• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.463-467
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• 2008

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.3003-3009
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• 2000

In this paper, we propose a new efficient hybrid-mixed C(sup)0 curved beam element with the optimal interpolation functions determined from numerical tests, which gives very accurate locking-free two-node curved beam element. In the element level, the stress parameters are eliminated from the stationary condition and the nodeless degrees of freedom are also removed by static condensation so that a standard six-by-six stiffness matrix is finally obtained. The numeri cal benchmark problems show that the element with cubic displacement functions and quadratic stress functions is the most efficient.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.5
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• pp.257-266
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• 2006

Recently TCP/IP Offload Engine (TOE) technology, which processes TCP/IP on a network adapter instead of the host CPU, has become an important approach to reduce TCP/IP processing overhead in the host CPU. There have been two approaches to implementing TOE: software TOE, in which TCP/IP is processed by an embedded processor on a network adapter; and hardware TOE, in which all TCP/IP functions are implemented by hardware. This paper proposes a hybrid TOE that combines software and hardware functions in the TOE. In the hybrid TOE, functions that cannot have guaranteed performance on an embedded processor because of heavy load are implemented by hardware. Other functions that do not impose as much load are implemented by software on embedded processors. The hybrid TOE guarantees network performance near that of hardware TOE and it has the advantage of flexibility, because it is easy to add new functions or offload upper-level protocols of TCP/IP. In this paper, we developed a prototype board with an FPGA and an ARM processor to implement a hybrid TOE prototype. We implemented the hardware modules on the FPGA and the software modules on the ARM processor. We also developed a coprocessing mechanism between the hardware and software modules. Experimental results proved that the hybrid TOE prototype can greatly reduce the load on a host CPU and we analyzed the effects of the coprocessing mechanism. Finally, we analyzed important features that are required to implement a complete hybrid TOE and we predict its performance.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.137-147
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• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• Structural Engineering and Mechanics
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• v.29 no.6
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• pp.643-658
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• 2008

In this paper, we present a quadratic element model based on non-conforming displacement modes and modified shape functions. This new and refined 8-node hybrid stress plane element consists of two additional non-conforming modes that are added to the translational degree of freedom to improve the behavior of a membrane component. Further, the modification of the shape functions through quadratic polynomials in x-y coordinates enables retaining reasonable accuracy even when the element becomes considerably distorted. To establish its accuracy and efficiency, the element is compared with existing elements and - over a wide range of mesh distortions - it is demonstrated to be exceptionally accurate in predicting displacements and stresses.

• The Journal of the Acoustical Society of Korea
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• v.24 no.5
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• pp.255-261
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• 2005

In this paper. experiments are conducted to extract a set of non-Parametric warping functions to examine the characteristics of the warping among speakers' utterances. For this Purpose. we made use of MFCC and LP spectra of vowels in choosing reference spectrum of each vowel as well as representative spectra of each speaker. These spectra are compared by DTW to give the warping functions of each speaker. The set of warping functions are then defined by clustering the warping functions of all the speakers. Noting that male and female warping functions have shapes similar to Piecewise linear function and Power function respectively, a new hybrid set of warping functions is defined. The effectiveness of the extracted warping functions are evaluated by conducting phone level recognition experiments, and improvements in accuracy rate are observed in both warping functions.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.126-129
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• 1997

This paper propose a new hybrid genetic algorithm for auto-tunig auzzy controller improving the performance. In general, fuzzy controller used pre-determine d moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controller, using hybrid genetic algorithms. The object of the proposed algorithm is to promote search efficiency by overcoming a premature convergence of genetic algorithms. Hybrid genetic algorithm is based on genetic algorithm and modified gradient method. Simulation results verify the validity of the presented method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.36-43
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• 1999

This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.428-431
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• 2004

Smart structural system is defined as structural system with a certain-level of autonomy relying on the embedded functions of sensors, actuators and processors, that can automatically adjust structural characteristics, in response to the change in external disturbance and environments, toward structural safety and serviceability as well as the extension of structural service life. In this study, carbon and glass hybrid fiber materials were investigated fundamentally for the applicability of self diagnosis in smart concrete structural system as embedded functions of sensors.