• Korean Journal of Microbiology
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• v.35 no.1
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• pp.41-46
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• 1999

In detecting pathogeuic viruses in water sample, polymerase-chain-reaction (PCR) amplification was uscd. In order lo obtain the intact viral particlc, five concentration techniques were compared and an improved procedure was developed with some modifications. Among them, adsorption-elution~EG precipitation and flocculatio~~iultracentriEugation were more efficient than others with thc detection limit of 10 PFU $ml^{-1}$. By the additional step removing inhibitory compounds for PCR reaction, the purity of the concentrated sample was improved and the detection limit was lowered by one order (to 1 PFU $ml^{-1}$. To examine the availability of the optimized procedure for field surveys, the distributions of enterovirus in Han River were estimated using the novel procedure. Seventy-five percentage (618) of sewagc samples and twenty percentage (2110) of river water samples were positive for enterovirus. These results indicate that adsorption-elutionPEG precipitation by PCR method is useful for the prompt and handy monitoring of viral contaminaiton in water environment and pathogenic viruses are widely distributed in water environments of Seoul.

• Journal of Life Science
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• v.5 no.3
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• pp.105-116
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• 1995

To investigate genomic changes in c-myc gene by a chemical carcinogen, human lymphoblast NC-37 cells were exposed to benzo(a)pyrene(BP) and dimethylbenzanthracene(DMBA), and the c-myc gene expression was evaluated by Northern and Southern blot hybridization techniques. The results are as follows: When the genomic DNA of NC-37 cells exposed to several concentrations(1.25, 2.5 and 5ug/ml) of BP concentration. However, the c-myc gene was most significantly enhanced with 2.5ug/ml of BP. The expressions of c-myc gene in NC-37 cells was stimulated by BP and DMBA. Addition of TPA reduced the gene expression BP-treated cells, whereas it enhanced the gene expression in DMBA-treated cells. The expression of c-H-ras gene was slightly increased by treatment with BP and DMBA alone and in combination with TPA, however the magnitude of increase was not significantly different between each other. The expressions of c-myc c-H-ras genes in Burkitt's lymphoma cells were greater than those in NC-37 cells. When the DNA extracted from NC-37 cells exposed to various concentrations of BP were amplified by polymerase chain reaction using a primer set containing c-myc exon I, the amplified products were of the same size in all groups. To evaluate the BP toxicity in E.coli to which human c-myc gene-cloned pBR322 vector was inserted, Southern blot hybridization was conducted on c-myc genes digested with EcoRI/HindIII and Smal/Xbal restriction enzymes, and observing that in 2 ug/ml BP-treated cells a 3.5kb fragment was generated in addition to 1.3kb fragment which can be observed in normal cells. Direct nucleotide sequence analysis of polymerase chain reaction products showed a mutation of G$\longrightarrow$A transition at the Smal recognition site.

• Journal of Platform Technology
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• v.11 no.3
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• pp.45-55
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• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.823-830
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• 2022

The risk of project execution increases due to the enlargement and complexity of Engineering, Procurement, and Construction (EPC) plant projects. In the fourth industrial revolution era, there is an increasing need to utilize a large amount of data generated during project execution. The design is a key element for the success of the EPC plant project. Although the design cost is about 5% of the total EPC project cost, it is a critical process that affects the entire subsequent process, such as construction, installation, and operation & maintenance (O&M). This study aims to develop a system using machine-learning (ML) techniques to predict risks and support decision-making based on big data generated in an EPC project's design and construction stages. As a result, three main modules were developed: (M1) the design cost estimation module, (M2) the design error check module, and (M3) the change order forecasting module. M1 estimated design cost based on project data such as contract amount, construction period, total design cost, and man-hour (M/H). M2 and M3 are applications for predicting the severity of schedule delay and cost over-run due to design errors and change orders through unstructured text data extracted from engineering documents. A validation test was performed through a case study to verify the model applied to each module. It is expected to improve the risk response capability of EPC contractors in the design and construction stage through this study.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.61-66
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• 2024

This paper aims to design a simulation for path-finding of objects in a simulation or game environment using reinforcement learning techniques. The main feature of this study is that the objects in the simulation are trained to avoid obstacles at random locations generated on a given track and to automatically explore path to get items. To implement the simulation, ML Agents provided by Unity Game Engine were used, and a learning policy based on PPO (Proximal Policy Optimization) was established to form a reinforcement learning environment. Through the reinforcement learning-based simulation designed in this study, we were able to confirm that the object moves on the track by avoiding obstacles and exploring path to acquire items as it learns, by analyzing the simulation results and learning result graph.

