• 한국공간정보시스템학회:학술대회논문집
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• 2002.03a
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• pp.45-52
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• 2002

Artificial neural networks (ANN) have been successfully used for classifying remotely sensed imagery. However, ANN still is not the preferable choice for classification over the conventional classification methodology such as the maximum likelihood classifier commonly used in the industry production environment. This can be attributed to the ANN characteristic built-in stochastic process that creates difficulties in dealing with unequally represented training classes, and its training performance speed. In this paper we examined some practical aspects of training classes when using a back propagation neural network model for remotely sensed imagery. During the classification process of remotely sensed imagery, representative training patterns for each class are collected by polygons or by using a region-growing methodology over the imagery. The number of collected training patterns for each class may vary from several pixels to thousands. This unequally populated training data may cause the significant problems some neural network empirical models such as back-propagation have experienced. We investigate the effects of training over- or under- represented training patterns in classes and propose the pattern repopulation algorithm, and an adaptive alpha adjustment (AAA) algorithm to handle unequally represented classes. We also show the performance improvement when input patterns are presented in random fashion during the back-propagation training.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.623-628
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• 2007

Reliability analysis based on fracture mechanics requires knowledge of the on statistical parameters m and C in the fatigue crack growth law $da/dN=C({\Delta}K)^m$. The purpose of the present study is to investigate if it is possible to explain the change of parameter m by the fluctuation of C only. In this study, we apply the Paris-Erdogan law treating the parameter C as random and the parameter m as constant. Fluctuations in crack growth rate are assumed to be dependent only on C. The material resistance to fatigue crack growth(Z=1/C) is treated as a spatially random process, that varies along the crack path. The theoretical crack growth rates at various stress intensity factors are discussed. Additionally, the results of constant ${\Delta}K$ fatigue crack growth tests are reported for the structural steel, SM45C. The experimental data have been analyzed to determine the probability distribution of fatigue crack growth resistanc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5301-5323
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• 2017

Small cell deployment offers a low-cost solution for the boosted traffic demand in heterogeneous cellular networks (HCNs). Besides improved spatial spectrum efficiency and energy efficiency, future HCNs are also featured with the trend of network architecture convergence and feasibility for flexible mobile applications. To achieve these goals, dual connectivity (DC) is playing a more and more important role to support control/user-plane splitting, which enables maintaining fixed control channel connections for reliability. In this paper, we develop a tractable framework for the downlink SINR analysis of DC assisted HCN. Based on stochastic geometry model, the data-control joint coverage probabilities under multi-frequency and single-frequency tiering are derived, which involve quick integrals and admit simple closed-forms in special cases. Monte Carlo simulations confirm the accuracy of the expressions. It is observed that the increase in mobility robustness of DC is at the price of control channel SINR degradation. This degradation severely worsens the joint coverage performance under single-frequency tiering, proving multi-frequency tiering a more feasible networking scheme to utilize the advantage of DC effectively. Moreover, the joint coverage probability can be maximized by adjusting the density ratio of small cell and macro cell eNBs under multi-frequency tiering, though changing cell association bias has little impact on the level of the maximal coverage performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1457-1467
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• 2015

Depending on the type of Tx-Rx pairs, VHF Data Exchange System (VDES) for maritime communication is expected to employ the different frequency channels. Load imbalance between the different channels turns out to be a critical problem for the multi-hop communication using Ad-hoc Self-Organizing TDMA (ASO-TDMA) MAC protocol, which has been proposed to provide the connectivity between land station and remote ship stations. In order to handle the inter-channel load imbalance problem, we consider a model of the stochastic geomety in this paper. After analyzing the spatial reuse efficiency in each hop region by the given model, we show that the resource utility can be maximized by balancing the inter-channel traffic load with optimal resource allocation in each hop region.

• Composites Research
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• v.35 no.3
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• pp.188-195
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• 2022

This paper proposes a multiscale process-structural analysis methodology and applies to a battery housing part made of the short fiber-reinforced and fabric-reinforced composite layers. In particular, uncertainties of the material properties within the microscale representative volume element (RVE) were considered. The random spatial distribution of matrix properties in the microscale RVE was realized by the Karhunen-Loeve Expansion (KLE) method. Then, effective properties of the RVE reflecting on spatially varying matrix properties were obtained by the computational homogenization and mapped to a macroscale FE (finite element) model. Morever, through the hybrid process simulation, a FE (finite element) model mapping residual stress and fiber orientation from compression molding simulation is combined with one mapping fiber orientation from the draping process simulation. The proposed method is expected to rigorously evaluate the design requirements of the battery housing part and composite materials having various material configurations.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.3-12
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• 2010

