• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• Computers and Concrete
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• v.25 no.5
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• pp.427-432
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• 2020

In this research, the buckling analysis of sandwich beam with composite reinforced by graphene platelets (GPLs) in two face sheets is investigated. Three type various porosity patterns including uniform, symmetric and asymmetric are considered through the thickness direction of the core. Also, the top and bottom face sheets layers are considered composite reinforced by GPLs/CNTs based on Halpin-Tsai micromechanics model and extended mixture rule, respectively. Based on various shear deformation theories such as Euler-Bernoulli, Timoshenko and Reddy beam theories, the governing equations of equilibrium using minimum total potential energy are obtained. It is seen that the critical buckling load decreases with an increase in the porous coefficient, because the stiffness of sandwich beam reduces. Also, it is shown that the critical buckling load for asymmetric distribution is lower than the other cases. It can see that the effect of graphene platelets on the critical buckling load is higher than carbon nanotubes. Moreover, it is seen that the difference between carbon nanotubes and graphene platelets for Reddy and Euler-Bernoulli beam theories is most and least, respectively.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.76-81
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• 2004

Injection molding is the most widely used process fir the industrial forming of plastic articles. During an injection molding process of composites, the fiber-matrix separation and fiber orientation are caused by the flow of molten polymer/fiber mixture. As a result. the product tends to be nonhomogeneous and anisotropic. Hence, it is very important to clarify the relations between separation orientation and injection molding conditions. So far, there is no research on the measurement of fiber orientation using image processing. In this study, the effects of fiber content ratio and molding condition on the fiber orientation-angle distributions are studied experientially. Using the image processing method, the fiber orientation distribution of welding pars in injection-molded products is assessed. And the effects of fiber content and injection mold shapes on the fiber orientation in case of fiber reinforced polymeric composites are studied experimentally.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.73-80
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• 2005

Injection molding is the most widely used process for the industrial forming of plastic articles. During an injection molding process of composites, the fiber-matrix separation and fiber orientation are caused by the flow of molten polymer/fiber mixture. As a result, the product tends to be nonhomogeneous and anisotropic. Hence, it is very important to clarify the relations between separation orientation and injection molding conditions. So far, there is no research on the measurement of fiber orientation using image processing. In this study, the effects of fiber content ratio and molding condition on the fiber orientation-angle distributions are studied experimentally. Using the image processing method, the fiber orientation distribution of welding parts in injection-molded products is assessed. And the effects of fiber content and injection mold shapes on the fiber orientation in case of fiber reinforced polymeric composites are studied experimentally.

• The KSFM Journal of Fluid Machinery
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• v.20 no.2
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• pp.75-81
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• 2017

This paper presents a numerical investigation on the local hydraulic cavitation phenomena of water resulting from the rotor with high rotational speed in the hydraulic cavitation heat generator. The numerical simulation utilizes the standard k-epsilon turbulence model, the mixture multiphase model and the Schnerr-Sauer cavitation model to simulate the complex cavitation phenomena in the generator. For exploring the efficient shape of the dimples on the rotor to causing cavitation phenomena artificially, the pressure distributions and the volume fractions of the vapor on the rotor are investigated respectively about different shapes of the rotor in the generator. The optimum shape of the dimple to causing cavitation phenomena in the selected shapes is obtained by the means of the numerical simulation.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.11
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• pp.2981-2994
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• 1995

In order to elucidate the effects of positive pressure gradient on flame properties, structure and stabilization, an experimental study is made on turbulent diffusion flame stabilized by a circular cylinder in a divergent duct flow. A commercial grade gaseous propane is injected from two slits on the rod as fuel. In this paper, stabilization, characteristics and flame structure are examined by varying the divergent angle of duct. Temperature, ion current and Schlieren photographs were measured. It is found that critical divergent angle is expected to be about 8 ~ 12 degree through blow-off velocity pattern to divergent angle and the positive pressure gradient influences the flame temperature, intensity of ion current and eddy structure behind the rod. With the increase of divergent angle, typical temperature of recirculation zone is low but intensity of ion current is high in shear layer behind rod. Energy distributions of fluctuating temperature and ion current signals turn up low frequency corresponding to large scale eddies but high frequency corresponding to small scale eddies as well as low with the increase of divergent angle. Therefore the flame structure becomes a typical distributed-reacting flame.

