• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.38-51
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• 2007

This paper describes research results for the measurement and analysis method of magnetic signatures generated from the ship's magnetic mock-up model. In this paper, we present the theoretical and experimental techniques for the separation of the permanent and the induced magnetic field from the measured magnetic signature of the mock-up model. Also, we describe the prediction method of the induced magnetic field generated from mock-up model using the Magnet s/w, one of the FEM analysis tools for the electro-magnetic field and the magnetic dipole modelling method based on the least square techniques. The proposed modelling and analysis methods can be used for the prediction and the analysis of the static magnetic field generated from the real naval ship as well as the mock-up model.

• International Journal of Industrial Entomology and Biomaterials
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• v.10 no.2
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• pp.79-86
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• 2005

Molecular markers have increasingly been used in plant genetic analysis, due to their obvious advantages over conventional phenotypic markers, as they are highly polymorphic, more in number, stable across different developmental stages, neutral to selection and least influenced by environmental factors. Among the PCR based marker techniques, ISSR is one of the simplest and widely used techniques, which involves amplification of DNA segment present at an amplifiable distance in between two identical microsatellite repeat regions oriented in opposite direction. Though ISSR markers are dominant like RAPD, they are more stable and reproducible. Because of these properties ISSR markers have recently been found using extensively for finger printing, pohylogenetic analysis, population structure analysis, varietal/line identification, genetic mapping, marker-assisted selection, etc. In mulberry (Morus spp.), ISSR markers were used for analyzing phylogenetic relationship among cultivated varieties, between tropical and temperate mulberry, for solving the vexed problem of identifying taxonomic positions of genotypes, for identifying markers associated with leaf yield attributing characters. As ISSR markers are one of the cheapest and easiest marker systems with high efficiency in generating polymorphism among closely related varieties, they would play a major role in mulberry genome analysis in the future.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.31-34
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• 1996

The extensive researches have compared the performance of neural networks(NN) with those of various statistical techniques for the classification problem. The empirical results of these comparative studies have indicated that the neural networks often outperform the traditional statistical techniques. Moreover, there are some efforts that try to combine various classification methods, especially multivariate discriminant analysis with neural networks. While these efforts improve the performance, there exists a problem violating robust assumptions of multivariate discriminant analysis that are multivariate normality of the independent variables and equality of variance-covariance matrices in each of the groups. On the contrary, cluster analysis alleviates this assumption like neural networks. We propose a new approach to classification problems by combining the cluster analysis with neural networks. The resulting predictions of the composite model are more accurate than each individual technique.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Journal of the Korean Society for Railway
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• v.9 no.4 s.35
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• pp.412-418
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• 2006

In this paper we deal with the APARP theory which has been applied for UK railway system and risk assessment method which has been using in the domestic railway system for the safety demonstration. Both techniques are applied to the ATP wayside equipment for interface. Also, fur the applications of each techniques a analysis of the safety activity and a possibility of the application of ALARP theory are evaluated. Finally, we generate requirements of the safety demonstration for the future domestic railway system by way of the analysis of some assumptions and requirement data which can be applied to the risk assessment of ALARP.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1995.06a
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• pp.25-34
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• 1995

Large-scale low-alloy steel shafts, used in the manufacture of steam turbine, are produced by ingot making, forging and heat treatemtn processes. The numerical analysis techniques are introduced to analyze and design the working conditions in each process. The solidification of a steel ingot is studied through the finite element method. The open die press forging and quenching process are simulated by viscoplastic and elastic-plastic finite element method, respectively. Thus numerical analysis techniques are very useful tools to study favorable working conditions for better and more desirable product quality.

• East Asian mathematical journal
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• v.34 no.2
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• pp.177-189
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• 2018

Recently, Topological Data Analysis (TDA) has attracted attention among various techniques for analyzing big data. Mapper algorithm, which is one of TDA techniques, is used to visualize the cluster diagram. In this study, students were clustered according to the characteristics and degree of mathematics anxiety using a mapper, and students were visualized according to mathematics anxiety. In order to do this, Mathematical Anxiety Scale (Ko & Yi, 2011) in the aspect of mathematical instability in terms of teaching - learning, ie, Nature of Mathematics, Learning Strategy, Test/Performance is used. And the number of questions that measure the anxiety of mathematics can be extracted by extracting the most relevant items among the items that measure the anxiety of mathematics.

• International Journal of Contents
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• v.5 no.4
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• pp.88-93
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• 2009

It is important for communication networks to possess the capability to overcome failures and provide survivable services. We address modeling and analysis of performability affected by both performance and availability of system components for a token ring network under failure and repair conditions. Stochastic reward nets (SRN) is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, hierarchical SRN modeling techniques are used to overcome state largeness problem. The upper level model is used to compute availability and the lower level model captures the performance. And Normalized Throughput Loss (NTL) is obtained for the composite ring network for each node failures occurrence as a performability measure. One of the key contributions of this paper constitutes the Petri nets modeling techniques instead of complicate numerical analysis of Markov chains and easy way of performability analysis for a token ring network under SRN reward concepts.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.96-101
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• 2008

The field of load modeling has attracted the attention since it plays an important role for improving the accuracy of stability analysis and power flow estimation. Also, load modeling is an essential factor in the simulation and evaluation of power system performance. However, conventional load modeling techniques have some limitations with respect to accuracy for nonlinear and composite loads. Thus, precision load modeling technique and reasonable application method is needed for more accurate power system analysis. In this paper, we develop an intelligent load modeling method based. on neural network and application techniques for power system. The proposed method makes it possible to effectively estimate the load model for nonlinear models as well as linear models. Reasonable application method is also proposed for stability analysis. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.533-539
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• 2012

The performance analysis of wireless geolocation in a non-line-of-sight (NLOS) environment is a very important issue. Since Cramer-Rao lower bound (CRLB) determines the physical impossibility of the variance of an unbiased estimator being less than the bound, many studies presented the performance analysis in terms of CRLB. Several CRLBs for time-of-arrival (TOA), pseudo-range TOA, angle-of-arrival (AOA), and signal strength (SS) based positioning methods have been derived for NLOS environment. However, the performance analysis of time difference of arrival (TDOA) and TDOA/SS based geolocation techniques in a NLOS environment is still an opening issue. This paper derives the CRLBs of TDOA and TDOA/SS based positioning methods for NLOS environment. In addition, theoretical analysis proves that the derived CRLB for TDOA is the same as that of pseudo-range TOA and the TDOA/SS scheme has a lower CRLB than the TDOA (or SS) scheme.