In previous studies, the digital measurement systems and analysis algorithms were developed by using the related techniques, such as the aerial photograph detection and high resolution satellite image process. However, these studies were limited in 2-dimensional geo-processing. Therefore, it is necessary to apply the 3-dimensional spatial information and coordinate system for higher accuracy in recognizing and locating of geo-features. The objective of this study was to develop a stochastic algorithm for the optimal sensor placement using the 3-dimensional spatial analysis method. The 3-dimensional information of the LiDAR was applied in the sensor field algorithm based on 2- and/or 3-dimensional gridded points. This study was conducted with three case studies using the optimal sensor placement algorithms; the first case was based on 2-dimensional space without obstacles(2D-non obstacles), the second case was based on 2-dimensional space with obstacles(2D-obstacles), and lastly, the third case was based on 3-dimensional space with obstacles(3D-obstacles). Finally, this study suggested the methodology for the optimal sensor placement - especially, for ground-settled sensors - using the LiDAR data, and it showed the possibility of algorithm application in the information collection using sensors.

• The Journal of Engineering Geology
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• v.19 no.1
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• pp.81-88
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• 2009

Two dimensional Monte-Carlo simulations of a non-reactive solute plume in isotropic porous media which are physically and chemically heterogeneous are conducted to determine the variations of moment. Retardation factors of 1, 2 and 5 are given to ascertain how the second moments are changed as adsorption increased. Retarded longitudinal second spatial moment, ${Z_{11}}^{'R}(t',l')$, increased during the transport process and as the dimensionless lengths of line plume source, $l_2'$, increased. ${Z_{11}}^{'R}(t',l')$ decreased as the retardation factors increased, and the simulated moments fit well to the first-order analytical results. Retarded longitudinal plume centroid variance, ${Z_{11}}^{'R}(t',l')$, decreased as the dimensionless lengths of line plume source, $l_2'$, increased and as the retardation factor increased. The result indicates that the uncertainty about the plume center decreased, and the ergodic condition for the second spatial moments is far from reaching. Simulated longitudinal one particle displacement covariance, ${Z_{11}}^{'R}(t')$, well consistent with the first-order analytical results for the three degrees of retardation factors of 1, 2 and 5 respectively. It is, consequently, concluded that the retarded longitudinal second moments could be produced by stochastic simulation, and that the first-order analytical results definitely provides very close values of the longitudinal retarded moments.

• ETRI Journal
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• v.45 no.5
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• pp.768-780
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• 2023

The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three-dimensional multiple-input multiple-output concentric two-hemisphere model is proposed to characterize the scattering environment around a vehicle in an urban UAV-to-vehicle communication scenario. Multipath components of the model consisted of lineof-sight and single-bounced components. This study focused on the key parameters that determine the scatterer distribution. A time-variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space-time-frequency correlation function, Doppler power spectral density, level-crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level-crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level-crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry-based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.

• Journal of Korea Water Resources Association
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• v.44 no.5
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• pp.351-362
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• 2011

The soil moisture measurements and correlation analysis are presented to improve understanding the hydrological process at the hillslope scale. The rainfall events is a main driver of soil moisture variation, and its stochastic characteristic need to be properly treated prior to the correlation analysis between soil moisture measurements. Using field measurements for two designated periods during the late summer and autumn seasons in 2007 obtained from the Bumrunsa hillslope located at the Sulmachun watershed, prewhitened correlation analysis were performed for 8, 14, 7 and 7 relationships representing the vertical, lateral, recharge and return flows, for two designated periods, respectively. The analysis indicated both temporal and spatial variation patterns of hydrological processes, which can be explained by the relative contribution of matrix and macropore flows and the impact of transect topography, respectively.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.813-826
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• 2018

Recently, weather radar system has been widely used for effectively monitoring near real-time weather conditions. The radar rainfall estimates are generally relies on the Z-R equation that is an indirect approximation of the empirical relationship. In this regards, the bias in the radar rainfall estimates can be affected by spatial-temporal variations in the radar profile. This study evaluates the uncertainty of the Z-R relationship while considering the rainfall types in the process of estimating the parameters of the Z-R equation in the context of stochastic approach. The radar rainfall estimates based on the Bayesian inference technique appears to be effective in terms of reduction in bias for a given season. The derived Z-R equation using Bayesian model enables us to better represent the hydrological process in the rainfall-runoff model and provide a more reliable forecast.