• Journal of Korean Academy of Nursing
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• v.42 no.1
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• pp.48-55
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• 2012

Purpose: The aim of this study was to compare statistical methods to control response bias in nursing activity surveys. Methods: Data were collected at a medical unit of a general hospital. The number of nursing activities and consumed activity time were measured using self-report questionnaires. Descriptive statistics were used to identify general characteristics of the units. Average, Z-standardization, gamma regression, finite mixture model, and stochastic frontier model were adopted to estimate true activity time controlling for response bias. Results: The nursing activity time data were highly skewed and had non-normal distributions. Among the 4 different methods, only gamma regression and stochastic frontier model controlled response bias effectively and the estimated total nursing activity time did not exceeded total work time. However, in gamma regression, estimated total nursing activity time was too small to use in real clinical settings. Thus stochastic frontier model was the most appropriate method to control response bias when compared with the other methods. Conclusion: According to these results, we recommend the use of a stochastic frontier model to estimate true nursing activity time when using self-report surveys.

• Steel and Composite Structures
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• v.27 no.3
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• pp.255-271
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• 2018

This paper deals with the transient dynamic analysis and elastic wave propagation in a functionally graded graphene platelets (FGGPLs)-reinforced composite thick hollow cylinder, which is subjected to shock loading. A micromechanical model based on the Halpin-Tsai model and rule of mixture is modified for nonlinear functionally graded distributions of graphene platelets (GPLs) in polymer matrix of composites. The governing equations are derived for an axisymmetric FGGPLs-reinforced composite cylinder with a finite length and then solved using a hybrid meshless method based on the generalized finite difference (GFD) and Newmark finite difference methods. A numerical time discretization is performed for the dynamic problem using the Newmark method. The dynamic behaviors of the displacements and stresses are obtained and discussed in detail using the modified micromechanical model and meshless GFD method. The effects of the reinforcement of the composite cylinder by GPLs on the elastic wave propagations in both displacement and stress fields are obtained for various parameters. It is concluded that the proposed micromechanical model and also the meshless GFD method have a high capability to simulate the composite structures under shock loadings, which are reinforced by FGGPLs. It is shown that the modified micromechanical model and solution technique based on the meshless GFD method are accurate. Also, the time histories of the field variables are shown for various parameters.

• Korean Management Science Review
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• v.24 no.2
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• pp.143-155
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• 2007

The purpose of this paper is to develop a new method for information system sizing and selection based on a hybrid mixture of simulation and genetic algorithm, and to show its cost-effectiveness by applying it to a real world problem. To serve this purpose, we propose an operational model which identifies a set of system alternatives using simulation, and determines the optimal one using genetic algorithm. Specifically, with simulation, we generate probability distributions describing real data gathered from actual system, which can overcome the major weakness of the existing methodology that normally employs point estimates of the actual data and constant correction factors without theoretical rationale. We next search for the optimal combination of H/W, the number of CPUs, and S/W, which meets both of our business goals of incurring low TCO(total cost of ownership) and maintaining a good level of transaction processing performance. Experimental result shows the proposed method in this paper saves the cost while it preserves the system's capacity within allowable performance range.

• Journal of Korean Society for Quality Management
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• v.47 no.2
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• pp.271-281
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• 2019

Purpose: The purpose of this study is to suggest a more efficient distance measure taking into account the relationship between categorical variables for categorical data cluster analysis. Methods: In this study, the association-based dissimilarity was employed to calculate the distance between two categorical data observations and the distance obtained from the association-based dissimilarity was applied to the PAM cluster algorithms to verify its effectiveness. The strength of association between two different categorical variables can be calculated using a mixture of dissimilarities between the conditional probability distributions of other categorical variables, given these two categorical values. In particular, this method is suitable for datasets whose categorical variables are highly correlated. Results: The simulation results using several real life data showed that the proposed distance which considered relationships among the categorical variables generally yielded better clustering performance than the Hamming distance. In addition, as the number of correlated variables was increasing, the difference in the performance of the two clustering methods based on different distance measures became statistically more significant. Conclusion: This study revealed that the adoption of the relationship between categorical variables using our proposed method positively affected the results of cluster analysis.