• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.381-394
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• 2016

Graphical model represents conditional dependencies between variables as a graph with nodes and edges. It is widely used in various fields including physics, economics, and biology to describe complex association. Conditional dependencies can be estimated from a inverse covariance matrix, where zero off-diagonal elements denote conditional independence of corresponding variables. This paper proposes a efficient BCDR (block coordinate descent with random permutation) algorithm using graphics processing units and random permutation for the CONCORD (convex correlation selection method) based on the BCD (block coordinate descent) algorithm, which estimates a inverse covariance matrix based on pseudo-likelihood. We conduct numerical studies for two network structures to demonstrate the efficiency of the proposed algorithm for the CONCORD in terms of computation times.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.147-157
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• 2015

We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.537-551
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• 2021

We conducted a study on a regression spline estimator with a few pre-specified auxiliary variables. For the implementation of the proposed estimators, we adapted a coordinate descent algorithm. This was implemented by considering a structure of the sum of the residuals squared objective function determined by the B-spline and the auxiliary coefficients. We also considered an efficient stepwise knot selection algorithm based on the Bayesian information criterion. This was to adaptively select smoothly functioning estimator data. Numerical studies using both simulated and real data sets were conducted to illustrate the proposed method's performance. An R software package psav is available.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.281-294
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• 2021

In this paper, we propose an adaptive regression method based on the single-layer neural network structure. We adopt a symmetric activation function as units of the structure. The activation function has a flexibility of its form with a parametrization and has a localizing property that is useful to improve the quality of estimation. In order to provide a spatially adaptive estimator, we regularize coefficients of the activation functions via ℓ₁-penalization, through which the activation functions to be regarded as unnecessary are removed. In implementation, an efficient coordinate descent algorithm is applied for the proposed estimator. To obtain the stable results of estimation, we present an initialization scheme suited for our structure. Model selection procedure based on the Akaike information criterion is described. The simulation results show that the proposed estimator performs favorably in relation to existing methods and recovers the local structure of the underlying function based on the sample.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.413-418
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• 2001

In this paper, we propose a new camera calibration method which is based on a fuzzy model instead of a physical camera model of the conventional method. The camera calibration is to determine the correlation between camera image coordinate and real world coordinate. The camera calibration method using a fuzzy model can not estimate camera physical parameters which can be obtained in the conventional methods. However, the proposed method is very simple and efficient because it can determine the correlation between camera image coordinate and real world coordinate without any restriction, which is the objective of camera calibration. With calibration points acquired out of experiments, 3-D real world coordinate and 2-D image coordinate are estimated using the fuzzy modeling method and the results of the experiments demonstrate the validity of the proposed method.

• Communications of the Korean Mathematical Society
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• v.17 no.1
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• pp.121-142
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• 2002

We are interested in the curve fitting problems in such a way that the sum of the squares of the orthogonal distances to the given data points is minimized. Especially, the fitting an ellipse to the given data points is a problem that arises in many application areas, e.g. computer graphics, coordinate metrology, etc. In [1] the problem of fitting ellipses was considered and numerically solved with general purpose methods. In this paper we present another new ellipse fitting algorithm. Our algorithm if mainly based on the steepest descent procedure with the view of ensuring the convergence of the corresponding quadratic function Q(u) to a local minimum. Numerical examples are given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.188-210
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• 2022

As a promising technique to support tremendous numbers of Internet of Things devices and a variety of applications efficiently, mobile edge computing (MEC) has attracted extensive studies recently. In this paper, we consider a MEC-assisted peer-to-peer (P2P) mobile communication system where MEC servers are deployed at access points to accelerate the communication process between mobile terminals. To capture the tradeoff between the time delay and the energy consumption of the system, a cost function is introduced to facilitate the optimization of the computation and communication resources. The formulated optimization problem is non-convex and is tackled by an iterative block coordinate descent algorithm that decouples the original optimization problem into two subproblems and alternately optimizes the computation and communication resources. Moreover, the MEC-assisted P2P communication system is compared with the conventional P2P communication system, then a condition is provided in closed-form expression when the MEC-assisted P2P communication system performs better. Simulation results show that the advantage of this system is enhanced when the computing capability of the receiver increases whereas it is reduced when the computing capability of the transmitter increases. In addition, the performance of this system is significantly improved when the signal-to-noise ratio of hop-1 exceeds that of hop-2.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.367-375
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• 2002

Inverse kinematics is a very useful method for control]ing the posture of an articulated body. In most inverse kinematics processes, the major matter of concern is not the posture of an articulated body itself but the position and direction of the end effector. In some applications such as 3D character animations, however, it is more important to generate an overall natural posture for the character rather than place the end effector in the exact position. Indeed, when an animator wants to modify the posture of a human-like 3D character with many physical constraints, he has to undergo considerable trial-and-error to generate a realistic posture for the character. In this paper, the Inductive Inverse Kinematics(IIK) algorithm using a Uniform Posture Map(UPM) is proposed to control the posture of a human-like 3D character. The proposed algorithm quantizes human behaviors without distortion to generate a UPM, and then generates a natural posture by searching the UPM. If necessary, the resulting posture could be compensated with a traditional Cyclic Coordinate Descent (CCD). The proposed method could be applied to produce 3D-character animations based on the key frame method, 3D games and virtual reality.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.532-533
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• 2008

연계지역 전력시장 간의 에너지 거래는 전체 전력시장의 사회적 편익을 향상시키기 위해 이루어진다. 기존의 연계지역 전력시장 간시장 최적화 문제를 다루는 중앙처리 접근방식은 경쟁적 전력시장 환경하에서 적합한 모델이 아니다. 본 논문은 연계지역 전력시장 문제를 다루기 위해 보완 라그랑지안 승수 기법(Augmented Lagrangian Relaxation) 기반의 분산처리 최적화 방법을 제시한다. Block Coordinate Descent(BCD) 분산처리 기법이 보완 라그랑지안 승수의 최적화 문제를 분리하기 위해 적용된다. 연계시장 모델을 구현한 사례연구를 통해 제시된 알고리즘의 효용성을 입증한다.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1437-1445
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• 2014

A smartphone has very limited input methods regardless of its various functions. In this respect, it is one alternative that sensor motion recognition can make intuitive and various user interface. In this paper, we recognize user's motion using acceleration sensor, magnetic field sensor, and gyro sensor in smartphone. We try to reduce sensing error by gradient descent algorithm because in single sensor it is hard to obtain correct data. And we apply vector quantization by conversion of rotation displacement to spherical coordinate system for elevated recognition rate and recognition of small motion. After vector quantization process, we recognize motion using HMM(Hidden Markov Model).