• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.157-165
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• 2002

This paper describes our ongoing Korean-Chinese machine translation system, which is based on verb patterns. A verb pattern consists of a source language pattern part for analysis and a target language pattern part for generation. Knowledge description on lexical level makes it easy to achieve accurate analyses and natural, correct generation. These features are very important and effective in machine translation between languages with quite different linguistic structures including Korean and Chinese. We performed a preliminary evaluation of our current system and reported the result in the paper.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.375-381
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• 2007

Cognitive map (CM) has long been used as an effective way of constructing the human thinking process. In literature regarding CM, a number of successful researches were reported, where CM based what-if analysis could enhance firm's performance. However, there exit very few researches investigating the CM generation method. Therefore this study proposes a GENCOM (Genetic Algorithm based Cognitive Map Generator). In this model combined with CM and GA, GA will find the optimal weight and input vector so that the CM generation. To empirically prove the effectiveness of GENCOM, we collected valid questionnaires from expert in S/W sales cases. Empirical results showed that GENCOM could contribute to effective CM simulation and very useful method to extracting the tacit knowledge of sales experts.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.2
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• pp.158-165
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• 1996

The knowledge of air flow in an engine room has become more and more important in recent car design. The fluid flow in the engine compartment was investigated by numerical analysis. Due to the complex geometry of the engine compartment, mesh generation is a time-consuming job. In this research, the "ICEM" code was used to generate meshes by the Cartesian mesh model. The Reynolds-averaged Navier Stokes equations, together with the porous flow model for radiator and condenser, were solved. Computation was performed for the steady, incompressible, and high speed viscous flow, adopting the standard K-ε turbulence model. The "STAR-CD" code was used as a solver. The effect of car front openning area on the flow in engine room was also investigated.

• Structural Engineering and Mechanics
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• v.28 no.2
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• pp.153-166
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• 2008

This paper suggests the use of wavelet multiresolution analysis (WMRA) and neural network for generation of artificial earthquake accelerograms from target spectrum. This procedure uses the learning capabilities of radial basis function (RBF) neural network to expand the knowledge of the inverse mapping from response spectrum to earthquake accelerogram. In the first step, WMRA is used to decompose earthquake accelerograms to several levels that each level covers a special range of frequencies, and then for every level a RBF neural network is trained to learn to relate the response spectrum to wavelet coefficients. Finally the generated accelerogram using inverse discrete wavelet transform is obtained. An example is presented to demonstrate the effectiveness of the method.

• Vacuum Magazine
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• v.1 no.1
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• pp.21-26
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• 2014

The new materials such as graphene and other nano scale structured materials are attracting great attention due to its expandability for the future electronic devices. In this presentation, a variety of analysis techniques will be introduced for the latest new material applications such as graphene and organic materials with number of metals. The basic properties of next generation device should be carefully analyzed without being exposed to ambient surrounding since the physical and chemical properties of new material or interface states are easily and drastically changed by ambient condition. With the combination of the fabrication process and precise analysis instruments, it is expected to set the facilities supporting the nanotechnology industry and other research groups. This system will give strong support nanotechnology and other complex science with qualified data and information on basic knowledge on the new-forthcoming materials for the future.

• Journal of Ocean Engineering and Technology
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• v.12 no.3 s.29
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• pp.103-111
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• 1998

This paper describes the initial hull form design process which generate a hull form using a neurofuzzy modeling. Neurofuzzy system is to combine the merits of fuzzy inference system and neural networks. Therefore it has structured knowledge representations as well as adaptive capacities. Initial hull form design stage is the process which generate an adoptable hull form from the limited design information and multi-decidions condidering correlations with design factors. It can be assidted efficiently by neurofuzzy system. This paper suggests two methods of an initial hull form generation using the neurofuzzy modeling and B-spline theory. and examines the usefulness of suggested method through its application examples.

• Journal of Radiation Protection and Research
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• v.45 no.3
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• pp.108-116
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• 2020

Background: This foundational study on educational interventions aimed to analyze the changes in awareness, knowledge, and attitudes of young learners after they received objective information on safety management. Materials and Methods: Educational sessions on nuclear power and radiation safety were delivered to 4,934 Korean elementary, middle, and high school students in two separate sessions conducted in 2016 and 2017. The effects of these interventions were subsequently analyzed. Results and Discussion: Learner attitudes toward safety were found to be the predominant variables affecting the post-intervention risk (safety) awareness of nuclear power generation. Conclusion: The safety awareness of future generations will significantly influence policy decisions on nuclear power generation. Hence, the design of educational interventions on this subject must match variables suited to learner levels.

• ALGAE
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• v.31 no.2
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• pp.137-154
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• 2016

Next generation sequencing (NGS) technologies have revolutionized many areas of biological research due to the sharp reduction in costs that has led to the generation of massive amounts of sequence information. Analysis of large genome data sets is however still a challenging task because it often requires significant computer resources and knowledge of bioinformatics. Here, we provide a guide for an uninitiated who wish to analyze high-throughput NGS data. We focus specifically on the analysis of organelle genome and metagenome data and describe the current bioinformatic pipelines suited for this purpose.

• BMB Reports
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• v.37 no.6
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• pp.635-641
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• 2004

Interferons regulate a number of biological functions including control of cell proliferation, generation of antiviral activities and immumodulation in human cells. Studies by several investigators have identified a number of cellular signaling cascades that are activated during engagement of interferon receptors. The activation of multiple signaling cascades by the interferon receptors appears to be critical for the generation of interferon mediated biological functions and immune surveillance. The present review summarizes the existing knowledge on the multiple signaling cascades activated by Type I interferons. Recent developments in this research area are emphasized and the implications of these new discoveries on our understanding of interferon actions are discussed.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.521-524
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• 2002

This paper deals with the rule generation from data for control system and data mining using rough set. If the cores and reducts are searched for without consideration of the frequency of data belonging to the same equivalent class, the unnecessary attributes may not be discarded, and the resultant rules don't represent well the characteristics of the data. To improve this, we handle the inconsistent data with a probability measure defined by support, As a result the effect of uncertainty in knowledge reduction can be reduced to some extent. Also we construct the rule base in a hierarchical structure by applying core as the classification criteria at each level. If more than one core exist, the coverage degree is used to select an appropriate one among then to increase the classification rate. The proposed method gives more proper and effective rule base in compatibility and size. For some data mining example the simulations are performed to show the effectiveness of the proposed method.