• 원예과학기술지
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• 제31권3호
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• pp.344-351
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• 2013

국내에서 유통되고 있는 오이 110 품종을 대상으로 microsatellite 마커를 이용하여 DNA profile 데이터베이스를 구축하기 위하여 품종식별력이 높은 분자 마커의 선정 및 이를 활용한 품종간 유전적 유사도 검정 등에 대한 연구를 수행하였다. 오이 11 품종을 358개의 microsatellite 마커로 검정하여 31개의 다형성이 높은 마커를 선정한 다음 110품종에 대한 DNA profile 데이터베이스를 구축하였다. 오이 110품종을 31개의 microsatellite 마커로 분석하였을 때 대립유전자의 수는 2-9개로 비교적 다양한 분포를 나타내었으며 전체 139개의 대립유전자가 분석되었다. PIC 값은 0.253-0.873 범위에 속하였으며 평균값은 0.610으로 나타났다. Microsatellite 마커들의 대립유전자를 이용하여 계통도를 작성하였을 때 110 품종이 과실의 형태에 따라 그룹화되는 것을 확인하였으며, 대부분이 품종이 microsatellite 마커의 유전자형에 의해 식별이 되는 것으로 나타났다. 이 연구결과에 의해 개발된 오이 품종별 DNA profile 데이터베이스는 품종보호 출원 품종의 선 DNA 검정을 통한 대조품종 선정, 구별성, 균일성, 안정성 확인에 매우 유용하게 이용할 수 있어 향후, 품종보호권 강화 등에 크게 기여할 수 있을 것으로 사료된다.

• Asia Marketing Journal
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• 제12권3호
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• pp.1-24
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• 2010

본 연구는 혁신적인 모바일 서비스의 사용-확산 관점에서 혁신적인 서비스의 사용량과 사용의 다양성에 사회적 영향력이 어떠한 역할을 하는지 살펴보았다. 본 논문의 연구모형에 포함된 사회적 영향요인은 크게 두 가지인데 이는 네트워크 요인과 상표 동일시이다. 여기서 네트워크 요인은 사회적인 규범과 네트워크 효과를 포괄하는 개념이며, 상표 동일시는 소비자 개인의 자아정체성과 서비스 브랜드와의 일치성을 의미한다. 먼저 네트워크 요인의 경우 모바일 서비스의 사용량에 유의한 영향을 미치는 것으로 확인되었으며, 지속사용의도에도 긍정적인 영향을 미치는 것으로 나타났는데, 서비스 사용의 다양성에는 별 영향을 주지 않는 것으로 나타났다. 상표 동일시의 경우에는 사용량과 사용의 다양성에 모두 유의한 영향을 미치며 지속사용의도에도 긍정적인 효과를 가지는 것으로 나타났다. 본 연구에서 사용한 충성도 지표는 두 가지인데 하나는 모바일 서비스의 지속사용의도이고 또 하나는 상표애호도 즉 상표 전환의도이다. 이 두 가지 변수 중 전자는 서비스 카테고리에 대한 충성도이고 후자는 상표에 대한 충성도인데 서로 다른 의미를 갖는 만큼 연구 결과도 다르게 나왔다. 즉, 모바일 서비스의 사용량과 사용의 다양성은 모두 지속사용의도에 긍정적인 영향을 미쳤으나, 상표 전환의도에의 경우에는 사용량만이 유의한 영향을 주었고 가설과 반대로 사용량이 높을수록 오히려 상표 전환의도가 높아지는 것으로 나타났다. 이는 특정 혁신 서비스를 사용해 본 얼리어답터 고객들이 다양한 사업자의 서비스를 이용해 보고자 하는 니즈가 있음을 보여 준 것으로 이해할 수 있다.

• 한국문헌정보학회지
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• 제37권3호
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• pp.57-77
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• 2003

본 연구는 국내 학술지의 표준화된 학술지 식별코드 체계인 KOJIC(KOrean Journal Identification Code)를 개발하는 것이 목적이다. 이 시스템을 개발하기 위하여 전통적인 자료식별 번호체계, 주요 국가서지번호, 그리고 국제적 또는 국가적 자료식별코드체계의 구조를 조사 분석한 다음, 유일성, 간편성, 조기성, 국제성, 그리고 확정성을 갖춘 KOJIC을 개발하였다. 이것은 6 자리의 알파벳 영문자와 숫자로 구성되는 코드이며, 한 개의 체크기호를 포함하고 있다.

