• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.202-210
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• 2005

This paper presents a new neural network control for nonlinear bridge systems with earthquake excitation. We design multi-layer neural network controllers with a single hidden layer. The selection of an optimal number of neurons in the hidden layer is an important design step for control performance. To select an optimal number of hidden neurons, we progressively add one hidden neuron and observe the change in a performance measure given by the weighted sum of the system error and the control force. The number of hidden neurons which minimizes the performance measure is selected for implementation. A neural network was trained for mitigating vibrations of bridge systems caused by El Centro earthquake. We applied the proposed control approach to a single-degree-of-freedom (SDOF) and a two-degree-of-freedom (TDOF) bridge system. We assessed the robustness of the control system using randomly generated earthquake excitations which were not used in training the neural network. Our results show that the neural network controller drastically mitigates the effect of the disturbance.

• 상하수도학회지
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• 제29권3호
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• pp.293-303
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• 2015

Optimal design of the water supply pipe network aims to minimize construction cost while satisfying the required hydraulic constraints such as the minimum and maximum pressures, and velocity. Since considering one single design factor (i.e., cost) is very vulnerable for including future conditions and cannot satisfy operator's needs, various design factors should be considered. Hence, this study presents three kinds of design factors (i.e., minimizing construction cost, maximizing reliability, and surplus head) to perform multi-objective optimization design. Harmony Search (HS) Algorithm is used as an optimization technique. As well-known benchmark networks, Hanoi network and Gyeonggi-do P city real world network are used to verify the applicability of the proposed model. In addition, the proposed multi-objective model is also applied to a real water distribution networks and the optimization results were statistically analyzed. The results of the optimal design for the benchmark and real networks indicated much better performance compared to those of existing designs and the other approach (i.e., Genetic Algorithm) in terms of cost and reliability, cost, and surplus head. As a result, this study is expected to contribute for the efficient design of water distribution networks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.422_423
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• 2009

DNP 3.0 is the most widely used data communication protocol for electric power control systems. KEPCO(Korea Electric Power Corporation) also uses DNP 3.0 as a data transmission protocol for the DAS(Distribution Automation System) and the SCADA system of the transmmission system. But DNP 3.0 is one of the oldest industrial standard having many restrictions and there are many considerations for optimal design of DNP 3.0 network for the Distribution Automation System.

• Smart Structures and Systems
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• 제30권6호
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(1)
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• pp.295-296
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• 2005

• 대한교통학회지
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• 제6권1호
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• pp.5-16
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• 1988

Development is given to an optimal network design method using continuous design variables. Modified Hooke-and-Jeeves algorithm is implemented in order to solve nonlinear programming problem which is approximately equivalent to the real network design problem with system efficiency crieteria and improvement cost as objective function. the method was tested for various forms of initial solution, and dimensions of initial step size of link improvements. At each searching point of evaluating the objective function, a link flow problem was solved with user equilibrium principles using the Frank-Wolfe algorithm. The results obtained are quite promising interms fo numbers of evaluation, and the speed of convergence. Suggestions are given to selections of efficient initial solution, initial step size and convergence criteria. An approximate method is also suggested for reducing computation time.

• 한국정밀공학회지
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• 제20권7호
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• pp.36-43
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• 2003

In this paper, the warm forging process sequence has been determined to manufacture the warm forged product for the precision bevel gear used as the differential gear unit of a commercial automobile. The preform shape of bevel gear influences the dimensional accuracy and stiffness of final product. So, the design parameters related preform shape such as aspect ratio and chamfer length having an influence the formability of forged product are analyzed. Then the optimal conditions of design parameters have been selected by artificial neural network (ANN). Finally, to verify the optimal preform shape, the experiments of the warm forging of the bevel gear have been executed. The proposed method can give more systematic and economically feasible means for designing preform shape in metal forming process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2873-2875
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• 2005

The optimal structure of the conventional Fuzzy Polynomial Neural Networks (FPNN)[3] depends on experience of designer. For the conventional Fuzzy Polynomial Neural Networks, input variable number, number of input variable, number of Membership Functions(MFs) and consequence structures are selected through the experience of a model designer iteratively. In this paper, we propose the new design methodology to find the optimal structure of Fuzzy Polymomial Neural Network by using Genetic Algorithms(GAs)[4, 5]. In the sequel, It is shown that the proposed Advanced Genetic Algorithms based Fuzzy Polynomial Neural Network(Advanced GAs-based FPNN) is more useful and effective than the existing models for nonlinear process. We used Medical Imaging System(MIS)[6] data to evaluate the performance of the proposed model.

• 한국지반공학회논문집
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• 제21권9호
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• pp.25-34
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• 2005

본 연구에서는 최적의 인공신경망 구조 설계를 위하여 인공신경망과 유전자 알고리즘이 결합된 신경망구조 설계기법이 제안되었다. 저자들은 신경망 구조설계시 인공지능 적용에 따른 계산적인 복잡함을 줄이며, 신경망에 의한 예측의 정확성을 증가시키기 위하여 인공신경망과 유전자 알고리즘의 특성을 조합하였다. 최적의 신경망 구조를 얻기 위하여 신경망 구조의 설계변수들에 대한 유전자 선별기법을 적용하였다. 제안된 합성 기법의 적용성을 평가하기 위하여 여러 지반공학 물성치들을 추정하는 해석에 적용되었다.

• 대한교통학회지
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• 제19권1호
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• pp.89-99
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• 2001

기존의 고정수요(Fixed Demand)를 전제로 한 가로망 설계 모형에서는 가로망의 구조나 용량이 개선되더라도 장래 기·종점 통행수요는 변하지 않는다고 가정한다. 이는 단기적인 가로망 설계에서는 성립할 수 있지만, 현실적으로 기·종점 통행수요는 네트워크 서비스수준에 따라 변화하므로 고정수요를 전제한 장기적인 가로망 설계문제에서는 그 타당성을 잃어버린다 그러므로 장래 최적 가로망 설계는 현실적 여건과 교통특성상 기·종점 통행 수요가 모형 내부에서 결정되는 내생변수로 처리하는 가변수요(Variable Demand)를 반영한 가로망 설계 문제로 모형을 구축하는 것이 바람직하다. 이러한 맥락에서 본 논문은 가변수요를 갖는 가로망 설계문제에 대한 이중계층 모형을 구축한 다음, 가로망내의 특성치가 변화하였을 때 그 파급영향을 먼저 파악하고 현 가로망 개선에서 가장 먼저 고려해야 할 링크를 찾아내기 위해 민감도 분석을 수행하였고, 민감도 분석과 연관되어 전체 시스템 효과척도를 최적화할 수 있는 대안적인 알고리즘을 제시하고 적용하여 구축된 모형으로 그 유효성을 검증하였고, 기존 고정수요 가로망 설계기법에 내재된 한계점을 극복하고자 하였다.