• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.540-549
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• 2018

This paper proposes the method of active distribution network expansion planning considering distributed generation integration and distribution network reconfiguration. The distribution network reconfiguration is taken as the expansion planning alternative with zero investment cost of the branches. During the process of the reconfiguration in expansion planning, all the branches are taken as the alternative branches. The objective is to minimize the total costs of the distribution network in the planning period. The expansion alternatives such as active management, new lines, new substations, substation expansion and Distributed Generation (DG) installation are considered. Distribution network reconfiguration is a complex mixed-integer nonlinear programming problem, with integration of DGs and active managements, the active distribution network expansion planning considering distribution network reconfiguration becomes much more complex. This paper converts the dual-level expansion model to Second-Order Cone Programming (SOCP) model, which can be solved with commercial solver GUROBI. The proposed model and method are tested on the modified IEEE 33-bus system and Portugal 54-bus system.

• IE interfaces
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• v.9 no.3
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• pp.202-213
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• 1996

Video-on-demand is an interactive service that provides programs (movie, home shopping, etc.) to users connected to a network. This service will require high bandwidth network and video servers with a large amount of storage capacity. From the viewpoint of system analysis, there are optimization problems to be solved. In this paper, we present a dynamic programming method for allocating the storage for programs being served in a multi-level video-on-demand network. In the optimization of the network resource, we consider the three kinds of costs: installation cost for video servers, program storage cost, and transmission (or communication) cost. The factors related to the costs are investigated. An example is shown to illustrate the proposed method.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.1
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• pp.25-32
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• 1981

The optimum design problem of a proposed oil pipeline network has been formulated as a zero-one programming model to determine the optimum sizes of pipe and pump which minimize the sum of material costs and operating costs during the 20 years of life span. Applying to a real situation, the problem constitutes an assignment type zero-one programming with 372 zero-one variables and 13 constraints. A heuristic algorithm has been developed based on the modified Petersen algorithm utilizing the special form of the activity matrix. The results showed impressive cost savings of 37 percent of the total cost from the original proposal.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.490-495
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• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.899-902
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• 1993

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.

• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.23-29
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• 2009

This paper presents the development of embedded network educational system. This is an educational equipment which enables user to have training over Network Configuration and Embedded network programming practice on Internet environment. The network education system is developed on embedded environment. based on using ethernet interface. On the development environment. PAX255 VLSI chip is used for the processor, the ADSv1.2 for debugging, uC/OS276 for RTOS. The system software was developed using C language. The ping program provided an educational environment for the student to compile and load it to run after doing practice of demonstration behavior. Afterwards programming procedure starts the step-by-step training just like the demonstration function. In other words, programming method how to design the procedure of ARP operation and ICMP operation is explained.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.302-305
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• 1996

This paper presents a visual servoing combined by Neural Network with optimal structure and predictive control for robotic manipulators to tracking or grasping of the moving object. Using the four feature image information from CCD camera attached to end-effector of RV-M2 robot manipulator having 5 dof, we want to predict the updated position of the object. The Kalman filter is used to estimate the motion parameters, namely the state vector of the moving object in successive image frames, and using the multi layer feedforward neural network that permits the connection of other layers, evolutionary programming(EP) that search the structure and weight of the neural network, and evolution strategies(ES) which training the weight of neuron, we optimized the net structure of control scheme. The validity and effectiveness of the proposed control scheme and predictive control of moving object will be verified by computer simulation.

• Journal of the military operations research society of Korea
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• v.18 no.2
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• pp.66-95
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• 1992

Shortest paths are one of the simplest and most widely used concepts in deterministic networks. A decison of troops movement route can be analyzed in the network with a shortest path algorithm. But in reality, the value of arcs can not be determined in the network by crisp numbers due to imprecision or fuzziness in parameters. To account for this reason, a fuzzy network should be considered. A fuzzy shortest path can be modeled by general fuzzy mathematical programming and solved by fuzzy dynamic programming. It can be formulated by the fuzzy network with lingustic variables and solved by the Klein algorithm. This paper focuses on a revised fuzzy shortest path algorithm and an application is discussed.

• Korean Management Science Review
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• v.20 no.1
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• pp.65-76
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• 2003

The increased environmental concerns and the emphasis on recycling are gradually shifting the orientation of municipal solid waste (MSW) management. This paper is designed to evaluate regionalization programs for MSW management system. We developed a mixed intiger network programming (MIP) model to identify environment-friendly, cost-effective expansion plans for regionalization scenarios considered. The MIP model is a dynamic capacity expansion model based on the network flow model that depicts the MSW management cycle. In particular, our model is designed to determine the optimal form of regionalization using binary variables. We apply this model to assess the regionalization program of Seoul Metropolitan City, which includes three scenarios such as 1) districting, 2) regionalization with neighboring self-governing districts, and 3) g1obalization with all districts. We demonstrate how our model can be used to plan the MSW system. The results indicate that optimal regionalization with nearby self-governing districts can eliminate unnecessary landfills and expansions if jurisdictional obstacles are removed.