• International Journal of Control, Automation, and Systems
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• 제5권1호
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• pp.79-85
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• 2007

In this paper, a real-coded genetic algorithm is proposed for identification of time delay systems from step responses. FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) systems, which are the most useful processes in this field, but are difficult for system identification because of a long dead-time problem and a model mismatch problem. Genetic algorithms have been successfully applied to a variety of complex optimization problems where other techniques have often failed. Thus, the modified crossover operator of a real-code genetic algorithm is proposed to effectively search the system parameters. The proposed method, using a real-coding genetic algorithm, shows better performance characteristics when compared to the usual area-based identification method and the directed identification method that uses step responses.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.263-270
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• 2017

Various security threats exist in the smart grid environment due to the fact that information and communication technology are grafted onto an existing power grid. In particular, smart metering data exposes a variety of information such as users' life patterns and devices in use, and thereby serious infringement on personal information may occur. Therefore, we are in a situation where a de-identification algorithm suitable for metering data is required. Hence, this paper proposes a new de-identification method for metering data. The proposed method processes time information and numerical information as de-identification data, respectively, so that pattern information cannot be analyzed by the data. In addition, such a method has an advantage that a query such as a direct range search and aggregation processing in a database can be performed even in a de-identified state for statistical processing and availability.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.26.2-26
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• 2001

It is known that Volterra kernel model can represent a wide variety of nonlinear chemical processes. But almost all Volterra kernel models which appeared in the literature are up to second order, because it was difficult to measure higher order Volterra kernels. Kashiwagi has recently shown a method for measuring Volterra kernels up to third order using pseudorandom M-sequence signals. In this paper, the authors verified the applicability of this method for chemical processes using polymerization reactor simulation. Also, the authors have recently proposed a practical Identification method for chemical processes, which is based on the combination of off-line nonlinear identification and on-line linear identification. This method is also applied to the identification of polymerization reactor, and we obtained ...

• Smart Structures and Systems
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• 제15권4호
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• pp.1139-1158
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• 2015

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.

• Journal of Microbiology
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• 제33권2호
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• pp.95-102
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• 1995

A probability identification matrix of acidophilic Streptomyces was constructed. The phenetic data of the strains were derived from numerical classification described by Seong et al. The minimum number of diagnostic characters was determined using computer programs for calculation of different separation indices. The resulting matrix consisted of 25 clusters versus 53 characters. Theoretical evaluation of this matrix was achieved by estimating the chuster overlap and the identification scores for the Hypothetical Median Organisms (HMO) and for the representatives of each cluster. Cluster overlap was found to be relatively small. Identification scores for the HMO and the randomly selected representatives of each cluster were satisfactory. The matrix was assessed practically by applying the matrix to the identification of unknown isolates. Of the unknown isolates, 71.9% were clearly identified to one of eight clusters. The numerical classification data was also used to design a selective isolation medium for antibiotic-producing organisms. Four chemical substances including 2 antibiotics were determined by the DLACHAR program as diagnostic for the isolation of target organisms which have antimicrobial activity against Micrococcus luteus. It was possible to detect the increased rate of selective isolation on the synthesized medium. Theresults show that the numerical phenetic data can be applied to a variety of purposes, such as construction of identification matrix and selective isolation medium for acidophilic antinomycetes.

• 약학회지
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• 제45권4호
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• pp.426-430
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• 2001

Cells are eliminated in a variety of physiological settings by apoptosis, a genetically encoded process of cellular suicide. Bak, a member of the Bcl-2 protein family, accelerates apoptosis by an unknown mechanism. We have found a novel cDNA encoding a 101 amino acid protein possessing a Bak-like in our full-length cDNA bank. Bak-like shares the conserved domains BHI and 2 with other proapoptotic proteins but lacks the BH3 domain. Bak-like is expressed in a wide variety of tissues. Like Bak, Bak-like gene product primarily enhances apoptotic cell death following an appropriate stimulus.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.145-148
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• 1999

It is known that Volterra kernel models can represent a wide variety of nonlinear chemical processes. Also, it is necessary for Volterra model identification to excite the process to be identified with a signal having wide range of frequency spectrum and high enough amplitude of input signals. Kashiwagi[4 ∼ 7] has recently shown a method for measuring Volterra kernels up to third order using pseudorandom M-sequence signals. However, in practice, since it is not always possible to apply such input sequences to the actual chemical plants. Even when we can apply such a pseudorandom signal to the process, it takes much time to obtain higher order Volterra kernels. Considering these problems, the authors propose here a new method for practical identification of Volterra kernels by use of approximate open differential equation (ODE) model and simple plant test. Simulation results are shown for verifying the usefulness of our method of identification of nonlinear chemical processes.