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Pilsung Kang,Sung Jin Kim,Ha Ju Park,Il Chan Kim,Se Jong Han,Joung Han Yim The Korean Society for Microbiology and Biotechnol 2024 Journal of microbiology and biotechnology Vol.34 No.5
When cells are exposed to freezing temperatures, high concentrations of cryoprotective agents (CPA) prevent ice crystal formation, thus enhancing cell survival. However, high concentrations of CPAs can also cause cell toxicity. Exopolysaccharides (EPSs) from polar marine environments exhibit lower toxicity and display effects similar to traditional CPA. In this study, we sought to address these issues by i) selecting strains that produce EPS with novel cryoprotective activity, and ii) optimizing culture conditions for EPS production. Sixty-six bacteria producing mucous substances were isolated from the Ross Sea (Antarctic Ocean) using solid marine agar plates. Among them, Pseudoalteromonas sp. RosPo-2 was ultimately selected based on the rheological properties of the produced EPS (p-CY02). Cryoprotective activity experiments demonstrated that p-CY02 exhibited significantly cryoprotective activity at a concentration of 0.8% (w/v) on mammalian cells (HaCaT). This activity was further improved when combined with various concentrations of dimethyl sulfoxide (DMSO) compared to using DMSO alone. Moreover, the survival rate of HaCaT cells treated with 5% (v/v) DMSO and 0.8% (w/v) p-CY02 was measured at 87.9 ± 2.8% after freezing treatment. This suggests that p-CY02 may be developed as a more effective, less toxic, and novel non-permeating CPA. To enhance the production of EPS with cryoprotective activity, Response Surface Methodology (RSM) was implemented, resulting in a 1.64-fold increase in production of EPS with cryoprotective activity.
Locally linear ensemble for regression
Kang, Seokho,Kang, Pilsung Elsevier science 2018 Information sciences Vol.432 No.-
<P><B>Abstract</B></P> <P>Considerable research effort has been dedicated to the development of prediction models for yielding greater prediction accuracy in regression problems. Although non-linear models have achieved superior prediction accuracy by addressing the non-linearity of complex data, linear models are still favored because of their high prediction speed. In this study, a locally linear ensemble regression (LLER) is proposed in order to effectively address non-linearity while maintaining the advantage of linear models. The LLER predicts new instances based on multiple linear models that are trained on the regions that identify the local linearity of data. To achieve this, data are decomposed into several locally linear regions based on an expectation-maximization procedure, and linear models are built as local experts for each region to constitute an ensemble. We demonstrate the effectiveness of the LLER through experimental validation with benchmark datasets.</P>
Kang, Pilsung,Kim, Dongil,Cho, Sungzoon Elsevier 2016 expert systems with applications Vol.51 No.-
<P><B>Abstract</B></P> <P>Dataset size continues to increase and data are being collected from numerous applications. Because collecting labeled data is expensive and time consuming, the amount of unlabeled data is increasing. Semi-supervised learning (SSL) has been proposed to improve conventional supervised learning methods by training from both unlabeled and labeled data. In contrast to classification problems, the estimation of labels for unlabeled data presents added uncertainty for regression problems. In this paper, a semi-supervised support vector regression (SS-SVR) method based on self-training is proposed. The proposed method addresses the uncertainty of the estimated labels for unlabeled data. To measure labeling uncertainty, the label distribution of the unlabeled data is estimated with two probabilistic local reconstruction (PLR) models. Then, the training data are generated by oversampling from the unlabeled data and their estimated label distribution. The sampling rate is different based on uncertainty. Finally, expected margin-based pattern selection (EMPS) is employed to reduce training complexity. We verify the proposed method with 30 regression datasets and a real-world problem: virtual metrology (VM) in semiconductor manufacturing. The experiment results show that the proposed method improves the accuracy by 8% compared with conventional supervised SVR, and the training time for the proposed method is 20% shorter than that of the benchmark methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A new semi-supervised support vector regression method is proposed. </LI> <LI> Label distribution is estimated by probabilistic local reconstruction algorithm. </LI> <LI> Different oversampling rate is used based on uncertainty information. </LI> <LI> Expected margin based pattern selection is used to reduce the training complexity. </LI> <LI> The proposed method improves the prediction performance with lower time complexity. </LI> </UL> </P>
강필성(Pilsung Kang),금영정(Youngjung Geum),박현우(Hyun-woo Park),김상국(Sang-gook Kim),성태응(Tae-eung Sung),이학연(Hakyeon Lee) 대한산업공학회 2015 대한산업공학회지 Vol.41 No.2
This paper proposes a new approach to technology valuation, the market-replacement cost approach which integrates the cost-based approach and market-based approach. The proposed approach estimates the market-replacement cost of a target technology using R&D costs of similar R&D projects previously conducted. Similar R&D projects are extracted from project database based on document similarity between project proposals and technology description of the target technology. R&D costs of similar R&D projects are adjusted by mirroring the rate of technological obsolescence and inflation. Market-replacement cost of the technology is then derived by calculating the weighted average of adjusted costs and similarity values of similar R&D projects. A case of “Prevention method and system for the diffusion of mobile malicious code” is presented to illustrate the proposed approach.
