• Genomics & Informatics
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• v.14 no.4
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.38-43
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• 2003

The dimensions of die and workpiece are changed continuously during loading, unloading, and ejecting stage. Finally, to predict precisely the dimension of forged part and get the die dimension for the net-shape components, the analysis of die and workpiece should be evaluated from the loading to ejecting. Therefore, the experimental and FEM analysis are peformed to investigate the elastic characteristics at workpiece and die in the closed-die upsetting for ferrous material. FE techniques are proposed to consider the unloading and ejecting stages and estimate more precisely the dimension of forged part and die. The dimensional changes for the workpiece were evaluated quantatively during loading, unloading, and ejecting stages. The strains measured by the strain gages were compared with the estimated values by the FEM.

• Korea-Australia Rheology Journal
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• v.14 no.4
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• pp.161-174
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• 2002

A new technique for numerical calculation of viscoelastic flow based on the combination of Neural Net-works (NN) and Brownian Dynamics simulation or Stochastic Simulation Technique (SST) is presented in this paper. This method uses a "universal approximator" based on neural network methodology in combination with the kinetic theory of polymeric liquid in which the stress is computed from the molecular configuration rather than from closed form constitutive equations. Thus the new method obviates not only the need for a rheological constitutive equation to describe the fluid (as in the original Calculation Of Non-Newtonian Flows: Finite Elements St Stochastic Simulation Techniques (CONNFFESSIT) idea) but also any kind of finite element-type discretisation of the domain and its boundary for numerical solution of the governing PDE's. As an illustration of the method, the time development of the planar Couette flow is studied for two molecular kinetic models with finite extensibility, namely the Finitely Extensible Nonlinear Elastic (FENE) and FENE-Peterlin (FENE-P) models.P) models.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.1
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• pp.120-126
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• 2002

The near net shape forging of gears offers significant technical and economic advantages ever other forms of manufacture. These potential benefits can however only be realized by careful die design. This paper describes a computer-based methodology fur achieving this. A Visual-BASIC program has been developed on a rule based system that enables optimal design of the dies taking into account the elastic deflections generated in shrink-fitting the die inserts and that caused by the stresses generated in the forging process. The method also enables the profile of the spark erosion electrode to be determined. An example of the application to forging spur gears is given.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.7
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• pp.307-314
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• 2003

This paper presents a 3D shape optimization algorithm for electromagnetic devices using the design sensitivity analysis with finite element method. The structural deformation analysis based on the deformation theory of the elastic body under stress is used for mesh renewing. The design sensitivity and adjoint variable formulae are derived for the 3D finite element method with edge element. The results of sensitivity analysis are used as the input data of the structural analysis to calculate the relocation of the nodal points. This method makes it possible that the new mesh of analysis region can be obtained from the initial mesh without regeneration. The proposed algorithm is applied to the shape optimization of 3D electromagnet pole to net a uniform flux density at the target region.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.983-985
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• 1995

In this paper, we propose a Self Organizing Feature Map (SOFM) Type Neural Computation Algorithm for the Travelling Salesman Problem(TSP). The actual best solution to the TSP problem is computatinally very hard. The reason is that it has many local minim points. Until now, in neural computation field, Hopield-Tank type algorithm is widely used for the TSP. SOFM and Elastic Net algorithm are other attempts for the TSP. In order to apply SOFM type neural computation algorithms to the TSP, the object function forms a euclidean norm between two vectors. We propose a Largrangian for the above request, and induce a learning equation. Experimental results represent that feasible solutions would be taken with the proposed algorithm.

• Transactions of Materials Processing
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• v.12 no.7
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• pp.662-667
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• 2003

The dimensions of die and workpiece are changed continuously during loading, unloading, and ejecting stage. Finally, to predict precisely the dimension of forged part and get the die dimension for the net-shape components, the analysis of die and workpiece should be evaluated from the loading to ejecting. Therefore, the experimental and FEM analyses are performed to investigate the elastic characteristics at workpiece and die in the closed-die upsetting for ferrous material FE techniques are proposed to consider the unloading and ejecting stages and estimate more precisely the dimension of forged part and die. The dimensional changes fur the workpiece were evaluated quantatively during loading, unloading, and ejecting stages. The strains measured by the strain gages were compared with the estimated values by the FEM.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2953-2959
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• 2021

A new practical modeling of the Norton's power law creep is proposed and implemented to analyze the high temperature behaviors of Alloy 690 SG tube material. In the model, both the stress exponent n and the rate constant B are simply treated as the temperature dependent parameters. Based on the two-step optimization procedure, the temperature function of the rate constant B(T) was determined for the data set of each B value after fixing the stress exponent n value by using the prior optimized function at each temperature. This procedure could significantly reduce the numerical errors when using the power law creep equations. Based on the better description of the steady-state creep rates, the experimental rupture times could also be well predicted by using the Monkman-Grant relationship. Furthermore, the difference in tensile strengths at high temperatures could be very well estimated by assuming the imaginary creep stress related to the given strain rate after correcting the temperature effects on the elastic modulus.

• Computers and Concrete
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• v.24 no.1
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• pp.7-17
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• 2019

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.441-451
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• 2022

In the era of bigdata, scalability is a crucial issue in learning models. Among many others, the Alternating Direction of Multipliers (ADMM, Boyd et al., 2011) algorithm has gained great popularity in solving large-scale problems efficiently. In this article, we propose applying the ADMM algorithm to solve the least square problem penalized by the pairwise-difference penalty, frequently used to identify group structures among coefficients. ADMM algorithm enables us to solve the high-dimensional problem efficiently in a unified fashion and thus allows us to employ several different types of penalty functions such as LASSO, Elastic Net, SCAD, and MCP for the penalized problem. Additionally, the ADMM algorithm naturally extends the algorithm to distributed computation and real-time updates, both desirable when dealing with large amounts of data.