• Structural Engineering and Mechanics
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• v.51 no.6
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• pp.989-1003
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• 2014

In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determine optimal members' sizes and the investigation of the parameters required to minimize the differential column shortening are computational effort consuming, especially when considering various types of loads such as dead load, temperature action, time dependent effects, construction and live loads. Because the desired accuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neural networks have been proposed in order to approximate this information. Instead of using a commercial software package, many references providing advanced principles have been considered to code a program and generate these neural networks. The first one predicts the typical amount of time between two phases, needed to achieve the minimum maximorum differential column shortening. The other network aims to prognosticate sequential analysis results from those of the simultaneous analysis. After the training stages, testing procedures have been carried out in order to ensure the generalization ability of these respective systems. Numerical cases are studied in order to find out how good these ANN match with the sequential finite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in the preliminary design stage.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.547-549
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• 2007

Recently with increasing the use of parallel computing and cluster system which was connected high speed network, the interest about distributed and parallel file system is increasing. Specially, there are many researches, which focused on optimizing the performance of distributed and parallel file system for the more efficient use of cluster system. In this paper, we analyzed the performance of PVFS(Parallel Virtual File System) in small cluster system. In addition, to improve the PVFS performance we proposed the chancing the size of flow buffer according to the network speed and we optimized the PVFS performance on small cluster system.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.3
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• pp.120-132
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• 2008

Networks-on-Chip (NoC) is emerging as a practical development platform for future systems-on-chip products. We propose an energy-efficient static algorithm which optimizes the energy consumption of task communications in NoCs with voltage scalable links. In order to find optimal link speeds, the proposed algorithm (based on a genetic formulation) globally explores the design space of NoC-based systems, including network topology, task assignment, tile mapping, routing path allocation, task scheduling and link speed assignment. Experimental results show that the proposed design technique can reduce energy consumption by 28% on average compared with existing techniques.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.537-540
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• 2000

The Distribution System consist of many tie-line switches and sectionalizing switches, operated a radial type. When an outage occurs in Distribution System, outage areas are isolated by system switches, has to restored as soon as possible. At this time, system operator have to get a information about network topology for service restoration of outage areas. Therefore, the searching result of restorative topology has to fast computation time and reliable result topology for to restore a electric service to outage areas, equal to optimal switching operation problem. So, the problem can be defined as combinatorial optimization problem. The service restoration problem is so important problem which have outage area minimization, outage loss minimization. Many researcher is applying to the service restoration problem with various techniques. In this paper, advanced Dijkstra algorithm is applied to searching a restoration topology, is so efficient to searching a shortest path in graph type network. Additionally, fuzzy rules and operator are applied to overcome a fuzziness of correlation with input data. The present technique has superior results which are fast computation time and searching results than previous researches, demonstrated by example distribution model system which has 3 feeders, 26 buses. For a application capability to real distribution system, additionally demonstrated by real distribution system of KEPCO(Korea Electric Power Corporation) which has 8 feeders and 140 buses.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.215-220
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• 2007

최근 센서 네트워크에서 에너지 효율성을 위한 다양한 연구가 진행 중이다. 특히 센서 노드의 저전력을 위해서는 센서 네트워크에서 전송되는 데이터의 횟수나 전송량을 최소한으로 줄이면서 효율적이면서 신뢰성을 가지는 질의에 대한 결과를 얻을 수 있어야 한다. 본 연구에서는 해양 센서 네트워크 상에서 데이터의 전송량을 줄일 수 있는 SDMTree(Sensing Data Management Tree)를 제안한다. 제안된 SDMTree는 질의 최적화를 위해 질의 처리기 구성 요소로 도입 가능하다. 해양 센서 네트워크에서 in-network 각 4레벨에서 하위 노드로부터 받은 데이터를 병합, 관리하기 위한 방법으로 데이터를 속성별로 구분하여 중복된 데이터를 제거하여 트리형태로 구성되기 때문에 질의에 대한 응답에 해당하는 데이터 검색시 정확하고 신속하게 처리할 수 있으며, 트리 구성 또한 중복 데이터 및 중복 영역을 배제하여 구성되므로, 상위노드가 하위 노드로부터 센싱 데이터를 수집하여 저장하기 위한 에너지와 상위 노드에서 하위 노드로 질의를 전송시 질의에 해당하는 특정 영역에만 질의를 전송할 수 있기 때문에 데이터 저장 및 통신에 소모되는 불필요한 에너지를 최대한 줄일 수 있다.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.526-538
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• 2007

This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.10
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• pp.66-74
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• 1999

In this paper, we propose a generalized model of statistical Hopfield neural network applicable to solving the well known NP-Complete problem, TSP. Van Den Bout's method to simplify the energy function through normalization has severe weak points that it does not consider the necessary perturbation effects. In proposed model, the improved energy function is used and 5 kinds of perturbation effects and the ratio between perturbation effects are considered including van Den Bout's 2 kinds and one more kind of Park. Through the simulation of randomly generated distribution of 10-city, it is found that our model shows 90 out of 100 cases reach the optimum and near optimum solution(within 5% error). We show the simulation of the large scale, 30-city and 50-city.

• KIEAE Journal
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• v.17 no.4
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• pp.83-88
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• 2017

Purpose: The objective of this study is to develop a predictive model for calculating the amount of cooling load for the different setback temperatures during the setback period. An artificial neural network (ANN) is applied as a predictive model. The predictive model is designed to be employed in the control algorithm, in which the amount of cooling load for the different setback temperature is compared and works as a determinant for finding the most energy-efficient optimal setback temperature. Method: Three major steps were conducted for proposing the ANN-based predictive model - i) initial model development, ii) model optimization, and iii) performance evaluation. Result:The proposed model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results (Mi) and the predicted results (Si) under generally accepted levels. In conclusion, the ANN model presented its applicability to the thermal control algorithm for setting up the most energy-efficient setback temperature.

• Wind and Structures
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• v.18 no.3
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• pp.253-265
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• 2014

The platform and floating structure of spar type offshore wind turbine systems should be designed in order for the 6-DOF motions to be minimized, considering diverse loading environments such as the ocean wave, wind, and current conditions. The objective of this study is to optimally design the platform and substructure of a 3MW spar type wind turbine system with the maximum postural stability in 6-DOF motions as well as the minimum material cost. Therefore, design variables of the platform and substructure were first determined and then optimized by a hydrodynamic analysis. For the hydrodynamic analysis, the body weight of the system was considered, and the ocean wave conditions were quantified to the wave forces using the Morison's equation. Moreover, the minimal number of computation analysis models was generated by the Design of Experiments (DOE), and the design variables of the platform and substructure were finally optimized by using a genetic algorithm with a neural network approximation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.31-39
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• 2011

For rapid formed the ad hoc wireless backbone network in disaster scene, It is necessary for real-time deployment scheme of wireless ad hoc relay devices by first responders without pre-planning. However, in order to realize this scheme, redundant deployment should be minimized, as well as optimal location of relay devices should be selected to expand communication coverage. Therefore, in this paper, we propose a new deployment scheme of relay devices to optimize communication coverage and then through simulations showed that improved performance of algorithm.