• Journal of Microbiology and Biotechnology
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• v.27 no.3
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• pp.460-470
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• 2017

Mussels are major fouling organisms causing serious technical and economic problems. In this study, antifouling activity towards mussel was found in three compounds isolated from a marine bacterium associated with the sea anemone Haliplanella sp. This bacterial strain, called PE2, was identified as Vibrio alginolyticus using morphology, biochemical tests, and phylogenetic analysis based on sequences of 16S rRNA and four housekeeping genes (rpoD, gyrB, rctB, and toxR). Three small-molecule compounds (indole, 3-formylindole, and cyclo (Pro-Leu)) were purified from the ethyl acetate extract of V. alginolyticus PE2 using column chromatography techniques. They all significantly inhibited byssal thread production of the green mussel Perna viridis, with $EC_{50}$ values of $24.45{\mu}g/ml$ for indole, $50.07{\mu}g/ml$ for 3-formylindole, and $49.24{\mu}g/ml$ for cyclo (Pro-Leu). Previous research on the antifouling activity of metabolites from marine bacteria towards mussels is scarce. Indole, 3-formylindole and cyclo (Pro-Leu) also exhibited antifouling activity against settlement of the barnacle Balanus albicostatus ($EC_{50}$ values of 8.84, 0.43, and $11.35{\mu}g/ml$, respectively) and the marine bacterium Pseudomonas sp. ($EC_{50}$ values of 42.68, 69.68, and $39.05{\mu}g/ml$, respectively). These results suggested that the three compounds are potentially useful for environmentally friendly mussel control and/or the development of new antifouling additives that are effective against several biofoulers.

• Proceedings of the ESK Conference
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• 1994.04a
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• pp.157-157
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• 1994

원자력발전소 운영의 중추적 역할을 담당하고 있는 운전원과 발전소시스템 사이에서 발생하는 인간공학적 요인(인적요인)은 다중방호벽의 존재와 자동화 기술의 확대에도 불구하고 원전의 가동 성 및 안전성을 위협하는 최대의 요인이다. 최근 원자력발전소 시스템에 고도화된 전자공학 및 인공 지능기술 등이 반영되고 있는 추세이나 이러한 기술의 도입이 운전원과의 복합적 상호작용관점에서 원전의 안전성과 효율성에 적합한가를 실험적으로 평가할 수 있는 실험평가기술의 확보가 필요한 실정이다. 한국원자력연구소에서는 차세대 주제어실의 설계 및 평가를 위한 실험적 자료의 생성 및 설계 대안의 평가를 위한 기술확보라는 목적을 가지고 1992년도부터 수행하고 있다. 1992년도(1차년 도)에는 새로운 주제어실에서 실험적으로 평가해야 할 평가항목을 구체화하였고, 4년간의 연구추진 내용을 설정하였다. 기존의 원자력산업계에서 요구하고 있는 인가/허가 요건, 사업자요건서, 인간 공학분야에서 제기하고 있는 문제점 등을 분석하여 10개의 실험평가항목을 도출하였으며, 실험평가 항목을 실제로 실험을 통하여 연구하기 위한 장비 및 설비 그리고 소요기술 등을 고려하여 연구방향을 설정하였다. 1993년도(2차년도)에는 차세대 주제어실의 특징을 규명하고 실험연구의 대상시스템을 설정하였다. 설정된 대상시스템을 기능별로 분석하여 설계변수를 도출하였으며, 인간공학 실험실의 구축에 필수적인 원자력발전소 시뮬레이터의 기능요건 및 실험실의 구성요건 등을 개발하고 있다. 3차년도부터는 인간공학실험을 수행하면서 자료분석체계의 개발, 원전직무 시나리오의 개발, 측정방법의 개발, 인간공학 실험실의 설계, 구축 및 검증, 평가기법 연구, 실시간 자료수집체계의 개발 등을 수행할 예정이며, 연구종료시점인 1996년도(5차년도)에는 원자력발전소 주제어실의 인간공학적 평가를 위한 실험 환경의 구축 및 실험평가기술의 확립이라는 목표가 달성된다.하는 것으로 간주된다. 2. KR 53234 10mg/kg 정맥투여후의 최고혈중농도는 1.14ug/ml, 반감기는 0.50hr, 분포용적은 2.2L이었다. 20mg/kg 경구 투여시의 최소 혈중 농도는 0.33 ug/ml, 소실반감기는 1.5시간, AUC는 0.942ug.hr/ml, 분포용적 11L, Ka는 3.05 $hr^{－1}$ 그리고 Cl는 5.3L/hr/kg이었다. 이는 KR 53170에서와 같이 매우 적은량이 흡수되고 배설되었다. 3. KR 53170의 혈청단백 결합율은 5-500 ug/ml 범위에서 78.7-86.2%이었고 KR 53234의 혈청단백결합율은 5-100 ug/ml 범위에서 79.6-71.2%이었다.이었다.tic techniques, and we have recently cloned two of the major subunits; some of the data will be presented.LIFO, 우선 순위 방식등을 선택할 수 있도록 확장하였다. SIMPLE는 자료구조 및 프로그램이 공개되어 있으므로 프로그래머가 원하는 기능을 쉽게 추가할 수 있는 장점도 있다. 아울러 SMPLE에서 새로이 추가된 자료구조와 함수 및 설비제어 방식등을 활용하여 실제 중형급 시스템에 대한 시뮬레이션 구현과 시스템 분석의 예를 보인다._3$", chain segment, with the activation energy of carriers from the shallow trap with 0.4[eV], in he amorphous regions.의 증발산율은 우기의 기상자료를 이용하여 구한 결과 0.05 - 0.10 mm/hr 의 범위로서 이로 인한 강우손실량은 큰 의미가 없음을 알았다.재발이 나타난 3례의 환자를 제외한 9례 (75%)에서는 현재까지 재발소견을 보이지 않고 있다. 이러한 결과는 다른 보고자들과 유사한 결과를 보이고 있

