• Korean Journal of Materials Research
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• v.13 no.7
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• pp.447-452
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• 2003

Effects of annealing and thickness of Co layer in Co/IrMn bilayers on the magnetic properties have been investigated. The highest interfacial exchange coupling energy($J_{K}$ = 0.12 erg/$\textrm{cm}^2$) was obtained for 10 nm Co layer thickness. Exchange bias field is inversely proportional to the magnetization, the thickness of the pinned layer, and the grain size of antiferromagnetic layer. Also it is related to the interfacial exchange energy difference, which is expected to depend on the surface roughness. These results almost agree with the random-field model of exchange anisotropy proposed by Malozemoff. Exchange bias field decreased slowly with increasing annealing temperature up to X$300^{\circ}C$. However, exchange bias field increased above $300^{\circ}C$.

• Korean Journal of Cognitive Science
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• v.7 no.2
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• pp.57-73
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• 1996

• The Bulletin of The Korean Astronomical Society
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• v.20
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• pp.11.1-11.1
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• 1995

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1114-1119
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• 2010

This paper is concerned with an numerical analysis of electromagnetic wave propagation from randomly rough surfaces as a desert, sea surface and so on. We propose discrete ray tracing method (DRTM) for analysis of characteristics of wave propagation along one dimensional (1D) and two dimensional (2D) random rough surfaces. The point of the present method is to discretize not only rough surface but also ray tracing. This technique helps saving computer memories and does simplifying ray searching algorithm resulting in saving computation time. Numerical calculations are carried out for 1D and 2D random rough surfaces and electric field distributions are shown to check the effectiveness of the proposed DRTM.

• Journal of the Korean Institute of Telematics and Electronics
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• v.16 no.6
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• pp.1-10
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• 1979

Recent progresses in the signal processing technique in digital domain as well as that of analogue, impose a heavy burden on scientists and engineers intending to study this dis cipline, we surveyed basic tools for these vast branches to help those who have concerns on this field without being buried in detailed techniques. The first article is naturally confined to the basic tools namely random process analysis and characterization of random signal by covariance function.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.403-407
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• 2003

This paper studies the error of pressure, particle velocity, and intensity which are distributed in a space. Errors may be amplified when other sound field variables are predicted. We theoretically derive their bias error and random error. The analysis shows that many samples do not always guarantee good results. Random error of the velocity and intensity are increased when many samples are used. The characteristics of the amplification of the random error are analyzed in terms of the sample spacing. The amplification was found to be related to the spatial differential of random noise. The numerical simulations are performed to verify theoretical results.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.233-238
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• 2010

In this paper, we have proposed discrete ray tracing method (DRTM) for numerical analysis of characteristics of electromagnetic propagation along 2D random rough surfaces. The point of the present method is to discretize not only rough surface but also ray tracing. The former helps saving computer memories and the latter does simplifying ray searching algorithm resulting in saving computation time. Numerical calculations are carried out for 2D random rough surfaces, and electric field distributions are shown to check the effectiveness of the proposed DRTM.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.40-48
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• 2007

This paper presents a novel texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields in multiscale Bayesian framework. Multiscale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Texture classification at each scale is performed by the posterior probabilities from MLP networks and MAP (maximum a posterior) classification. Then, in order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multiscale MAP classifications sequentially from coarse to fine scales. This process is done by computing the MAP classification given the classification at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale classification. In this fusion process, the MRF (Markov random field) prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. The proposed segmentation method shows better performance than texture segmentation using the HMT (Hidden Markov trees) model and HMTseg.

• Journal of the Korean Geotechnical Society
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• v.24 no.8
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• pp.111-123
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• 2008

Geotechnical engineering problems are characterized by many sources of uncertainty. Some of these sources are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for a probabilistic analysis that considers the spatial variability of soil properties is presented to study the response of spatially random soil. The approach integrates a commercial finite difference method and random field theory into the framework of a probabilistic analysis. Two-dimensional non-Gaussian random fields are generated based on a Karhunen-$Lo{\grave{e}}ve$ expansion in a fashion consistent with a specified marginal distribution function and an autocorrelation function. A Monte Carlo simulation is then used to determine the statistical response based on the random fields. A series of analyses were performed to study the effects of uncertainty due to the spatial heterogeneity on the settlement and bearing capacity of a rough strip footing. The simulations provide insight into the application of uncertainty treatment to the geotechnical problem and show the importance of the spatial variability of soil properties with regard to the outcome of a probabilistic assessment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.515-525
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• 2014

Recently, because of increasing in downpour and typhoon, which are caused by climate changes, those sedimentation disasters, such as landslide and debris flow, have become frequent. Those sedimentation disasters take place in natural slope. In order to predict debris flow damage range within wide area, the response model is more appropriate than numerical analysis. However, to make a prediction using Random Walk Model, the regional parameters is needed to be decided, since the regional environments conditions are not always same. This random Walk Model is a probability model with easy calculation method, and simplified slope factor. The objective of this study is to calculate the optimal parameters of Random Walk Model for Umyeon mountain in Seoul, where the large debris flow has occurred in 2011. Debris flow initiation zones and sedimentation zones were extracted through field survey, aerial photograph and visual reading of debris flow before and after its occurrence via LiDAR DEM.