• Title/Summary/Keyword: network optimization

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Design of GBSB Neural Network Using Solution Space Parameterization and Optimization Approach

  • Cho, Hy-uk;Im, Young-hee;Park, Joo-young;Moon, Jong-sup;Park, Dai-hee
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.1 no.1
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    • pp.35-43
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    • 2001
  • In this paper, we propose a design method for GBSB (generalized brain-state-in-a-box) based associative memories. Based on the theoretical investigation about the properties of GBSB, we parameterize the solution space utilizing the limited number of parameters sufficient to represent the solution space and appropriate to be searched. Next we formulate the problem of finding a GBSB that can store the given pattern as stable states in the form of constrained optimization problems. Finally, we transform the constrained optimization problem into a SDP(semidefinite program), which can be solved by recently developed interior point methods. The applicability of the proposed method is illustrated via design examples.

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Design Optimization of a Staggered Dimpled Channel Using Neural Network Techniques (신경회로망기법을 사용한 엇갈린 딤플 유로의 최적설계)

  • Shin, Dong-Yoon;Kim, Kwang-Yong
    • The KSFM Journal of Fluid Machinery
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    • v.10 no.3 s.42
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    • pp.39-46
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    • 2007
  • This study presents a numerical procedure to optimize the shape of staggered dimple surface to enhance turbulent heat transfer in a rectangular channel. The RBNN method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport (SST) turbulence model. The dimple depth-to-dimple print diameter (d/D), channel height-to-dimple print diameter ratio (H/D), and dimple print diameter-to-pitch ratio (D/S) are chosen as design variables. The objective function is defined as a linear combination of heat transfer related term and friction loss related term with a weighting factor. Latin Hypercube Sampling (LHS) is used to determine the training points as a mean of the design of experiment. The optimum shape shows remarkable performance in comparison with a reference shape.

Adaptive Cutting Parameter Optimization Applied to Face Milling Operations (면삭 밀링공정에서의 절삭조건의 적응 최적화)

  • 고태조;조동우
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.19 no.3
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    • pp.713-723
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    • 1995
  • In intelligent machine tools, a computer based control system, which can adapt the machining parameters in an optimal fashion based on sensor measurements of the machining process, should be incorporated. In this paper, the technology for adaptively optimizing the cutting conditions to maximize the material removal rate in face milling operations is proposed using the exterior penalty function method combined with multilayered neural networks. Two neural networks are introduced ; one for estimating tool were length, the other for mapping input and output relations from experimental data. Then, the optimization of cutting conditions is adaptively implemented using tool were information and predicted process output. The results are demonstrated with respect to each level of machining such as rough, fine and finish cutting.

An Effective Experimental Optimization Method for Wireless Power Transfer System Design Using Frequency Domain Measurement

  • Jeong, Sangyeong;Kim, Mina;Jung, Jee-Hoon;Kim, Jingook
    • Journal of electromagnetic engineering and science
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    • v.17 no.4
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    • pp.208-220
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    • 2017
  • This paper proposes an experimental optimization method for a wireless power transfer (WPT) system. The power transfer characteristics of a WPT system with arbitrary loads and various types of coupling and compensation networks can be extracted by frequency domain measurements. The various performance parameters of the WPT system, such as input real/imaginary/apparent power, power factor, efficiency, output power and voltage gain, can be accurately extracted in a frequency domain by a single passive measurement. Subsequently, the design parameters can be efficiently tuned by separating the overall design steps into two parts. The extracted performance parameters of the WPT system were validated with time-domain experiments.

A study on distribution system reconfiguration with constant power load using Genetic algorithms (유전알고리즘을 이용한 정전력부하를 갖는 배전계통 선로의 재구성에 관한 연구)

  • Mun, K.J.;Kim, H.S.;Hwang, G.H.;Lee, H.S.;Park, J.H.
    • Proceedings of the KIEE Conference
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    • 1995.11a
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    • pp.71-73
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    • 1995
  • This paper presents an optimization technique using genetic algorithms(GA) for loss minimization in the distribution network reconfiguration. Determining switch position to be opened for loss minimization in the radial distribution system is a discrete optimization problem. GA is appropriate to solve the multivariable optimization problem and it uses population, not a solution. For this reason, GA is attractive to solve this problem. In this paper, we aimed at finding appropriate open sectionalizing switch position using GA, which can lead to minimum transmission losses.

