• Management Science and Financial Engineering
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• v.18 no.2
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• pp.19-22
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• 2012

In the bin-packing problem, we deal with how to pack the items by using a minimum number of bins. In the inverse bin-packing number problem, IBPN for short, we are given a list of items and a fixed number of bins. The objective is to perturb at the minimum cost the item-size vector so that all items can be packed into the prescribed number of bins. We show that IBPN is NP-hard and provide an approximation algorithm. We also consider a variant of IBPN where the prescribed solution value should be returned by a pre-selected specific approximation algorithm.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.12 no.4
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• pp.249-260
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• 2008

In this work we propose a mixed finite volume method for the Signorini problem which are based on the idea of Keller's finite volume box method. The triangulation may consist of both triangles and quadrilaterals. We choose the first-order nonconforming space for the scalar approximation and the lowest-order Raviart-Thomas vector space for the vector approximation. It will be shown that our mixed finite volume method is equivalent to the standard nonconforming finite element method for the scalar variable with a slightly modified right-hand side, which are crucially used in a priori error analysis.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.757-766
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• 2017

For implementing the proportional navigation guidance law on passive homing missiles equipped with strapdown imaging infrared seekers, the line-of-sight angles and rates with respect to the inertial frame should be estimated by carefully handling the parasitic instability effect due to the seeker's latency. By introducing a new state vector representation along with the Pade approximation for compensating the time-delay of the seeker, this paper proposes a new guidance filter structure, stochastic dynamic models and measurement equations, in three-dimensional homing problem. Then, it derives the line-of-sight angle and rate estimator in general two-dimensional engagement by applying the extended Kalman filter to the proposed structure. The estimation performance and the characteristics of the proposed filter were evaluated via a series of numerical experiments.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.561-567
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• 2010

Multiclassification is typically performed using voting scheme methods based on combining a set of binary classifications. In this paper we use multiclassification method with a hat matrix of least squares support vector machine (LS-SVM), which can be regarded as the revised one-against-all method. To tackle multiclass problems for large data, we use the $Nystr\ddot{o}m$ approximation and the quadratic Renyi entropy with estimation in the primal space such as used in xed size LS-SVM. For the selection of hyperparameters, generalized cross validation techniques are employed. Experimental results are then presented to indicate the performance of the proposed procedure.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.15-24
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• 2009

Compressed sensing addresses the recovery of a sparse vector from its few linear measurements. Recently, the success for the vector case has been extended to the matrix case. Compressed sensing of low-rank matrices solves the ill-posed inverse problem with fie low-rank prior. The problem can be formulated as either the rank minimization or the low-rank approximation. In this paper, we survey recently proposed efficient algorithms to solve these two formulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.316-321
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• 2003

Recently, support vector learning attracts great interests in the areas of pattern classification, function approximation, and abnormality detection. It is well-known that among the various support vector learning methods, the so-called no-versions are particularly useful in cases that we need to control the total number of support vectors. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and a no-version support vector learning called $\nu-SVR$. After reviewing $\varepsilon-SVR$, $\nu-SVR$, and a semi-parametric approach, this paper presents an extension of the conventional $\nu-SVR$ method toward the direction that can utilize Predetermined basis functions. Moreover, the applicability of the presented method is illustrated via an example.

• Bulletin of the Korean Mathematical Society
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• v.33 no.2
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• pp.193-204
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• 1996

Let U and V be nonempty compact subsets of two Hausdorff topological vector spaces. Suppose that a function $J : U \times V \to R$ is such that for each $\upsilon \in V, J(\cdot, \upsilon)$ is lower semi-continuous and convex on U, and for each $ u \in U, J(u, \cdot)$ is upper semi-continuous and concave on V.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.131-136
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• 2011

The original Bates-Watts framework applies only to the complete parameter vector. Thus, guidelines developed in that framework can be misleading when the adequacy of the linear approximation is very different for different subsets. The subset curvature measures appear to be reliable indicators of the adequacy of linear approximation for an arbitrary subset of parameters in nonlinear models. Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. The accuracy of outlier tests is investigated using subset curvatures.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.148-156
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• 2014

In the MVDR (minimum variance distortionless response) adaptive array, its performance could be greatly deteriorated in the presence of steering vector errors as the desired signal is treated as an interference. This paper suggests an computationally simple adaptive beamforming method which is robust against these errors. In the proposed method, a minimization problem that is formulated according to the DCB (doubly constrained beamforming) principle is solved to find a solution vector, which is in turn projected onto a subspace to obtain a new steering vector. The minimization problem and the subspace projection are dealt with using some principal eigenpairs, which are obtained using a modified PASTd(projection approximation subspace tracking with deflation). We improve the existing MPASTd(modified PASTd) algorithm such that the computational complexity is reduced. The proposed beamforming method can significantly reduce the complexity as compared with the conventional ones directly eigendecomposing an estimate of the corelation matrix to find all eigenvalues and eigenvectors. Moreover, the proposed method is shown, through simulation, to provide performance improvement over the conventional ones.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.297-305
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• 2008

Recently, kernel methods have attracted great interests in the areas of pattern classification, function approximation, and anomaly detection. The role of the kernel is particularly important in the methods such as SVM(support vector machine) and KPCA(kernel principal component analysis), for it can generalize the conventional linear machines to be capable of efficiently handling nonlinearities. This paper considers the problem of combining radar and rain gauge observations utilizing the regression approach based on the kernel-based gaussian process and support vector learning. The data-assimilation results of the considered methods are reported for the radar and rain gauge observations collected over the region covering parts of Gangwon, Kyungbuk, and Chungbuk provinces of Korea, along with performance comparison.