• Geotechnical Engineering
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• v.14 no.3
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• pp.47-62
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• 1998

The disturbed state concept (DSC) proposed here is based on the idea that a deforming material element can be treated as a mixture of two constituent parts in the relative intact (Rl) and fully adjusted (FA) states, referred to as reference states. Based on this idea, DSC provides a unified constitutive model for the characteristics of saturated sands under cyclic loading. The model parameters for saturated sands are evaluated by using data from truly triaxial test device, The laboratory test results are also used for the verification of D SC model. In general, the model predictions are found to provide satisfactory correlation with the test results. From the results of this research, it can be stated that the DSC model is capable of characterizing the cyclic behavior of saturated sands under dynamic loading.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.6
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• pp.88-95
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• 2003

The weighted sum of gray gas model(WSGGM) is applied to arbitrary mixtures of CO$_2$ and H$_2$0 gases. To evaluate this model, the spectral and total intensities are obtained for two different problem types. One has uniform, parabolic and boundary layer type temperature profiles with uniform partial pressure, and the other has nonuniform partial pressure and temperature profile. The results obtained from the two different problem types show fairly good agreements with the results obtained by the statistical narrow band model(SNB model) which is regarded as the reference solutions. The WSGGM and its data base provided by this study can be used for analysis of radiative transfer by combustion gases with different thermal loadings and chemical compositions.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.3
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• pp.419-429
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• 1999

Identifying the boundary of the effective receiving power of waves is one of the most important factors for cell optimization. In this paper, we introduce a propagation loss prediction model which yields highly accurate prediction in very complex areas as Seoul where a mixture of many large buildings, small buildings, broad streets, narrow alleys, rivers and forests co-exist in an irregular arrangement. This prediction model is based on neural networks trained on field measurement data collected in the past. Using these data along with 3-D digital elevation maps and vector data for building structures, we extract the parameter values which mainly affect the amount of propagation loss. These parameter values are then used as the inputs to the neural network. Trained neural network becomes the approximated function of the propagation loss model which generalizes very well and can predict accurately in the regions not included in training the neural network. The experimental results show a superior performance over the other models in the cells operating in the city of Seoul.

• Korean Chemical Engineering Research
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• v.46 no.4
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• pp.824-832
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• 2008

micellar solution which is comprised of surfactant monomers, monodisperse micelles, and solvent(water) is studied from a statistical-mechanical point of view. The model examined in this article is for the ideal mixture of monomers, micelles, and solvent with the dielectric constant identical to that of solvent, which is an assumption common to continuum models. The model also reflects interactions between monomer and solvent molecule, and also between micelle and solvent molecule. The statistical-mechanical model under consideration yields ln $X_{CMC}=A+BT+C/T+D{\ln}T$ with $X_{CMC}$ being critical mcielle concentration (in mole fraction), being temperature, and A, B, C, D being constants which depend on the properties of the surfactant molecules. The statistical-mechanical model discussed in this article provides a theoretical basis on the thermal dependence of critical micelle concentration

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1149-1152
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• 2005

Detecting faces in images with complex backgrounds is a difficult task. Our approach, which obtains state of the art results, is based on a generative neural network model: the Constrained Generative Model (CGM). To detect side view faces and to decrease the number of false alarms, a conditional mixture of networks is used. To decrease the computational time cost, a fast search algorithm is proposed. The level of performance reached, in terms of detection accuracy and processing time, allows to apply this detector to a real word application: the indexation of face images on the Web.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2721-2726
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• 2014

In a MOG algorithm using the GMM to subtract background, the model parameter computation and the object classification to be performed at every pixel require a huge computation and are the chief obstacles to its uses. This paper proposes a fast MOG algorithm that partly adopts the simple model parameter computation and the object classification skip on the basis of the object prediction. The former is applied to the pixels that gives little effect on the model parameter and the latter is applied to the pixels whose object prediction is firmly trusted. In comparative experiment between the conventional and proposed algorithms using videos, the proposed algorithm carries out the simple model parameter computation and the object classification skip over 77.75% and 92.97%, respectively, nevertheless it retains more than 99.98% and 99.36% in terms of image and moving object-unit average classification accuracies, respectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.12
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• pp.1489-1495
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• 2013

High-pressure flows are ubiquitous in many industrial fields. A representative application is fuel injection using a common-rail control system in diesel engines, where the injection pressure in the injector exceeds 1000 bar. In high-speed injection, the fluid injected through the nozzle undergoes breakup owing to the interaction with the ambient gas. The breakup process influences mixture formation, which in turn influences combustion in diesel engines. Therefore, it is very important to analyze the breakup process of fuel spray. The Reitz and Diwakar model and cascade atomization and breakup (CAB) model were used in this study as sub-models for the numerical analysis of the breakup process of fuel spray. This study aims to precisely analyze the breakup process of spray and to investigate the breakup frequency of the injected fuel. Consequently, it proposes a suitable sub-model for analyzing the breakup process of a diesel spray by using CFX, a commercial CFD program.

• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.69-73
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• 2004

A speaker model In speaker recognition system is to be trained from a large data set gathered in multiple sessions. Large data set requires large amount of memory and computation, and moreover it's practically hard to make users utter the data inseveral sessions. Recently the incremental adaptation methods are proposed to cover the problems, However, the data set gathered from multiple sessions is vulnerable to the outliers from the irregular utterance variations and the presence of noise, which result in inaccurate speaker model. In this paper, we propose an incremental robust adaptation method to minimize the influence of outliers on Gaussian Mixture Madel based speaker model. The robust adaptation is obtained from an incremental version of M-estimation. Speaker model is initially trained from small amount of data and it is adapted recursively with the data available in each session, Experimental results from the data set gathered over seven months show that the proposed method is robust against outliers.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.259-268
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• 2007

This paper contributes to an improvement of the statistical bandwidth extension(BWE) system based on Hidden Markov Model(HMM). First, the existing HMM training method for BWE, which is suggested originally by Jax, is analyzed in comparison with the general Baum-Welch training method. Next, based on this analysis, a new HMM-based BWE method is suggested which adopts the Baum-Welch re-estimation algorithm instead of the Jax's to train HMM model. Conclusionally speaking, the Baum-Welch re-estimation algorithm is a generalized form of the Jax's training method. It is flexible and adaptive in modeling the statistical characteristic of training data. Therefore, it generates a better model to the training data, which results in an enhanced BWE system. According to experimental results, the new method performs much better than the Jax's BWE systemin all cases. Under the given test conditions, the RMS log spectral distortion(LSD) scores were improved ranged from 0.31dB to 0.8dB, and 0.52dB in average.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.174-187
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• 2011

The flowed soil is able to be estimated from topographic data of before and after the debris flow. However, it is often difficult to obtain airborne LiDAR data before the debris flow area. Thus, this study tries to develop a topographic restoration method that can provide spatial distribution of flowed soil and reconstruct the topography before the debris flow using airborne LiDAR data. The topographic restoration method can express a numerical formula induced from a Gaussian mixture model after extracting the cross sections of linear or non-linear in debris flowed area. The topographic restoration method was verified by two ways using airborne LiDAR data of before and after the debris flow. First, each cross section extracted from the debris flow sites to restore the topography was compared with airborne LiDAR data of before the debris flow. Also, the topographic data produced after the topographic restoration method applied to the debris flow sites was verified by airborne LiDAR DEM. Verifying the results of the topographic restoration method, overall fitting accuracy showed high accuracy close to 0.5m.