• Journal of Information Technology Applications and Management
• /
• v.9 no.2
• /
• pp.1-16
• /
• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.1
• /
• pp.44-53
• /
• 2008

An interacting multiple model (IMM) estimation algorithm based on the mixing of the predicted state estimates is proposed in this paper for a right continuous jump-linear system model different from the left-continuous system model used to develop the existing IMM algorithm. The difference lies in the modeling of the mode switching time. Performance of the proposed algorithm is compared numerically with that of the existing IMM algorithm for noisy system identification. Based on the numerical analysis, the proposed algorithm is applied to target tracking with a large sampling period for performance comparison with the existing IMM.

• Structural Engineering and Mechanics
• /
• v.80 no.5
• /
• pp.539-551
• /
• 2021

With the continuous increase of computational capacity, more and more complex nonlinear elastoplastic constitutive models were developed to study the mechanical behavior of elastoplastic materials. These constitutive models generally contain a large amount of physical and phenomenological parameters, which often require a large amount of computational costs to determine. In this paper, an inverse parameter determination method is proposed to identify the constitutive parameters of elastoplastic materials, with the consideration of both strain rate effect and temperature effect. To carry out an efficient design, a hybrid optimization algorithm that combines the genetic algorithm and the Nelder-Mead simplex algorithm is proposed and developed. The proposed inverse method was employed to determine the parameters for an elasto-viscoplastic constitutive model and Johnson-cook model, which demonstrates the capability of this method in considering strain rate and temperature effect, simultaneously. This hybrid optimization algorithm shows a better accuracy and efficiency than using a single algorithm. Finally, the predictability analysis using partial experimental data is completed to further demonstrate the feasibility of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.1
• /
• pp.54-64
• /
• 1997

In this paper, a method of dynamic modeling and a dynamic control of a mobile robot are presented to show the superiority of the dynamic control comparing to the PD control. This dynamic model is derived from the cartesian coordinates using lagrange equations. Based upon the derived dynamic model, we implemented the dynamic control of the mobile robot using the computed torque method. Time varying non-linear friction terms are not incroporated in this dynamic model. Instead, those are considered as disturbances. This uncertainty in dynamic model of mobile robot is compensated by the outer loop controller using PD algorithm. The validity of this model and the control algorithm are confirmed through the experiments, where the dynamic control algorithm demonstrated robust velocity tracking performance against the unmodeled non-linear frictions. The superiority of this algorithm is demonstrated by comparing to classical PD control algorithm.

• Proceedings of the SAREK Conference
• /
• 2008.11a
• /
• pp.387-392
• /
• 2008

In order to improve the efficiency of the main transformer in a tilting train, the optimal operation of a cooling system is necessary. For the development of the optimal control algorithm of a cooling system, the mathematical model of a main transformer cooling system was developed. This includes the dynamic model of a main transformer, an oil pump, an oil cooler and a blower. The system algorithm of a cooling system, which consists of the temperature setpoint algorithm and the temperature control algorithm, was developed. Optimal oil temperatures of the inlet and the outlet of the main transformer were obtained by considering the total electric power consumption of the system. The oil inlet temperature was controlled by the blower and the oil outlet temperature was controlled by the oil pump. A simulation program was developed by using the mathematical model and the system algorithm. Simulation results showed that the system algorithm developed from this study may be effectively used to control the main transformer cooling system in a tilting train.

• Proceedings of the KSME Conference
• /
• 2001.06c
• /
• pp.537-542
• /
• 2001

In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.

• Nuclear Engineering and Technology
• /
• v.39 no.4
• /
• pp.313-326
• /
• 2007

In this paper, an algorithm to reconstruct two orthogonal images into a three-dimensional image is developed in order to measure the bubble size and volume in a two-phase boiling flow. The central-active contour model originally proposed by P. $Szczypi\'{n}ski$ and P. Strumillo is modified to reduce the dependence on the initial reference point and to increase the contour stability. The modified model is then applied to the algorithm to extract the object boundary. This improved central contour model could be applied to obscure objects using a variable threshold value. The extracted boundaries from each image are merged into a three-dimensional image through the developed algorithm. It is shown that the object reconstructed using the developed algorithm is very similar or identical to the real object. Various values such as volume and surface area are calculated for the reconstructed images and the developed algorithm is qualitatively verified using real images from rubber clay experiments and quantitatively verified by simulation using imaginary images. Finally, the developed algorithm is applied to measure the size and volume of vapor bubbles condensing in a subcooled boiling flow.

• Korean Journal of Computational Design and Engineering
• /
• v.15 no.2
• /
• pp.136-143
• /
• 2010

Realistic Modeling is to maximize the reality of the environment in which perception is made by virtual environment or remote control using two or more senses of human. Especially, the field of haptic rendering, which provides reality through interaction of visual and tactual sense in realistic model, has brought attention. Haptic rendering calculates the force caused by model deformation during interaction with a virtual model and returns it to the user. Deformable model in the haptic rendering has more complexity than a rigid body because the deformation is calculated inside as well as the outside the model. For this model, Gibson suggested the 3D ChainMail algorithm using volumetric data. However, in case of the deformable model with non-homogeneous materials, there were some discordances between visual and tactual sense information when calculating the force-feedback in real time. Therefore, we propose an algorithm for the Volume Haptic Rendering of non-homogeneous deformable object that reflects the force-feedback consistently in real time, depending on visual information (the amount of deformation), without any post-processing.

• Journal of computational fluids engineering
• /
• v.17 no.4
• /
• pp.9-15
• /
• 2012

Thermal model reduction algorithms and techniques are introduced to condense a huge satellite panel thermal model into the simplified model on the purpose of calculating the thermal responses of a satellite on orbit. Guyan condensation algorithm with the substitution matrix manipulation is developed and the mathematical procedure is depicted step by step. A block-form LU decomposition method is also invited to compare the developed algorithm. The constructed reduced thermal model induced from the detailed model based on a real satellite panel is satisfying the correlation criterion of ${\pm}2^{\circ}C$ for the validity accuracy. Guyan condensation algorithm is superior to the block-form LU decomposition method on computation time.

• Journal of Microbiology and Biotechnology
• /
• v.14 no.6
• /
• pp.1163-1169
• /
• 2004

An unstructured mathematical model is presented for lactic acid fermentation based on the energy balance. The proposed model reflects the energy metabolic state and then predicts the cell growth, lactic acid production, and glucose consumption rates by relating the above rates with the energy metabolic rate. Fermentation experiments were conducted under various initial lactic acid concentrations of 0, 30, 50, 70, and 90 g/l. Also, a genetic algorithm was used for further optimization of the model parameters and included the operations of coding, initialization, hybridization, mutation, decoding, fitness calculation, selection, and reproduction exerted on individuals (or chromosomes) in a population. The simulation results showed a good fit between the model prediction and the experimental data. The genetic algorithm proved to be useful for model parameter optimization, suggesting wider applications in the field of biological engineering.