• 산업경영시스템학회지
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• 제45권4호
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• pp.53-60
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• 2022

In a group-testing method, instead of testing a sample, for example, blood individually, a batch of samples are pooled and tested simultaneously. If the pooled test is positive (or defective), each sample is tested individually. However, if negative (or good), the test is terminated at one pooled test because all samples in the batch are negative. This paper considers a queueing system with a two-stage group-testing policy. Samples arrive at the system according to a Poisson process. The system has a single server which starts a two-stage group test in a batch whenever the number of samples in the system reaches exactly a predetermined size. In the first stage, samples are pooled and tested simultaneously. If the pooled test is negative, the test is terminated. However, if positive, the samples are divided into two equally sized subgroups and each subgroup is applied to a group test in the second stage, respectively. The server performs pooled tests and individual tests sequentially. The testing time of a sample and a batch follow general distributions, respectively. In this paper, we derive the steady-state probability generating function of the system size at an arbitrary time, applying a bulk queuing model. In addition, we present queuing performance metrics such as the offered load, output rate, allowable input rate, and mean waiting time. In numerical examples with various prevalence rates, we show that the second-stage group-testing system can be more efficient than a one-stage group-testing system or an individual-testing system in terms of the allowable input rates and the waiting time. The two-stage group-testing system considered in this paper is very simple, so it is expected to be applicable in the field of COVID-19.

• 산업경영시스템학회지
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• 제45권4호
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• 한국가구학회지
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• 제23권2호
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• pp.110-121
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• 2012

With people's recent increasing interest in good design products, wood and metal products have gained great popularity. However, it was believed that it would be necessary to have a transformation to the manufacturing method based on digital data and equipments from existing analog-based manufacturing method, in order to meet consumers' demand. This study was aimed to seek for the possibility of mass-producing wood and metal products through the research on the type, usage and development conditions of digital data and the methods of utilizing digital equipments. As for research methods, the study analyzed the concepts and types of digital data through various internet and literature reviews and suggested perpetual calendar products as the final outcome of design development using computer data. Through this, the study summarized and organized actual design development processes by stage to provide basic data that could become the foundation of research on the design of wood and metal products using digital data. Through the outcome of this project, the following effects could be expected by developing wood and metal products through digital data. First, its accurate and precise process would help mass-produce complex forms of products and reduce their defective rate. Second, the compatible production of various types of digital equipments would lead to a cost reduction. Third, the diversity of design could be pursued by overcoming technical limitations. In order to satisfy the above expectation effects, such as realization of developing and producing various wood and metal products, there should be designers' creative experimental spirits, their active information exchange and cooperation with the companies concerned.

• 한국전기전자재료학회논문지
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• 제20권1호
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• pp.57-64
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• 2007

One of the cause of insulation failure in power cable is well known by electrical treeing discharge. This is occurred for imposed continuous stress at cable. And this event is related to safety, reliability and maintenance. In this paper, throughout analysis of partial discharge(PD) distribution when occurring the electrical tree, is studied for the purpose of knowing of electrical treeing discharge characteristics according to defects. Own characteristic of tree will be differently processed in each defect and this reason is the first purpose of this paper. To acquire PD data, three defective tree models were made. And their own data is shown by the phase-resolved partial discharge method (PRPD). As a result of PRPD, tree discharge sources have their own characteristics. And if other defects (void, metal particle) exist internal power cable then their characteristics are shown very different. This result Is related to the time of breakdown and this is importance of cable diagnosis. And classification method of PD sources was studied in this paper. It needs select the most useful method to apply PD data classification one of the proposed method. To meet the requirement, we select methods of different type. That is, neural network(NN-BP), adaptive neuro-fuzzy inference system and PCA-LDA were applied to result. As a result of, ANFIS shows the highest rate which value is 98 %. Generally, PCA-LDA and ANFIS are better than BP. Finally, we performed classification of tree progress using ANFIS and that result is 92 %.

• Journal of Applied Biological Chemistry
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• 제51권3호
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• pp.102-110
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• 2008

Complementation assay of the urea uptake-defective yeast mutants led to the identification of the Arabidopsis AtTIP4;1 gene encoding the aquaporin. However, its physiological functions still remain elusive. In the present study, histochemical and genetic analyses were performed to understand the physiological roles of AtTIP4;1 in urea uptake. The AtTIP4;1 product was detectible in the roots, but not in the leaves, the stem, and the flower. Its promoter allowed the expression of the $\beta$-glucuronidase reporter gene in the roots and the apical meristem in Arabidopsis. The AtTIP4;1 products were induced under nitrogen-deficient conditions. To investigate the role of the tonoplast intrinsic protein in urea transport and developments, Arabidopsis with the loss- and the gain-of-function mutations by T-DNA insertion in AtTIP4;1 and 35S promoter-mediated overexpression of AtTIP4;1 were identified, respectively. The transfer DNA insertion and the AtTIP4;1-overexpressed plants showed normal growth and development under normal or abiotic stress growth conditions. The urea-uptake studies using $^{14}C$-labeled urea revealed higher accumulation of urea in the AtTIP4;1-overexpressed plants. These results provide evidence that overexpression of AtTIP4;1 leads to the increase in the urea-uptake rate in plants without detectable defects to the growth and development.

