• The Magazine of the IEIE
• /
• v.38 no.1
• /
• pp.56-67
• /
• 2011

As a result of quickly growing data, a digital transmission system is required to deal with the challenge of acquiring signals at a very high sampling rate, Fortunately, the CS (Compressed Sensing or Compressive Sensing) theory, a new concept based on theoretical results of signal reconstruction, can be employed to exploit the sparsity of the received signals. Then, they can be adequately reconstructed from a set of their random projections, leading to dramatic reduction in the sampling rate and in the use of ADC (Analog-to-Digital Converter) resources. The goal of this article is provide an overview of the basic CS theory and to survey some important compressed sensing applications in wireless communications.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.05a
• /
• pp.342-351
• /
• 2002

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.

• Proceedings of the KIEE Conference
• /
• 2003.07a
• /
• pp.125-127
• /
• 2003

The Conventional time-domain simulation of transient stability requires iterative calculation procedures to consider the saliency of generator. Recently, a non-iterative algorithm has successfully developed to take into account the generator saliency exactly with the use of $E_q'$-model. This study proposes an extended non-iterative algorithm by adopting the two-axis generator model. Given internal voltages and rotor angles of the generators, network voltages and generator currents can be directly calculated by solving a linear algebraic equation, which enables us to reduce the computation time remarkably.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.519-523
• /
• 1995

Power system is becomming more and more complex and large. Exsiting procedual programming technique can't cope with software flexibility and maintance problems. So, Object-Oriented Programming is increasingly used to solve these problems. OOP in power system analysis field has been greatly developed. This paper applies OOP in power flow analysis, and presents new algorithm which uses only a Jacobian to solve mismatch equations, and introduces new method which is different from exsisting method to store elements.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.11
• /
• pp.121-126
• /
• 2016

In recommender systems based on collaborative filtering, measuring similarity is very critical for determining the range of recommenders. Data sparsity problem is fundamental in collaborative filtering systems, which is partly solved by Jaccard coefficient combined with traditional similarity measures. This study proposes a new coefficient for improving performance of Jaccard coefficient by compensating for its drawbacks. We conducted experiments using datasets of various characteristics for performance analysis. As a result of comparison between the proposed and the similarity metric of Pearson correlation widely used up to date, it is found that the two metrics yielded competitive performance on a dense dataset while the proposed showed much better performance on a sparser dataset. Also, the result of comparing the proposed with Jaccard coefficient showed that the proposed yielded far better performance as the dataset is denser. Overall, the proposed coefficient demonstrated the best prediction and recommendation performance among the experimented metrics.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.3
• /
• pp.223-231
• /
• 1990

Optimal Power Flow (OPF) solution by the Newton's method provides a reliable and robust method to classical OPF problems. The major challenge in algorithm development is to identify the binding inequalities efficiently. This paper proposes a simple strategy to identify the binding set. From the mechanism of penalty shifting with soft penalty in trial iteration, an active binding set is identidied automatically. This paper also suggests a technique to solve the linear system whose coefficients are presented in the matrix from. This implementation is highly efficient for sparsity programming. Case studies for 3, 5, 14, 118 bus and practical TPC-190, KEPCO-306 bus systems are performed as well.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.3
• /
• pp.735-744
• /
• 2007

Support vector machine(SVM) is capable of providing a more complete description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse kernel regression(SKR) to overcome a weak point of SVM, which is, the steep growth of the number of support vectors with increasing the number of training data. The iterative reweighted least squares(IRWLS) procedure is used to solve the optimal problem of SKR with a Laplacian prior. Furthermore, the generalized cross validation(GCV) function is introduced to select the hyper-parameters which affect the performance of SKR. Experimental results are then presented which illustrate the performance of the proposed procedure.

• Journal of Information Processing Systems
• /
• v.16 no.1
• /
• pp.30-41
• /
• 2020

Lines and textures are natural properties of the surface of natural objects, and their images can be sparsely represented in suitable frames such as wavelets, curvelets and wave atoms. Based on characteristics that the curvelets framework is good at expressing the line feature and wavesat is good at representing texture features, we propose a model for the weighted sparsity constraints of the two frames. Furtherly, a multi-step iterative fast algorithm for solving the model is also proposed based on the split Bergman method. By introducing auxiliary variables and the Bergman distance, the original problem is transformed into an iterative solution of two simple sub-problems, which greatly reduces the computational complexity. Experiments using standard images show that the split-based Bergman iterative algorithm in hybrid domain defeats the traditional Wavelets framework or curvelets framework both in terms of timeliness and recovery accuracy, which demonstrates the validity of the model and algorithm in this paper.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.749-753
• /
• 1996

This paper presents a new method for first and second order eigen-sensitivity analysis of system matrix in augmented form. Eigen-sensitivity analysis provides invaluable informations in power system planning and operation. However, conventional eigen-sensitivity analysis methods, which need all the eigenvalues and eigenvectors, can not be applicable to large scale power systems due to large computer memory and computing time required. In the proposed method, all sensitivity computations for a mode are carried out using the augmented system matrix and its own eigenvalue and right & left eigenvectors. In other words sensitivity analysis for a mode does not need informations on the other eigenvalues and eigenvectors and sparsity technique can be fully utilized. Thus compuations can be done very efficiently with moderate computer memory and computing time even for large power systems. The proposed algorithm is tested for one machine infinite bus system.

• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.163-166
• /
• 1993

This paper describes an parallell computing algorithm in Gauss elimination of Jacobian matrix to large-scale power system. The structure of Jacobian matrix becomes different according to ordering method of buses. In sequential computation buses are ordered to minimize the number of fill-in in the triangulation of the Jacobian matrix. The proposed method using ND(nested dissection) ordering develops the parallelism in the Gauss elimination to have balance of computing load among processes and each processor uses the sequential computation method to preserve the sparsity of matrix.