2, and -3). The biodistribution of polymeric nanoparticles and drug was confirmed by using gamma camera with <SUP>131</SUP>I-labeled NAcHis-GC and <SUP>131</SUP>I-labeled doxorubicin (DOX) and by using <I>in vivo</I> live animal imaging with near-infrared fluorescence Cy5.5-labeled NAcHis-GC. Among bare nanoparticles, NAcHis-GC3 (7.8% NAcHis content) showed much higher tumor targeting efficiency than NAcHis-GC1 (3.3% NAcHis content) and NAcHis-GC2 (6.8% NAcHis content). In contrast, for drug-loaded nanoparticles, DOX-NAcHis-GC1 displayed two-fold higher tumor targeting property than DOX-NAcHis-GC3. These data imply that the biodistribution and tumor targeting efficiency between bare and drug-loaded nanoparticles may be greatly different. Therapeutic responses for NAcHis-GC nanoparticles after drug loading were also evaluated. In xenograft animal model, we could find out that DOX-NAcHis-GC1 with higher tumor targeting of DOX has more excellent therapeutic effect than DOX-NAcHis-GC3 and free DOX. These results mean that the hydrophobic core stability might be a critical factor for tumor targeting efficiency of nanoparticles. The present study indicates that by using molecular imaging, we can select more appropriate nanoparticles with the highest tumor targeting properties, leading to exerting more excellent therapeutic results in cancer therapy.</P> <P><B>Graphical Abstract</B></P><P><ce:figure id='f0035'></ce:figure></P>
Robust Interval Estimation Using Density Power Divergence
Sangjin Lee,Changkon Hong 한국자료분석학회 2022 한국자료분석학회 학술대회자료집 Vol.2021 No.2
It is well known that the maximum likelihood estimator (MLE) is asymptotically efficient but not robust with respect to both model misspecification and outliers. Basu et al. (1998) suggest a family of density-based divergence measures called `density power divergences . Each measure in this family is indexed by a single tuning parameter α, which controls the trade-off between robustness and asymptotic efficiency of the estimators. The Kullback-Leibler divergence (Kullback, Leibler, 1951) and 𝐿₂ -distance are members of this family. With a suitably chosen tuning parameter, a minimum density power divergence estimator (MDPDE) can be obtained. For 0<α<1, the estimator is in between MLE (efficient-but-nonrobust) and minimum 𝐿₂ -distance estimator 𝐿₂𝐸 (robust-but-inefficient). Hong, Kim(2001) suggest a data-driven selection of α. In this paper we will suggest a confidence interval using MDPDE when the data set is contaminated. Bootstrap resampling will be used to obtain the confidence interval. The resulting confidence intervals (called MDPD bootstrap confidence intervals) are expected to be robust with respect to the outliers. The performance of the MDPDE bootstrap confidence intervals are investigated via simulation study.