• Proceedings of the IEEK Conference
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• summer
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• pp.294-301
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• 2004

This paper presents a combination of optimization concept and congestion control for multicast communications to bring best benefit for the network. For different types of Internet services, there will be different utility functions and so there will be different ways to choose on how to control the congestion, especially for real time multicast traffic. Our proposed algorithm OMCC brings the first implementation experiment of utility-based Multicast Congestion Control. Simulation results show that OMCC brings better network performances in multicast session throughput while it still keeps a certain fairness of unicast and multicast sessions, and thus, provides better benefit for all network participants.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.64-68
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• 2012

In this paper, a design method for minimizing the cogging torque of an Interior Permanent Magnet Motor (IPM) is proposed based on a hybrid algorithm. The suggested optimization algorithm is based on a combination of the Response Surface Method (RSM) and Simplex Method. The results show that the proposed method provides improved characteristics compared to the conventional methods, such as a shorter calculation time and the acquisition of a more correct solution.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.647-649
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• 2000

This paper presents a optimization procedure by using Response Surface Methodology(RSM) to determine design Parameters for reducing cogging torque in BLDC motor of Electric Power Steering (EPS). RSM is achieved through using the experiment design method in combination with Finite Element Method and well adapted to make analytical model for a complex problem considering a lot of interaction of these parameters. Moreover, Sequential Quadratic Problem (SQP) method is used to solve the resulting of constrained nonlinear optimization problem.

• Journal of computational fluids engineering
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• v.14 no.2
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• pp.1-8
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• 2009

The purposes of present work are to analyze the convective heat transfer with three-dimensional Reynolds-averaged Navier-Stokes analysis, and to optimize shape of the mixing vane taken tolerance into consideration by using the analysis results. Response surface method is employed as an optimization technique. The objective function is defined as a combination of heat transfer rate and inverse of pressure drop. Two bend angles of mixing vane are selected as design variables. Thermal-hydraulic performances have been discussed and optimum shape has been obtained as a function of weighting factor in the objective function. The results show that the optimized geometry improves the heat transfer performance far downstream of the mixing vane.

• Journal of the Optical Society of Korea
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• v.18 no.1
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• pp.82-88
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• 2014

A binary-coupled dipole approximation (BCDA) is described for designing metal nanoparticles with nonperiodic structures in one, two, and three dimensions. This method can be used to simulate the variation of near- and far-field properties through the interactions of metal nanoparticles. An advantage of this method is in its combination with the binary particle swarm optimization (BPSO) algorithm to find the best array of nanoparticles from all possible arrays. The BPSO algorithm has been used to design an array of plasmonic nanospheres to achieve maximum absorption, scattering, and extinction coefficient spectra. In BPSO, a swarm consists of a matrix with binary entries controlling the presence ('1') or the absence ('0') of nanospheres in the array. This approach is useful in optical applications such as solar cells, biosensors, and plasmonic nanoantennae, and optical cloaking.

• Structural Engineering and Mechanics
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• v.24 no.2
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• pp.155-180
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• 2006

Many applications in mechanical design involve elastic bodies coming into contact under the action of the applied load. The distribution of the contact pressure throughout the contact interface plays an important role in the performance of the contact system. In many applications, it is desirable to minimize the maximum contact pressure or to have an approximately uniform contact pressure distribution. Such requirements can be attained through a proper design of the initial surfaces of the contacting bodies. This problem involves a combination of two disciplines, contact mechanics and shape optimization. Therefore, the objective of the present paper is to develop an integrated procedure capable of evaluating the optimal shape of contacting bodies. The adaptive incremental convex programming method is adopted to solve the contact problem, while the augmented Lagrange multiplier method is used to control the shape optimization procedure. Further, to accommodate the manufacturing requirements, surface parameterization is considered. The proposed procedure is applied to a couple of problems, with different geometry and boundary conditions, to demonstrate the efficiency and versatility of the proposed procedure.

• Journal of the Korea Society for Simulation
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• v.6 no.1
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• pp.71-84
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• 1997

For many optimization problems where some of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. Many, if not most, simulation optimization problems have multiple aspects. Historically, multiple aspects have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple aspects. In this paper we propose a MAGA (Multiple Aspects-based Genetic Algorithm) as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two problems.

• 유체기계공업학회:학술대회논문집
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• 2003.12a
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• pp.627-630
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• 2003

This work presents a numerical procedure to optimize the shape of three-dimensional channel with angled ribs mounted on one of the walls to enhance turbulent heat transfer. The response surface method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of flow and heat transfer. SST turbulence model is used as a turbulence closure. The width-to-height ratio of the rib, rib height-to-channel height ratio, pitch-to-rib height ratio and attack angle of the rib are chosen as design variables. The objective function is defined as a linear combination of heat-transfer and friction-loss related terms with weighting factor. D-optimal experimental design method is used to determine the data points. Optimum shapes of the channel have been obtained for the weighting factors in the range from 0.0 to 1.0.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.686-691
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• 2011

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.131-138
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• 2022

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.