• Journal of Environmental Science International
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• v.4 no.1
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• pp.71-80
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• 1995

A discrete state space model for a multiple-reach river system is formulated using the dynamics of biochemical oxygen demand(BOD) and dissolved oxygen(DO). A hierarchical optimization technique, which is applicable to large-scale systems with time-delays in states, is also described to control stream quality in a river as an optimal manner based on the interaction prediction method. The steady state tracking error of the proposed method is determined analytically and a necessary and sufficient condition on which a constant target tracking problem has zero steady-state error is derived. Computer simulations for the river pollution model illustrate the algorithm.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.185-189
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• 1987

An analysis and design of large-scale linear multivariable systems often requires to be block triangularized form for good sensitivity of the systems when their poles and zeros are varied. But the decomposition algorithms presented up to now need a procedure of permutation, rescaling and a solution of nonlinear algebraic equations, which are usually burden. To avoid these problem, in this paper we develop a newly alternative block triangular decomposition algorithm which used the generalized matrix sign function on the Z-plane. Also, the decomposition algorithm demonstrated using the fifth order linear model of a distillation tower system.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.714-716
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• 1996

This paper presents an optimization method for optimal reactive power dispatch which minimizes real power loss and improves voltage profile of power systems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP). and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods were applied to the IEEE 30-bus system. Each simulation result, compared with that obtained by using a conventional gradient-based optimization method, Sequential Quadratic Programming (SQP), shows the possibility of applications of evolutionary computation to large scale power systems.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.326-328
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• 1996

The 10kW or 1MW model of a photovoltaic array written in PSPICE is presented in this paper. A problem with this large scale centralized photovoltaic system is the decrease of power due to the resistance of cable connecting individual subarray with inverter. In this paper, we analyzed the relationship between the resistance of cable and subarray output power of 1MW photovoltaic system by the PSPICE modeling. As a result of simulation, we can proved that photovoltaic array output power is limitted by the resistance of cable.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.37-50
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• 2021

Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.3
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• pp.145-156
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• 2002

Business managers are often required to use LP problems to deal with uncertainty inherent in decision making due to rapid changes in today＇s business environments. Uncertain parameters can be easily formulated in the two-stage stochastic LP problems. However, since solution methods are complex and time-consuming, a common approach has been to use modified formulations to provide upper and lower bounds on the two-stage stochastic LP problem. One approach is to use an expected value problem, which provides upper and lower bounds. Another approach is to use “walt-and-see” problem to provide upper and lower bounds. The objective of this paper is to propose a modified approach of “wait-and-see” problem to provide an upper bound and to compare the relative error of optimal value with various upper and lower bounds. A computing experiment is implemented to show the relative error of optimal value with various upper and lower bounds and computing times.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.105-114
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• 2000

Job Shop Problem which consists of the m different machines and n jobs is a NP-hard problem of the combinatorial optimization. Each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. Each machine can process at most one operation at a time. The purpose of this paper is to develop the heuristic method to solve large scale scheduling problem using Constraint Satisfaction Problem method and Simulated Annealing. The proposed heuristic method consists of the search algorithm and optimization algorithm. The search algorithm is to find the solution in the solution space using CSP concept such as backtracking and domain reduction. The optimization algorithm is to search the optimal solution using SA. This method is applied to MT06, MT10 and MT20 Job Shop Problem, and compared with other heuristic method.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.113-116
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• 1995

In this study, we formulate production costing problem with environmental and operational constraints into an optimization problem of LP form. In the process of formulation, auxiliary constraints on which reflect unit loading order are constructed to reduce the size of optimization problem by economic operation rules. As a solution of the optimization problem in LP form, we use Karmarkar's method which performs much faster than simplex method in solving large scale LP problem. The proposed production costing algorithm is applied to IEEE Reliability Test System, and performs production simulation under environmental and operational constraints. Test and computer results are given to show the accuracy and usefulness of the proposed algorithm in the field of power system planning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5287-5303
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• 2018

Handling vast amount of data found in large transactional datasets is an obvious challenge for the conventional data mining algorithms. Addressing this challenge, our paper proposes a parallel approach for proper decomposition of mining problem into sub-problems in order to find frequent patterns from these datasets. The proposed, Pattern Mining for Large Distributed Dataset (PMLDD) approach, ensures minimum dependencies as well as minimum communications among sub-problems. It establishes a linear aggregation of the intermediate results so that it can be adapted to large-scale programming models like MapReduce. In this context, an algorithmic structure for MapReduce programming model is presented. PMLDD guarantees an efficient load balancing among the sub-problems by a specific selection criterion. Further, it optimizes the number of required iterations over the dataset for mining frequent patterns as compared to the existing approaches. Finally, we believe that our approach is scalable enough to handle larger datasets in terms of performance evaluation, and the result analysis justifies all these mentioned concerns.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.263-269
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• 2016

The purpose of this study is to challenge a computational regression-type problem, that is handling large-size data, in which conventional metamodeling techniques often fail in a practical sense. To solve such problems, regression-type boosting, one of ensemble model techniques, together with bootstrapping-based re-sampling is a reasonable choice. This study suggests weight updates by the amount of the residual itself and a new error decision criterion which constructs an ensemble model of models selectively chosen by rejection limits. Through these ideas, we propose AdaBoost.RMU.R as a metamodeling technique suitable for handling large-size data. To assess the performance of the proposed method in comparison to some existing methods, we used 6 mathematical problems. For each problem, we computed the average and the standard deviation of residuals between real response values and predicted response values. Results revealed that the average and the standard deviation of AdaBoost.RMU.R were improved than those of other algorithms.