• Horticultural Science & Technology
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• v.31 no.3
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• pp.344-351
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• 2013

Microsatellite is one of the most suitable marker for cultivar identification as it has great discrimination power for cultivars with narrow genetic variation. The polymorphism level between 358 microsatellite primer pairs and 11 commercial cucumber cultivars was investigated. Thirty-one primer pairs showed high polymorphism within cucumber cultivars with different fruit types. These markers were applied for the constructing DNA profile data base of 110 commercial cucumber cultivars through multiplex PCR and fluorescence based automatic detection system. A total of 139 polymorphic amplified fragments were obtained by using 31 microsatellite markers. The average of PIC value was 0.610 ranging from 0.253 to 0.873. One hundred and thirty nine microsatellite loci were used to calculate Jaccard's distance coefficients for UPGMA cluster analysis. A clustering group of varieties, based on the results of microsatellite analysis, were categorized into plant shape and fruit type. Almost the cultivars were discriminated by marker genotypes. This information may be useful to compare through genetic relationship analysis between existing variety and candidate varieties in distinctive tests and protection of plant breeders' intellectual property rights through variety identification.

• Asia Marketing Journal
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• v.12 no.3
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• pp.1-24
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• 2010

Typically, marketing literature on innovation diffusion has focused on the pre-adoption process and only a few studies explicitly examined consumers' post-adoption behavior of innovative mobile services. Besides, prior use diffusion research has considered the variables that determine the consumers' initial adoption in explaining the post adoption usage behavior. However, behavioral sciences and individual psychology suggest that social influences are a potentially important determinant of usage behavior as well. The purpose of this study is to investigate into the effects of network factor and brand identification as social influences on the consumers' use diffusion or continued usage intention of a mobile service. Network factor designates consumer perception of the usefulness of a network, which embraces the concept of network externality and that of critical mass. Brand identification captures distinct aspects of social influence on technology acceptance that is not captured by subjective norm in situations where the technology use is voluntary. Additionally, this study explores the effect of the use diffusion on the brand switching intention, a generally unexplored form of post-adoption behavior. There are only a few empirical studies in the literature addressing the issue of IT user switching. In this study, the use diffusion comprises of rate of use and variety of use. The research hypotheses are as follows; H1. Network factor will have a positive influence on the rate of use of mobile services. H2. Network factor will have a positive influence on variety of use of mobile services. H3. Network factor will have a positive influence on continued usage intention. H4. Brand identification will have a positive influence on the rate of use. H5. Brand identification will have a positive influence on variety of use. H6. Brand identification will have a positive influence on continued usage intention. H7. Rate of use of mobile services are positively related to continued usage intention. H8. Variety of Use of mobile services are positively related to continued usage intention. H9. Rate of use of mobile services are negatively related to brand switching intention. H10. Variety of Use of mobile services are negatively related to brand switching intention. With the assistance of a marketing service company, a total of 1023 questionnaires from an online survey were collected. The survey was conducted only on those who have received or given a mobile service called "Gifticon". Those who answered insincerely were excluded from the analysis, so we had 936 observations available for a further stage of data analysis. We used structural equation modeling and overall fit was good enough (CFI=0.933, TLI=0.903, RMSEA=0.081). The results show that network factor and brand identification significantly increase the rate of use. But only brand identification increases variety of use. Also, network factor, brand identification and the use diffusion are positively related to continued usage intention. But the hypotheses that the use diffusion are positively related to brand switching intention were rejected. This result implies that continued usage intention cannot guarantee reducing brand switching intention.

• Journal of the Korean Society for Library and Information Science
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• v.37 no.3
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• pp.57-77
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• 2003

The objective of the study is to develop the identification code of the Korean sci-tech journals for KSCI. To achieve the purpose, the study has researched and analyzed a variety of the major international and national serials or information objects identification code systems. And then, KOJIC(KOrean Journal Identification Code) has been developed. KOJIC is unique, unambiguous identifiers for titles of Korean journals in all subject areas. The concepts of KOJIC are simplicity, mnemonics, internationalization, and extensibility of its use. KOJIC is a six-character and alphanumeric code and has one check character.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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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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• v.4 no.4
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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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• 2001.10a
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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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• v.15 no.4
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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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• v.33 no.2
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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.

• YAKHAK HOEJI
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• v.45 no.4
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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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• 1999.10a
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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.