강필성(Pilsung Kang),이형주(Hyoung-joo Lee),조성준(Sungzoon Cho) 한국정보과학회 2004 한국정보과학회 학술발표논문집 Vol.31 No.2Ⅱ
대부분의 기계학습 알고리즘은 학습 데이터에서 각각의 범주간의 비율이 동일하거나 비슷하다는 가정 하에 문제를 풀게 된다. 그러나 실제 문제에서는 그 비율이 동일하지 않으며 매우 큰 차이를 보이기도 하는데 이는 분류 성능을 저하시키는 요인이기도 하다. 따라서 본 논문에서는 이러한 데이터의 불균형 문제를 해소하는 방안으로 SVM 앙상블 기법을 적용한 샘플링을 제안하고 이를 실제 불균형 데이터에 적용함으로써 제안된 방법이 기존의 방법들에 비해 향상된 성능을 나타내는 것을 보였다.
강필성(Pilsung Kang),박영준(Youngjoon Park),조수곤(Sugon Cho),김성범(Seoung Bum Kim) 대한산업공학회 2014 대한산업공학회지 Vol.40 No.1
This paper aims to propose a research framework of analyzing voting activities of a national assembly on the basis of member-level voting similarity and provides a case study in the 18<SUP>th</SUP> national assembly in South Korea. First, we propose a bill contentiousness measure that gives a higher score to bills for which ayes and noes are more diversified in both conservative and progressive parties. Based on the bill contentiousness measure, the top 5%, 10%, and 20% bills were identified and used for further analyses. Moreover, we propose a member-level voting similarity measure that compensates for the lower frequency of noes, and evaluate the pair-wise voting similarities for all lawmakers. Then, voting similarity differences to the affiliated/non-affiliated parties were analyzed for the members in the two major parties according to some internal/external key factors. Finally, similar voting groups were identified and their affiliations were investigated based on the multi-dimensional scaling (MDS) and network analysis techniques. A case study on the 18<SUP>th</SUP> national assembly of South Korea showed that the cohesion of the members in the ‘Hanara’ party becomes higher than that of the ‘Minju’ party as the bill contentiousness increases, whereas the number of elected, local constituency versus proportional representation, and the competition intensity in a local constituency were found to be partially influential to the voting activities of lawmakers. In addition, MDS and network analysis showed that there is a distinctive difference between two parties when all bills are analyzed, whereas the diversity of parties increases in the same group as the bill contentiousness increases.
이상치 탐지 방법론을 활용한 반도체 가상 계측 결과의 신뢰도 추정
강필성(Pilsung Kang),김동일(Dongil Kim),이승경(Seung-kyung Lee),도승용(Seungyong Doh),조성준(Sungzoon Cho) 대한산업공학회 2012 대한산업공학회지 Vol.38 No.1
The purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer’s metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.