• Smart Structures and Systems
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• v.19 no.2
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• pp.115-126
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• 2017

In this paper, size dependent bending and free flexural vibration behaviors of functionally graded (FG) nanobeams are investigated using a nonlocal quasi-3D theory in which both shear deformation and thickness stretching effects are introduced. The nonlocal elastic behavior is described by the differential constitutive model of Eringen, which enables the present model to become effective in the analysis and design of nanostructures. The present theory incorporates the length scale parameter (nonlocal parameter) which can capture the small scale effect, and furthermore accounts for both shear deformation and thickness stretching effects by virtue of a hyperbolic variation of all displacements through the thickness without using shear correction factor. The material properties of FG nanobeams are assumed to vary through the thickness according to a power law. The neutral surface position for such FG nanobeams is determined and the present theory based on exact neutral surface position is employed here. The governing equations are derived using the principal of minimum total potential energy. The effects of nonlocal parameter, aspect ratio and various material compositions on the static and dynamic responses of the FG nanobeam are discussed in detail. A detailed numerical study is carried out to examine the effect of material gradient index, the nonlocal parameter, the beam aspect ratio on the global response of the FG nanobeam. These findings are important in mechanical design considerations of devices that use carbon nanotubes.

• IMMUNE NETWORK
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• v.13 no.4
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• pp.133-140
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• 2013

Since the discovery of the immunomodulation property of mesenchymal stem cells (MSCs) about a decade ago, it has been extensively investigated whether MSCs can be used for the treatment of immune-related diseases, such as graft versus-host disease (GvHD). However, how to evaluate the efficacy of human MSCs for the clinical trial is still unclear. We used an MHC-mismatched model of GvHD (B6 into BALB/c). Surprisingly, the administration of the human MSCs (hMSCs) could reduce the GvHD-related mortality of the mouse recipients and xenogeneically inhibit mouse T-cell proliferation and $IFN-{\gamma}$ production in vitro. We recently established a new protocol for the isolation of a homogeneous population of MSCs called subfractionation culturing methods (SCM), and established a library of clonal MSC lines. Therefore, we also investigated whether MSCs isolated by the conventional gradient centrifugation method (GCM) and SCM show different efficacy in vivo. Intriguingly, clonal hMSCs (hcMSCs) isolated by SCM showed better efficacy than hMSCs isolated by GCM. Based on these results, the MHC-mismatched model of GvHD may be useful for evaluating the efficacy of human MSCs before the clinical trial. The results of this study suggest that different MSC lines may show different efficacy in vivo and in vitro.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.145-152
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• 2003

In this paper, assuming that the score of speech utterance is the product of HMM log likelihood and HMM weight, we propose a new method that HMM weights are adapted iteratively like the general MCE training. The proposed method adjusts HMM weights for better performance using delta coefficient defined in terms of misclassification measure. Therefore, the parameter estimation and the Viterbi algorithms of conventional 1:.um can be easily applied to the proposed model by constraining the sum of HMM weights to the number of HMMs in an HMM set. Comparing with the general segmental MCE training approach, computing time decreases by reducing the number of parameters to estimate and avoiding gradient calculation through the optimal state sequence. To evaluate the performance of HMM-based speech recognizer by weighting HMM likelihood, we perform Korean isolated digit recognition experiments. The experimental results show better performance than the MCE algorithm with state weighting.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.32-42
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• 2000

This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

• Korean Chemical Engineering Research
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• v.51 no.1
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• pp.151-156
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• 2013

Crystallization of $CaCO_3$ is practiced on a polymethylsiloxane (PDMS) - based microfluidic system. Liquid- liquid reaction was investigated by mixing calcium chloride ($CaCl_2$) and sodium carbonate ($Na_2CO_3$) solution to crystallize $CaCO_3$. Aspartic acid (Asp) was added to investigate the morphology change such as vaterite and calcite. Suitable ratio of $Na_2CO_3$ and $CaCl_2$ was searched for initial seed formation. Christmas tree model was used as microfluidic device to form concentration gradient of $Na_2CO_3$ and $CaCl_2$. After observing microfluidic channel by using optical microscope, we found that seeds of $CaCO_3$ were formed under the condition that the ratio of $Na_2CO_3$ and $CaCl_2$ was 2:1. Morphology of crystals were also observed as $CaCO_3$ crystals grow. When Asp was added, vaterite crystal was more frequently found in two morphologies (vaterite and calcite) and seed formation and crystal growth were inhibited.

• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.722-734
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• 2004

Longitudinal profiles of bedrock rivers play a fundamental role in landscape history by setting the boundary conditions for landform evolution. Longitudinal profiles are changed with climatic conditions, lithology and tectonic movements. Tectonic movement is an important factor controlling longitudinal profiles, especially in tectonically active area where uplift rates are regarded as a major factor controlling channel gradient. However study on bedrock channel has made little progress, because controls over bedrock river incision are yet to be clarified. Previous numerical simulations have used a simple diffusion model, which links together the overall processes of bedrock channel erosion as in other landform evolution models. In this study, previous bedrock incision models based on physical processes (especially abrasion) are reviewed and new modifications are introduced. Using newly formulated numerical model, the role of spatial pattern and intensity of tectonic uplift on changes in river longitudinal profile was simulated and discussed.

• Tunnel and Underground Space
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• v.19 no.5
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• pp.407-420
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• 2009

In this study geostatistical technique using indicator kriging was performed to evaluate the optimal rock mass classification by integrating the various geophysical information such as borehole data and geophysical data. To get the optimal kriging result, it is necessary to devise the suitable technique to integrate the hard (borehole) and soft (geophysical) data effectively. Also, the model parameters of the variogram must be determined as a priori procedure. Iterative non-linear inversion method was implemented to determine the model parameters of theoretical variogram. To verify the algorithm, behaviour of object function and precision of convergence were investigated, revealing that gradient of the range is extremely small. This algorithm for the field data was applied to a mountainous area planned for a large-scale tunneling construction. As for a soft data, resistivity information from AMT survey is incorporated with RMR information from borehole data, a sort of hard data. Finally, RMR profiles were constructed and attempted to be interpreted at the tunnel elevation and the upper 1D level.

• Journal of the Korean Geotechnical Society
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• v.29 no.5
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• pp.65-74
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• 2013

The natural disaster occurrences and the loss of lives caused by the steep-slope failures in Korea were investigated in this study. The investigation includes the frequency rate of the steep-slope failures with respect to the characteristics of precipitation, underlying bedrock, and weathered soils. Analysis on the problems in the existing estimation methods of steep-slope failure was also undertaken, and a new model using unsaturated infinite slope stability was developed for the better slope failure estimation. The slope analyses by the newly developed model were performed considering unsaturated infinite slope, the gradient of slope, and hydro/mechanical properties of soils. Steep-slope failure estimation criterion is proposed based on the analysis results. In addition, the precipitation amount corresponding to warning stages against steep-slope failure is provided as an equation of Intensity-Duration criterion.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.4
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• pp.241-247
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• 2019

A band gap refers to a certain frequency range where the propagation of mechanical waves is prohibited. This work focuses on engineering three-dimensional Kelvin lattices having external band gaps at low audible frequency ranges using a gradient-based design optimization method. Elastic wave propagation in an infinite periodic lattice is investigated by employing the Bloch theorem. We model the ligaments using a shear-deformable beam model obtained by consistent linearization in a geometrically exact beam theory. For a given lattice topology, we enlarge band gap sizes by controlling the configuration of the beam neutral axis and cross-section thickness that are smoothly parameterized by B-spline basis functions within the isogeometric analysis framework.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.