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Optimization of T-Structure Supporting Steering System Using μGA (승용차용 스티어링시스템 지지 T-형구조물의 최적설계)

  • Lee Jong Soo;Kim Sung Chul
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.6 s.237
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    • pp.809-814
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    • 2005
  • The goal of this paper is to minimize the weight of the T-structure supporting steering system in reducing the vibration level on steering wheel which could be amplified by the resonance. Presently, requirements for reducing noise, vibration and harshness (NVH) in automotive area are more stringent than ever. One of them is the vibration of steering system which occurs sometimes at high speeds or when the engine is idling. Besides, the reduction of weight is also one of requirements for improvement of vehicle performance. This paper used the micro genetic algorithm as an optimization method to satisfy above two requirements. The whole T-structure assembly including steering column was used for frequency analysis.

DESIGN OPTIMIZATION OF A STAGGERED DIMPLED CHANNEL TO ENHANCE TURBULENT HEAT TRANSFER (열전달성능 향상을 위한 엇갈린 딤플 유로의 최적설계)

  • Shin, D.Y.;Kim, K.Y.
    • 한국전산유체공학회:학술대회논문집
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    • 2007.04a
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    • pp.159-162
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    • 2007
  • This study presents a numerical procedure to optimize the shape of a staggered dimpled surface to enhance the turbulent heat transfer in a rectangular channel. A optimization technique based on neural network is used with Reynolds-averaged Navier-Stakes analysis of the fluid flow and heat transfer with Shear Stress Transport turbulence model. The dimple depth-to-dimple print diameter ratio, channel height-to-dimple print diameter ratio, and dimple print diameter-to-pitch ratio are chosen as design variables. The objective function is defined as a linear combination of terms related to heat transfer and friction loss with a weighting factor. Latin Hypercube Sampling is used to determine the training points as a mean of the Design of Experiment. Optimal values of the design variables were obtained in a range of the weighting factor.

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Space Optimization for Warehousing Problem: A Methodology for Decision Support System

  • Murthy, A.L.N.
    • Management Science and Financial Engineering
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    • v.18 no.1
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    • pp.39-48
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    • 2012
  • This article presents a way of tackling a special class of space optimization problems that arise in a number of practical applications in industry and elsewhere. It presents an elegant solution to a problem that was considered by (Das, 2005) in optimizing storage space in warehouse of a footwear manufacturing company. In (Das, 2005), the problem was formulated as a nonlinear programming problem. In this article, it is shown that the problem can be formulated as a generalized transportation problem which is a special case of generalized network flow problems. Further, an elegant scheme is devised to handle the dynamic situation of warehousing problem which can be easily translated into a decision support system for the warehouse management system. Also, the article points out certain obscurities and gaps in (Das, 2005).

Multidisciplinary optimization of collapsible cylindrical energy absorbers under axial impact load

  • Mirzaei, M.;Akbarshahi, H.;Shakeri, M.;Sadighi, M.
    • Structural Engineering and Mechanics
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    • v.44 no.3
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    • pp.325-337
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    • 2012
  • In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. The specific absorbed energy and the maximum crushing force are considered as objective functions. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. D/t and L/D ratios are constricted in the range of which collapsing of tubes occurs in concertina or diamond mode. The Non-dominated Sorting Genetic Algorithm-II is applied to obtain the Pareto optimal solutions. A back-propagation neural network is constructed as the surrogate model to formulate the mapping between the design variables and the objective functions. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the artificial neural networks. To validate the results of finite element model, several impact tests are carried out using drop hammer testing machine.

Compensation of Networked Control Systems using LMI-Based H_$\infty$Optimization Method

  • Ho-Jun Yoo;Myung-Eui Lee;Oh-Kyu Kwon
    • KIEE International Transaction on Systems and Control
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    • v.2D no.2
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    • pp.72-77
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    • 2002
  • Delay and noise in networked control systems are inevitable and can degrade system performance or stability This paper propose a compensation method for networked control systems with network-induced delay and noise using LMI(linear matrix inequality)-based H_\infty optimization. The H_\infty optimization methods have adapted to account for both the time delay and noise effects. Some simulations applied to inverted pendulum with networked control show that the proposed method works well.

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