• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1046-1048
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• 1999

This Paper compares two mathematical decomposition coordination methods to implementing the distributed optimal Power flow(OPF) using the regional decomposition: the Auxiliary Problem Principle(APP) and the Alternating Direction Method(ADM), a variant of the conventional Augmented Lagrangian approach. A case study was performed with IEEE 50-bus system.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.171-173
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• 2002

This paper presents a power system decomposition technique for digital simulation of large power system. To decompose power system, distributed transmission line model is used. But this model can be used only for long transmission lines. In this paper, capacitor compensation method is proposed to use distributed transmission line model for short transmission line. And case study shows proposed method can be used for effective power system decompositon in digital simulation of large power system.

• Journal of KSNVE
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• v.9 no.3
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• pp.525-534
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• 1999

The present paper proposes a generalized modal analysis procedure for non-uniform, distributed-parameter rotor-bearing systems. An exact element matrix is derived for a Timoshenko shaft model which contains rotary inertia, shear deformation, gyroscopic effect and internal damping. Complex coordinates system is adopted for the convenience in formulation. A generalized orthogonality condition is provided to make the modal decomposition possible. The generalized modal analysis by using a modal decomposition delivers exact and closed form solutions both for frequency and time responses. Two numerical examples are presented for illustrating the proposed method. The numerical study proves that the proposed method is very efficient and useful for the analysis of distributed-parameter rotor-bearing systems.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.182-186
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• 1997

This paper presents a mathematical approach to implementing distributed optimal power flow (OPF), wherein a regional decomposition technique is adopted to parallelize the OPF. Three mathematical decomposition coordination methods are introduced firs to implement the proposed distributed scheme: the Auxiliary Problem Principle (APP), the Predictor-Corrector Proximal Multiplier Method (PCPM), and the Alternating Direction Method (ADM). Then two alternative schemes for modeling distributed OPF are introduced; the Dummy Generator-Dummy Generator (DGDG) scheme and Dummy Generator-Dummy Load (DGDL) scheme. We present the mathematical analyses of the proposed approach, and demonstrate the approach on several test, systems, including IEEE Reliability Test Systems and parts of the ERCOT (Electric Reliability Council of Texas) system.

• Journal of KIISE
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• v.45 no.3
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• pp.280-287
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• 2018

Recently, the use of a recommendation system and tensor data analysis, which has high-dimensional data, is increasing, as they allow us to analyze the tensor and extract potential elements and patterns. However, due to the large size and complexity of the tensor, it needs to be decomposed in order to analyze the tensor data. While several tools are used for tensor decomposition such as rTensor, pyTensor, and MATLAB, since such tools run on a single machine, they are unable to handle large data. Also, while distributed tensor decomposition tools based on Hadoop can handle a scalable tensor, its computing speed is too slow. In this paper, we propose S-PARAFAC, which is a tensor decomposition tool based on Apache Spark, in distributed in-memory environments. We converted the PARAFAC algorithm into an Apache Spark version that enables rapid processing of tensor data. We also compared the performance of the Hadoop based tensor tool and S-PARAFAC. The result showed that S-PARAFAC is approximately 4~25 times faster than the Hadoop based tensor tool.

• ETRI Journal
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• v.38 no.4
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• pp.746-756
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• 2016

Owing to the intermittent power generation of renewable energy sources (RESs), future wireless cellular networks are required to reliably aggregate power from retailers. In this paper, we propose a distributed power allocation (DPA) scheme for base stations (BSs) powered by retailers with heterogeneous RESs in order to deal with the unreliable power supply (UPS) problem. The goal of the proposed DPA scheme is to maximize our well-defined utility, which consists of power satisfaction and unit power costs including added costs as a non-subscriber, based on linear and quadratic cost models. To determine the optimal amount of DPA, we apply dual decomposition, which separates the master problem into sub-problems. Optimal power allocation from each retailer can be obtained by iteratively coordinating between the BSs and retailers. Finally, through a mathematical analysis, we show that the proposed DPA can overcome the UPS for BSs powered from heterogeneous RESs.

• Journal of the Korean Operations Research and Management Science Society
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• v.15 no.1
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• pp.23-35
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• 1990

A mathematical model for a dynamic capacitate facility location problem is formulated by a mixed integer problem. The objective of the model is to minimize total discounted costs that include fixed charges and distributed costs. The Cross Decomposition method of Van Roy is extended and applied to solve the dynamic capacitated facility location problem. The method unifies Benders Decomposition and Lagrangean relaxation into a single framework. It successively solves a transportation problem and a dynamic uncapacitated facility location problem as two subproblems. Computational results are compared with those of general mixed integer programming.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.156-159
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• 2008

Proper orthogonal decomposition is a statistical pattern analysis technique for finding the dominant components, called the proper orthogonal modes, in ensembles of spatially distributed data. We present recent ideas based on proper orthogonal decomposition (POD) and detailed experiments that yield new perspectives into the microscale structures. The linearized modeling technique based on POD is very useful to show the principal characteristics of the complex dynamic responses.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.2
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• pp.161-197
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• 2020

Total variation minimization is standard in mathematical imaging and there have been numerous researches over the last decades. In order to process large-scale images in real-time, it is essential to design parallel algorithms that utilize distributed memory computers efficiently. The aim of this paper is to illustrate recent advances of domain decomposition methods for total variation minimization as parallel algorithms. Domain decomposition methods are suitable for parallel computation since they solve a large-scale problem by dividing it into smaller problems and treating them in parallel, and they already have been widely used in structural mechanics. Differently from problems arising in structural mechanics, energy functionals of total variation minimization problems are in general nonlinear, nonsmooth, and nonseparable. Hence, designing efficient domain decomposition methods for total variation minimization is a quite challenging issue. We describe various existing approaches on domain decomposition methods for total variation minimization in a unified view. We address how the direction of research on the subject has changed over the past few years, and suggest several interesting topics for further research.

• Journal of KIISE:Software and Applications
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• v.27 no.8
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• pp.795-803
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• 2000

This paper presents a shared data decomposition model for improving concurrency in multi-user, distributed software developments. In our model, the target software system is decomposed into the independent components based on project roles to be distributed over clients. The distributed components are decomposed into view objects and core objects to replicate only view objects in a distributed collaboration session. The core objects are kept in only one client and the locking is used to prevent inconsistencies. The grain size of a lock is a role instead of a class which is commonly used as the locking granularity in the existing systems. The experimental result shows that our model reduces response time by 12${\sim}$18% and gives good scalability.