• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.275-282
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• 2006

System algorithms estimated by deterministic input may occur the error between predicted and actual output. Especially, actual system can't predict the exact outputs due to uncertainty and tolernce of input parameters. A single output to a set of inputs has a limited value without the variation. Hence, we should consider various scatters caused by the load assessment, material characteristics, stress analysis and manufacturing methods in order to perform the robust design or etimate the reliability of structure. The system design with uncertainty should perform the probabilistic structural optimization with the statistical response and the reliability. This method calculated the probability distributions of the characteristics such as stress by combining stress analysis, response surface methodology and Monte Carlo simulation and got the probabilistic sensitivity. The sensitivity of structural response with respect to in constant design variables was estimated by fracture probability. Therefore, this paper proposed the probabilistic reliability design method for fracture of uncorved freight end beam and the design criteria by fracture probability.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1397-1405
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• 2009

In recent decades, exergy analysis has been holding spotlight as a useful tool in the design, assessment, optimization, and improvement of energy system. This paper presents the results of the energy and exergy analysis of a steam turbine cogeneration system for industrial complex using two efficiency concepts of conventional one and exergetic one. In order to obtain the destroyed exergy of each component, mathematical analysis is conducted by using exergy balance and the second law of thermodynamics, according as the parameters are changed, such as the ratio of returned process steam, process steam supplied, temperature and pressure of boiler and power. The computer program developed in this study can determine the efficiencies and exergy destroyed at each component of cogeneration system. As a result of this study, a component having the largest destroyed exergy was boiler. And closed and opened feedwater heater had the lowest one. The affects to the cogeneration system due to the variation of process steam flow and return rate of condensed water is shown that the total electric power efficiency(${\eta}_E$) is decreased as increasing the return rate of condensed water under constant process steam flow. As the boiler pressure is increased for the more production of electricity, the efficiency of cogeneration system was decreased.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.11
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• pp.624-630
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• 2013

A truck refrigeration system using phase change material (PCM) is expected to have a lower noise level, reduced energy cost, and much lower local greenhouse gas emission. Recently, a forced convection type PCM refrigeration module has been developed. As the operation time increases, the PCM around the air inlet melts, because of a large temperature difference between the PCM and air. Therefore, the latent heat transfer area decreases and the heat transfer rate of the module decreases even though there is a lot of PCM which does not melt around the air outlet. A computational fluid dynamic modeling of the PCM refrigeration module was developed and validated by the experiment. Using the CFD, the design parameters, such as the mass flow rate of the air and roughness of the slab, were investigated to improve the heat transfer inhomogeneity. As a result, the adoption of partial roughness on the slabs improved the heat transfer inhomogeneity and reduced a fan power.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1142-1166
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• 2020

Salp Swarm Algorithm (SSA) is a new nature-inspired swarm optimization algorithm that mimics the swarming behavior of salps navigating and foraging in the oceans. SSA has been proved to enable to avoid local optima and enhance convergence speed benefiting from the adaptive nonlinear mechanism and salp chains. In this paper, visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. A novel SSA-based tracking framework is proposed and the analysis and adjustment of parameters are discussed experimentally. Besides, the qualitative analysis and quantitative analysis are performed to demonstrate the tracking effect of our proposed approach by comparing with ten classical tracking algorithms. Extensive comparative experimental results show that our algorithm has good performance in visual tracking, especially for abrupt motion tracking.

• Tunnel and Underground Space
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• v.21 no.3
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• pp.174-180
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• 2011

An anisotropic version of Mohr-Coulomb failure criterion is proposed in order to provide a strength criterion for transversely isotropic rock. The concept of fabric tensor introduced by Pietruszczak & Mroz (2001) is employed to define the friction angle and cohesion as scalar functions of the fabric tensors. The anisotroy in these two strength parameters are calculated in association with the consideration of the relative rotation between the principal stress coordinate and the principal material triad. The critical plane on which the anisotropic function maximized is found by an optimization technique based on the Lagrange multiplier method. To demonstrate the performance of the anisotropic failure criterion, conventional triaxial tests on the samples having various inclinations of weakness plane are simulated and the resulting triaxial strength and dip angle of failure plane are discussed.

• New & Renewable Energy
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• v.8 no.2
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• pp.6-13
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• 2012

Due to global warming, the need to secure an alternative resource has become more important nationally. Having very strong current on the west coast with up to 10 m tidal range, there are many suitable site for the application of TCP (Tidal Current Power) in Korea. On the south west regions between many islands that create strong current in the narrow channels. The rotor is one of the essential components which can convert tidal current energy into rotational energy to generate electricity. The design optimization of rotor is very important to maximize the power production. The performance of rotor can be determined by various parameters including number of blades, shape, sectional size, diameters and etc. This paper introduces the multi-layer vertical axis tidal current power system which can be applied to offshore jetties and piers effectively. Various cases of VAT turbine were designed. Specifically, the number of blades and turbine shape are changed in several cases. Also, performance analysis was carried out by CFD.

• Transactions of Materials Processing
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• v.15 no.8 s.89
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• pp.550-555
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• 2006

In this study, effects of forming angle and friction coefficient on a initiation of the Mannesmann hole defect were analyzed by using a response surface method. The maximum effective plastic strain at center point of specimen is utilized for the prediction of the starting point of crack occurrence, which is suggested by the comparison of integrals of four different ductile fracture models between the histories of the effective plastic strain at center point. It was revealed that the principal stress at the center is the dominant element to the increase of the effective plastic strain. It was also verified by the simulation results from the comparison of experiment and simulation. It is provided that the forming angle of 25 degrees and the spreading angle of 1 degree can be a proper design condition without an occurrence of internal hole defect and an excessive slip.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• The Journal of Korean Association of Computer Education
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• v.11 no.5
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• pp.57-66
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• 2008

An adaptive optimization of parametric cubic convolution for image interpolation is derived in this paper. The proposed technique is based on optimizing the standard cubic convolution interpolation formula at each interpolated pixel. Conventional parametric cubic convolution methods use a fixed parameter in an image, so properties of each pixel cannot be incorporated into the interpolation. The proposed method optimizes the interpolation kernel by obtaining parameters adaptively on each pixel. A new cost function is introduced to reflect frequency properties of the original data. The proposed technique produces noticeably sharper edges than traditional techniques and exhibits an average PSNR improvement of traditional techniques.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1129-1134
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• 2016

This paper suggests a new method of finding regularization parameter for image restoration problems. If the prior information is not available, separate optimization functions for Tikhonov regularization parameter are suggested in the literature such as generalized cross validation and L-curve criterion. In this paper, unified Bayesian interpretation of Tikhonov regularization is introduced and applied to the image restoration problems. The relationship between Tikhonov regularization parameter and Bayesian hyper-parameters is established. Update formular for the regularization parameter using both maximum a posteriori(: MAP) and evidence frameworks is suggested. Experimental results show the effectiveness of the proposed method.