• 한국가스학회지
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• 제12권4호
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• pp.1-7
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• 2008

최근 5년간 국내 가스사고 중 CO중독사고는 사망 36명, 부상 79명이 발생하였으며, 전체 가스 사고에 비해 인명피해율이 8배나 높게 나타나고 있다. 이러한 CO중독사고의 대부분이 노후 가스보일러 및 다중 가스 사용시설의 배기통 불량에 의한 가스누출사고로 조사되고 있다. 본 연구에서는 실험을 통해 건축물 내 보일러실의 배기통 불량에 의한 CO가스의 누출확산 메커니즘을 해석하였다. 배기통에서 누출된 CO 가스는 건축물 내부의 천정부근에서 가장 높은 농도분포를 보인다. 또한 CO경보기 설치 실험을 통해서 천정부근의 CO경보기가 가장먼저 작동하고, $30{\sim}40$분 경과한 후에 바닥 및 중간에 설치된 경보기가 작동하는 것을 확인하였다. 본 연구를 통해 CO경보기의 적정 설치위치를 확인하였으며, 이 결과를 토대로 건출물내 CO경보기 설치위치를 제안하였다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권6호
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• pp.2447-2451
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• 2014

Human papillomavirus (HPV) is the primary etiologic agent of cervical cancer. Consideration of safety and non human leukocyte antigen restriction, protein vaccine has become the most likely form of HPV therapeutic vaccine, although none have so far been reported as effective. Since tumor cells consistently express the two proteins E6 and E7, most therapeutic vaccines target one or both of them. In this study, we fabricated DC vaccines by transducing replication-defective recombinant adenoviruses expressing E6/E7 fusion gene of HPV-16, to investigate the lethal effects of specific cytotoxic T lymphocytes (CTL) against CaSki cells in vitro. Mouse immature dendritic cells (DC) were generated from bone marrow, and transfected with pAd-E6/E7 to prepare a DC vaccine and to induce specific CTL. The surface expression of CD40, CD68, MHC II and CD11c was assessed by flow cytometry (FCM), and the lethal effects of CTL against CaSki cells were determined by DAPI, FCM and CCK-8 methods. Immature mouse DC was successfully transfected by pAd-E6/E7 in vitro, and the transfecting efficiency was 40%-50%. A DC vaccine was successfully prepared and was used to induce specific CTL. Experimental results showed that the percentage of apoptosis and killing rate of CaSki cells were significantly increased by coculturing with the specific CTL (p <0.05). These results illustrated that a DC vaccine modified by HPV-16 E6/E7 gene can induce apoptosis of CaSki cells by inducing CTL, which may be used as a new strategy for biological treatment of cervical cancer.

• 한국컴퓨터정보학회논문지
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• 제25권11호
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• pp.115-122
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• 2020

본 논문에서는 실시간 공정능력관리 기반의 MES(Manufacturing Execution System) 시스템과 해당 시스템을 활용한 제조 업무 수행 절차에 대해서 제안한다. 현 MES 시스템은 제조 현장을 쉽게 인지할 수 있는 KPI(Key Performance Indicator) 리포트는 제시하지만, 실질적 제조 현장의 KPI 향상을 위한 개선 방안은 도출하지 못하고 있다. 즉, 불량률 증가, 수율 감소, 생산 리드 타임(Lead-Time) 증가의 궁극적 원인을 발췌하여 개선 방안을 도출하고, 이를 제조 현장에 적용하는 부분에는 어려움이 있다. 본 논문에서 제안하는 실시간 공정능력관리 기반의 MES 시스템은 공정능력지수를 결정하는 설비 주요 인자의 산포도를 실시간으로 관리하여, 기존 MES 시스템의 한계점을 극복한 제조 운영 관리시스템 구축이 목적이다. 그리고, 실시간 대용량 데이터 처리를 위한 속도개선 방안을 제시하여, 해당 시스템이 잘 운영될 수 있도록 적용하려고 한다.

• 미생물학회지
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• 제52권3호
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• pp.298-305
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• 2016

본 연구에서는 p-cresol 초기 분해에 관여하는 기존의 lap과 pcu 유전자군 외에 새로운 pch 유전자군을 Pseudomonas alkylphenolica KL28로 부터 동정하였다. 이 유전자군(pchACXF-pcaHG-orf4-pcaBC)은 p-cresol을 ${\beta}$-carboxy-cis,cis-muconate로의 전환을 촉매할 수 있는 효소를 암호화하는 것을 알 수 있었다. 이 유전자 군은 영국에서 분리된 Pseudomonas putida NCIMB 9866과 9869의 plasmid에서 유래된 pch 유전자 군과 동일하여, 이들 유전자군은 종간 horizontal gene transfer로 전달되었을 가능성을 제시하였다. 각 유전자군의 관련 유전자의 변이와 gfp 레포터를 갖는 프로모터의 발현 분석을 통해 3개의 분해 유전자군이 모두 p-cresol의 분해에 관여하는 것을 알 수 있었으며, pch 유전자는 p-cresol에 의해 유도되며, 고체 및 액체 배지에서도 pcu 유전자군이 가장 높게 발현되는 것을 확인할 수 있었다. 또한 pcu 유전자 변이주는 p-cresol을 이용하여 버섯모양의 공중체(aerial structure) 형성하지 않았으므로, 탄소원의 이용 속도가 다세포 구조 형성에 영향을 주는 중요한 요소 중의 하나임을 알 수 있었다.

• 산업경영시스템학회지
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• 제40권2호
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.