• Korean Journal of Environmental Biology
• /
• v.22 no.1
• /
• pp.167-170
• /
• 2004

A self-directing optimization procedure was applied to determine the best environmental factors in operating the bioreactor. The self-directing optimization process was employed to determine the best conditional combination of multi parameters, pH, temperature, aeration rate and mixing rate toy maximal production of chitinase by Trichoderma harzianum SJG-99721 in batch mode fermentation. Among these factors, the parameters of pH and aeration rate were found to be particularly important on mycellial growth and chitinase activity. pH 4.89, an aeration rate of 3.22 ι per minute and an agitation rate of 225 rpm was found to be the best combination. By the optimization, chitinase activity was dramatically increased from an initial value of 4.221 U under basic conditions to n final value of 16.825 U.

• Journal of KIISE:Software and Applications
• /
• v.30 no.9
• /
• pp.867-875
• /
• 2003

This paper concerns continuous density HMM topology optimization. There have been several researches for HMM topology optimization. BIC (Bayesian Information Criterion) is one of the well known optimization criteria, which assumes statistically well behaved homogeneous model parameters. HMMs, however, are composed of several different kind of parameters to accommodate complex topology, thus BIC's assumption does not hold true for HMMs. Even though BIC reduced the total number of parameters of HMMs, it could not improve the recognition rates. In this paper, we proposed two new model selection criteria, HBIC (HMM-oriented BIC) and BIC_Anti. The former is proposed to improve BIC by estimating model priors separately. The latter is to combine BIC and anti-likelihood to accelerate discrimination power of HMMs. We performed some comparative research on couple of model selection criteria for online handwriting data recognition. We got better recognition results with fewer number of parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.4987-5005
• /
• 2017

This paper introduces a new method to solve Similarity Recognition for Cloth Products, which is based on Fuzzy logic and Particle swarm optimization algorithm. For convenience, it is called the SRCPFP method hereafter. In this paper, the SRCPFP method combines Fuzzy Logic (FL) and Particle Swarm Optimization (PSO) algorithm to solve similarity recognition for cloth products. First, it establishes three features, length, thickness, and temperature resistance, respectively, for each cloth product. Subsequently, these three features are engaged to construct a Fuzzy Inference System (FIS) which can find out the similarity between a query cloth and each sampling cloth in the cloth database D. At the same time, the FIS integrated with the PSO algorithm can effectively search for near optimal parameters of membership functions in eight fuzzy rules of the FIS for the above similarities. Finally, experimental results represent that the SRCPFP method can realize a satisfying recognition performance and outperform other well-known methods for similarity recognition under considerations here.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.18 no.4
• /
• pp.67-72
• /
• 2010

In this study, performance analysis and design optimization is carried out for a multi-rate spring brake system, which is used in a cable ride to stop the arriving passengers in safe and comfortable manner. Mathematical model for the spring is developed toward the objective of minimizing the impact at the arrival while satisfying the constraint of limited distance at the stop. Matlab code is utilized to examine parameters affecting the performance of the brake system. The results are validated by a commercial software RecurDyn. Kriging meta model is used to reduce the computational cost of the analysis. Optimization is conducted by RecurDyn, from which the design parameters are determined that minimizes the impact at the stop.

• Journal of Magnetics
• /
• v.17 no.1
• /
• pp.46-50
• /
• 2012

A reliability-based optimization method for electromagnetic design is presented to take uncertainties of design parameters into account. The method can provide an optimal design satisfying a specified confidence level in the presence of uncertain parameters. To achieve the goal, the reliability index approach based on the firstorder reliability method is adopted to deal with probabilistic constraint functions and a double-loop optimization algorithm is implemented to obtain an optimum. The proposed method is applied to the TEAM Workshop Problem 22 and its accuracy and efficiency is verified with reference of Monte Carlo simulation results.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.4
• /
• pp.701-708
• /
• 2017

This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the conventional control methods. In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum parameters of fuzzy PID controller are selected by Teaching-Learning optimization. For the fitness function of Teaching-Learning optimization, the performance index of PID controller is used. To verify the performances of the proposed method, we use a Maglev model and compare the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

• International Journal of Precision Engineering and Manufacturing
• /
• v.7 no.4
• /
• pp.57-62
• /
• 2006

The quality of precision spindle units (S/Us) running on rolling bearings depends strongly on their structural parameters, such as the configuration and geometry of the S/U elements and bearing preloads. When S/Us are designed, their parameters should be optimized to improve the performance characteristics. However, it is practically impossible to state perfectly a general criterion function for S/U quality. Therefore, we propose to use a multicriteria optimization based on the parameter space investigation (PSI) method We demonstrate the efficiency of the proposed method using the optimization results of high-speed S/Us.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2007.04a
• /
• pp.309-314
• /
• 2007

Optimal layer sequencing of a multi-layered acoustical foam is solved to maximize its sound transmission loss. A foam consisting of air and poroelastic layers can be optimized when a limited amount of a poroelastic material is allowed. By formulating the sound transmission loss maximization problem as a one dimensional topology optimization problem, optimal layer sequencing and thickness were systematically found for several frequencies. For optimization, the transmission losses of air and poroelastic layers were calculated by the transfer matrix derived from Biot's theory. By interpolating five intrinsic parameters among several poroelastic material parameters, dear air-poroelastic layer distributions were obtained; no filtering or post-processing was necessary. The optimized foam layouts by the proposed method were shown to differ depending on the frequency of interest.

• Proceedings of the KIEE Conference
• /
• 1998.07a
• /
• pp.241-243
• /
• 1998

This paper describes a new method for the faster shape optimization of the electromagnetic devices. In a conventional iterative method of shape design optimization using design sensitivity based on a finite element method, meshes for a new shape of the model are generated and a discretized system equation is solved using the meshes in each iteration. They cause much design time. To save this time, a polynomial approximation of the finite element solution with respect to the geometric design parameters using Taylor expansion is constructed. This approximate state variable expressed explicitly in terms of design parameters is employed in a gradient-based optimization method. The proposed method is applied to the shape design of quadrupole magnet.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.51 no.2
• /
• pp.55-60
• /
• 2002

This paper presents an optimization procedure by using Response Surface Methodology(RSM) to determine design parameters for reducing cogging torque. RSM is achieved through using the experimental design method in combination with Finite Element Method and adapted to make analytical model for a complex problem considering a lot of interaction of these parameters. Sequential Quadratic Problem (SQP) method is used to solve the relsulting of constrained nonlinear optimization problem.