• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.4 s.316
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• pp.1-7
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• 2007

When the sensor geometry is poor, the user position estimate obtained by of GN (Gauss-Newton) method is often diverged in radio navigation. In other to avoid divergence problem QCLS (Quadratic Correction Least Square) method using TDOA (Time Difference of Arrival) measurements is introduced, but the estimation error is somewhat large. This paper presents the modified QCLS method using weighted least square. Since the weighting matrix is influenced by the unknown user position, two-step approach is employed in the proposed method. The weighting matrix is estimated in the first step using least square, and then find user position is obtained using weighted least square. Simulation results show that the performance of the proposed method is superior to the conventional QCLS all over the workspace.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.121-144
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• 2005

In this paper, we propose unconstrained and constrained posynomial Geometric Programming (GP) problem with negative or positive integral degree of difficulty. Conventional GP approach has been modified to solve some special type of GP problems. In specific case, when the degree of difficulty is negative, the normality and the orthogonality conditions of the dual program give a system of linear equations. No general solution vector exists for this system of linear equations. But an approximate solution can be determined by the least square and also max-min method. Here, modified form of geometric programming method has been demonstrated and for that purpose necessary theorems have been derived. Finally, these are illustrated by numerical examples and applications.

• 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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.2
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• pp.75-80
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• 2004

Vehicle structures are composed of many substructure connected to one another by various types of mechanical joints. In vehicle engineering it is important to study these connected structures under various dynamic forces for the evaluations of fatigue life and stress concentration exactly. It is difficult to obtain the accurate load history of specified positions because of the errors such as modeling, measurement and etc. In the beginning of design exact load data are actually necessary for the fatigue strength and life analysis to minimize the cost and time of designing. In this paper, the procedure of practical dynamic force determination is developed by the combination of the principal stresses of F. E. Analysis and experiment. Least square pseudo inverse matrix is adopted to obtain in inverse matrix of analyzed stresses matrix. The error minimization method utilizes the inaccurate measured error and the shifting error that the whole data is stiffed over real data. The least square criterion is adopted to avoid these non. Finally, to verify the proposed procedure, a bus is analyzed. This measurement and prediction technology can be extended to the structural modification of any geometric shape in complex structure.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.272-276
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• 2009

In this paper, the arc region of a gas circuit breaker (GCB) is analyzed using the fast moving least square reproducing kernel method (FMLSRKM) which can simultaneously calculate an approximated solution and its derivatives. For this problem, an axisymmetric and inhomogeneous formulation of the FMLSRKM is used and applied. The field distribution obtained by the FMLSRKM is compared to that of the finite element method. Then, a whole breaking period of a GCB is simulated, including analysis of the arc gas flow by finite volume fluid in the cell, and the electric field of the arc region using the FMLSRKM.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.14-15
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• 2006

A saturation of magnetic flux in the core may occur when a large primary current flows when the iron-cored current transformer is used. This saturation makes the distorted secondary current of the CT. the distorted secondary current may cause the mal-operation or operation time delay of protective relays. CT compensation algorithm using The LSQ(Least Square) fitting method has a problem. It needs to acquire enough data for executing this algorithm without an error. In this paper, an enhanced algorithm using interpolation based LSQ(Least Square) Fitting Method is proposed. The Lagrange Interpolation Method is used for the interpolation and CT is simulated by EMTP. The results show that the proposed algorithm can accurately compensate a distorted secondary current more than existing Algorithm when the saturation severely occurs.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.683-687
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• 2007

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.994-996
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• 2003

The Response Surface Methodology is frequently used for building an approximation model. However, its approximation errors often occur in engineering problem, because of the use of the Least Square Method. Therefore, this paper introduces the Moving Least Square Method to obtain the more accurate Response Surface Model, and then the detent force of a Permanent Magnet Linear Synchronous Motor is applied to verify the accuracy of the introduced method.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.5
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• pp.89-96
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• 2012

The Falling Weight Deflectormeter has been widely used in evaluating the structural adequacy of pavement structures. The deflections measured from the FWD are capable of estimating the stiffness of pavement layers and measuring the pavement responses in the pavement structure. The objective of paper is to develop the pavement response model using a partial least square regression technique based on the FWD deflection data. The partial least square regression method enables to solve the multicollinearity problem occurred in multiple regression model. It is also found that the pavement response model can be developed using the raw data when a partial least square regression was used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3543-3557
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• 2017

Compression is a very important technique for remotely sensed hyperspectral images. The lossless compression based on the recursive least square (RLS), which eliminates hyperspectral images' redundancy using both spatial and spectral correlations, is an extremely powerful tool for this purpose, but the relatively high computational complexity limits its application to time-critical scenarios. In order to improve the computational efficiency of the algorithm, we optimize its serial version and develop a new parallel implementation on graphics processing units (GPUs). Namely, an optimized recursive least square based on optimal number of prediction bands is introduced firstly. Then we use this approach as a case study to illustrate the advantages and potential challenges of applying GPU parallel optimization principles to the considered problem. The proposed parallel method properly exploits the low-level architecture of GPUs and has been carried out using the compute unified device architecture (CUDA). The GPU parallel implementation is compared with the serial implementation on CPU. Experimental results indicate remarkable acceleration factors and real-time performance, while retaining exactly the same bit rate with regard to the serial version of the compressor.