• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.881-889
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• 2009

Support vector machines(SVMs) are known as flexible and efficient classifier of multivariate observations, producing a hyperplane or hyperdimensional curved surface in multidimensional feature space that best separates training samples by known groups. As various methodological extensions are made for SVM classifiers in recent years, it becomes more difficult to understand the constructed model intuitively. The aim of this paper is to visualize various SVM classifications tuned by several parameters in reduced dimensions, so that data analysts secure the tangible image of the products that the machine made.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2164-2167
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• 2003

In this paper we propose effective speech recognizer through recognition experiments for three feature parameters(PCA, ICA and MFCC) using SVM(Support Vector Machine) classifier In general, SVM is classification method which classify two class set by finding voluntary nonlinear boundary in vector space and possesses high classification performance under few training data number. In this paper we compare recognition result for each feature parameter and propose ICA feature as the most effective parameter

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.363-369
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• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1539-1547
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• 2014

In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.453-458
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• 2018

Geomechanical parameters are important factors for engineering projects during design, construction and support stages of tunnel and dam projects. Geostatistical estimation methods are known as one of the most significant approach at estimation of Geomechanical parameters. In this study, Azad dam headrace tunnel is chosen to estimate Geomechanical parameters such as Rock Quality Designation (RQD) and uniaxial compressive strength (UCS) by ordinary kriging as a geostatistical method. Also Rock Mass Rating (RMR) distribution is presented along the tunnel. Main aim in employment of geostatistical methods is estimation of points that unsampled by sampled points.To estimation of parameters, initially data are transformed to Gaussian distribution, next structural data analysis is completed, and then ordinary kriging is applied. At end, specified distribution maps for each parameter are presented. Results from the geostatistical estimation method and actual data have been compared. Results show that, the estimated parameters with this method are very close to the actual parameters. Regarding to the reduction of costs and time consuming, this method can use to geomechanical estimation.

• Bulletin of the Korean Chemical Society
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• v.11 no.2
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• pp.85-88
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• 1990

The electron paramagnetic resonance (EPR) spectrum of the copper (II) complex with the 2-methylamino-1-cyclo-pentene-1-dithiocarboxylate (acdc) anion, $Cu(N-CH_3acdc)_2$ has been studied in the diamagnetic host lattices afforded by the corresponding divalent nickel, zinc, cadmium and mercury complexes. EPR parameters of the complex support the exclusive use of sulfur atoms by the ligand in metal binding. A combination of host lattice structure and covalency effects can be account for the observed spin-Hamiltonian parameters.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.89-100
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• 2016

Given the unpredictability of the space environment, satellite communications are manually performed by exchanging telecommands and telemetry. Ground support for orbiting satellites is given only during limited periods of ground antenna visibility, which can result in conflicts when multiple satellites are present. This problem can be regarded as a scheduling problem of allocating antenna support (task) to limited visibility (resource). To mitigate unforeseen errors and costs associated with manual scheduling and mission planning, we propose a novel method based on a genetic algorithm to solve the ground support problem of multiple satellites and antennae with visibility conflicts. Numerous scheduling parameters, including user priority, emergency, profit, contact interval, support time, remaining resource, are considered to provide maximum benefit to users and real applications. The modeling and formulae are developed in accordance with the characteristics of satellite communication. To validate the proposed algorithm, 20 satellites and 3 ground antennae in the Korean peninsula are assumed and modeled using the satellite tool kit (STK). The proposed algorithm is applied to two operation modes: (i) telemetry, tracking, and command and (ii) payload. The results of the present study show near-optimal scheduling in both operation modes and demonstrate the applicability of the proposed algorithm to actual mission control systems.

• Composites Research
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• v.28 no.2
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• pp.65-69
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• 2015

This study dealt with dynamic characteristics by real car crash simulation of composite support structures for road facilities. The effects of different material properties of composites for various car crash speeds are studied using the LS-DYNA finite element program for this study. In this study, the existing finite element analysis of steel support structures using the LS-DYNA program is further extended to study dynamic behaviors of the support structures made of various composite materials. Based on the passenger safety assessment, the numerical results for various parameters are verified by comparing different models with internal energy occurred in the support and car.

• Journal of Industrial Technology
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• v.23 no.A
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• pp.69-81
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• 2003

Numerical analysis is important for the design, construction and maintenance of large caverns. The rock mass contains generally discontinuities such as faults, joints and fissures. The mechanical behavior and geometric characteristics of these discontinuities would have a significant impact on the stability of the caverns. In this research the Distinct Element Method(DEM) was used to analyze the structural stability of the large cavern. The Barton-Bandis Joint Model (B-B J.M) was used as a constitutive model for the joint. In addition, two different cases 1) analysis with a support system and 2) analysis with no support system, were analyzed to optimize a support system and to investigate reinforcing effects of a support system. The most significant parameters of in-situ stress, JRC of in-situ natural joints, and spatial distribution characteristics of discontinuities were acquired through field investigation. Displacement (horizontal, joint shear), maximum joint opening, maximum and minimum principal stresses, range of relaxed zone, rockbolt axial forces and shotcrete stresses were calculated at each excavation stage. As a result of analysis the calculated values proved to be under the allowable value Rockbolts also proved to be an efficient support measure to control joint shear displacement which had significant effects on extending the relaxed zone. As a consequence, the structural stability of the cavern was assured with an appropriate support system.

• Journal of Korean Academy of Nursing
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• v.43 no.1
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• pp.81-90
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• 2013

Purpose: This study was done to test structural equation modeling of health-related quality of life (QOL) of men with obstructive sleep apnea in order to identify parameters affecting QOL and provide guidelines for interventions and strategies to improve QOL in these patients. Methods: Model construction was based on 'The conceptual model of patient outcome in health-related QOL' by Wilson and Cleary, using the variables; age, physiological factors, social support, cognitive appraisal, symptoms and QOL. Participants were 201 adult male patients recruited at a tertiary university hospital in Seoul. Data were collected via questionnaires, polysomnography, and clinical records. Results: Age and symptoms directly influenced QOL. Social support and cognitive appraisal about sleep did not have a direct influence on QOL, but indirectly affected it via symptoms. QOL was lower in patients who were younger and had more severe symptoms. Symptoms were more severe for patients with lower social support and more dysfunctional cognitive appraisal. When social support was lower, cognitive appraisal was more dysfunctional. Conclusion: These results suggest it is necessary to not only manage symptoms, but also apply interventions to increase social support and cognitive appraisal about sleep in order to increase QOL in patients with obstructive sleep apnea.