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.615-626
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• 2024

To accurately and efficiently monitor soil moisture (SM) across South Korea, this study developed a SM estimation model that integrates the cloud computing platform Google Earth Engine (GEE) and Automated Machine Learning (AutoML). Various spatial information was utilized based on Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and the global precipitation observation satellite GPM (Global Precipitation Measurement) to test optimal input data combinations. The results indicated that GPM-based accumulated dry-days, 5-day antecedent average precipitation, NDVI (Normalized Difference Vegetation Index), the sum of LST (Land Surface Temperature) acquired during nighttime and daytime, soil properties (sand and clay content, bulk density), terrain data (elevation and slope), and seasonal classification had high feature importance. After setting the objective function (Determination of coefficient, R² ; Root Mean Square Error, RMSE; Mean Absolute Percent Error, MAPE) using AutoML for the combination of the aforementioned data, a comparative evaluation of machine learning techniques was conducted. The results revealed that tree-based models exhibited high performance, with Random Forest demonstrating the best performance (R² : 0.72, RMSE: 2.70 vol%, MAPE: 0.14).

• Journal of Distribution Science
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• v.13 no.9
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• pp.103-112
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• 2015

Purpose - This paper aims to provide a step-by-step approach to factor analytic procedures, such as principal component analysis (PCA) and exploratory factor analysis (EFA), and to offer a guideline for factor analysis. Authors have argued that the results of PCA and EFA are substantially similar. Additionally, they assert that PCA is a more appropriate technique for factor analysis because PCA produces easily interpreted results that are likely to be the basis of better decisions. For these reasons, many researchers have used PCA as a technique instead of EFA. However, these techniques are clearly different. PCA should be used for data reduction. On the other hand, EFA has been tailored to identify any underlying factor structure, a set of measured variables that cause the manifest variables to covary. Thus, it is needed for a guideline and for procedures to use in factor analysis. To date, however, these two techniques have been indiscriminately misused. Research design, data, and methodology - This research conducted a literature review. For this, we summarized the meaningful and consistent arguments and drew up guidelines and suggested procedures for rigorous EFA. Results - PCA can be used instead of common factor analysis when all measured variables have high communality. However, common factor analysis is recommended for EFA. First, researchers should evaluate the sample size and check for sampling adequacy before conducting factor analysis. If these conditions are not satisfied, then the next steps cannot be followed. Sample size must be at least 100 with communality above 0.5 and a minimum subject to item ratio of at least 5:1, with a minimum of five items in EFA. Next, Bartlett's sphericity test and the Kaiser-Mayer-Olkin (KMO) measure should be assessed for sampling adequacy. The chi-square value for Bartlett's test should be significant. In addition, a KMO of more than 0.8 is recommended. The next step is to conduct a factor analysis. The analysis is composed of three stages. The first stage determines a rotation technique. Generally, ML or PAF will suggest to researchers the best results. Selection of one of the two techniques heavily hinges on data normality. ML requires normally distributed data; on the other hand, PAF does not. The second step is associated with determining the number of factors to retain in the EFA. The best way to determine the number of factors to retain is to apply three methods including eigenvalues greater than 1.0, the scree plot test, and the variance extracted. The last step is to select one of two rotation methods: orthogonal or oblique. If the research suggests some variables that are correlated to each other, then the oblique method should be selected for factor rotation because the method assumes all factors are correlated in the research. If not, the orthogonal method is possible for factor rotation. Conclusions - Recommendations are offered for the best factor analytic practice for empirical research.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9C
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• pp.676-682
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• 2008

With the coming of information times, digital contents are widely applied in various fields of the society. Along with this, scholars provided many techniques and research approaches related to digital contents management and copyright protection. But till now, most proposed techniques and approaches are based on single system. The independency and separation among management systems causes the problems such as application across systems are limited, and resources sharing can't be available, etc. In this paper, for the application cross systems and contents sharing, we propose the cross-systems DRM approach based on unified metadata. This approach can be applied for the incorporation of multiple congeneric or heterogeneous CMSs, in the same time, solve the copyright problem cross digital content systems.