• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.373-378
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• 2001

The optimization method based on an enhanced genetic algorithms is for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is a global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by single point method in reconstructive search space. Four numerical examples are also presented in this papers to comparing with conventional methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.173-181
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• 2001

This paper presents an artificial life algorithm which is remarkable in the area of engineering for functions optimization. As artificial life organisms have a sensing system, they can find the resource which they want to find and metabolize. And the characteristics of artificial life are emergence and dynamic interaction with environment. In other words, the micro-interaction with each other in the artificial lifes group results in emergent colonization in the whole system. In this paper, therefore, artificial life algorithm by using above characteristics is employed into functions optimization. The optimizing ability and convergent characteristics of this proposed algorithm is verified by using three test functions. The numerical results also show that the proposed algorithm is superior to genetic algorithms and immune algorithms for the multimodal functions.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.76-78
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• 1998

Niching methods extend genetic algorithms to domains that require the location of multiple solutions. But, current niching methods have some of drawbacks in the ability of search and preservation of solutions. So, this paper presents a new technique, named as Restricted Competition Selection(RCS). Then, RCS method is compared with sharing and deterministic crowding by applying to some multimodal problems in order to verify that it has more favorable properties.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.92-101
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• 2018

As the interactive AI speaker becomes popular, voice recognition is regarded as an important vehicle-driver interaction method in case of autonomous driving situation. The purpose of this study is to confirm whether multimodal interaction in which feedback is transmitted by auditory and visual mode of AI characters on screen is more effective in user experience optimization than auditory mode only. We performed the interaction tasks for the music selection and adjustment through the AI speaker while driving to the experiment participant and measured the information and system quality, presence, the perceived usefulness and ease of use, and the continuance intention. As a result of analysis, the multimodal effect of visual characters was not shown in most user experience factors, and the effect was not shown in the intention of continuous use. Rather, it was found that auditory single mode was more effective than multimodal in information quality factor. In the semi-autonomous driving stage, which requires driver 's cognitive effort, multimodal interaction is not effective in optimizing user experience as compared to single mode interaction.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.151-156
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• 2015

In this paper, we have proposed personal multimodal biometric authentication system based on face detection, recognition and speaker verification for smart-phone environment. Proposed system detect the face with Modified Census Transform algorithm then find the eye position in the face by using gabor filter and k-means algorithm. Perform preprocessing on the detected face and eye position, then we recognize with Linear Discriminant Analysis algorithm. Afterward in speaker verification process, we extract the feature from the end point of the speech data and Mel Frequency Cepstral Coefficient. We verified the speaker through Dynamic Time Warping algorithm because the speech feature changes in real-time. The proposed multimodal biometric system is to fuse the face and speech feature (to optimize the internal operation by integer representation) for smart-phone based real-time face detection, recognition and speaker verification. As mentioned the multimodal biometric system could form the reliable system by estimating the reasonable performance.

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.391-400
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• 2004

Within multimodal medical image registration techniques, which correlate different images and Provide integrated information, surface registration methods generally minimize the surface distance between two modalities. However, the features of two modalities acquired from one subject are similar. So, it can improve the accuracy of registration result to match two images based on optimization of both surface distance and shape feature. This research proposes a registration method which optimizes surface distance and surface curvature of two brain modalities. The registration process has two steps. First, surface information is extracted from the reference images and the test images. Next, the optimization process is performed. In the former step, the surface boundaries of regions of interest are extracted from the two modalities. And for the boundary of reference volume image, distance map and curvature map are generated. In the optimization step, a transformation minimizing both surface distance and surface curvature difference is determined by a cost function referring to the distance map and curvature map. The applying of the result transformation makes test volume be registered to reference volume. The suggested cost function makes possible a more robust and accurate registration result than that of the cost function using the surface distance only. Also, this research provides an efficient means for image analysis through volume visualization of the registration result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6276-6281
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• 2014

Genetic Algorithms (GAs) are efficient methods for search and optimization problems. On the other hand, there are some problems associated with the premature convergence to local optima of the multimodal function, which has multi peaks. The problem is related to the lack of genetic diversity of the population to cover the search spaces sufficiently. A sharing and crowding method were introduced. This paper proposed strategies to improve the convergence speed and the convergence to the global optimum for solving the multimodal optimization function. These strategies included the random generated sub-population that were well-distributed and spread widely through search spaces. The results of the simulation verified the effects of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1809-1814
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• 2003

Differential evolution (DE) algorithm is presented and applied to global optimization in this research. DE suggested initially fur the solution to Chebychev polynomial fitting problem is similar to genetic algorithm(GA) including crossover, mutation and selection process. However, differential evolution algorithm is simpler than GA because it uses a vector concept in populating process. And DE turns out to be converged faster than CA, since it employs the difference information as pseudo-sensitivity In this paper, a trial vector and its control parameters of DE are examined and unconstrained optimization problems of highly nonlinear multimodal functions are demonstrated. To illustrate the efficiency of DE, convergence rates and robustness of global optimization algorithms are compared with those of simple GA.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.1008-1015
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• 2002

This paper proposes a combinatorial method to compute the global and local solutions of optimization problem. The present hybrid algorithm is the synthesis of a genetic algorithm and a local concentrate search algorithm (simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution. In addition, this algorithm can find both the global and local optimum solutions. An optimization result is presented to demonstrate that the proposed approach successfully focuses on the advantages of global and local searches. Three numerical examples are also presented in this paper to compare with conventional methods.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.826-827
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• 2011

The selection of optimum parameters in electromagnetic design usually requires optimization of multimodal, non linear functions. This leads to extensive calculations which pose a huge inconvenience in the design process. This paper proposes a novel algorithm for dealing efficiently with this issue. Through the use of contour line concept coupled with Kriging, the algorithm finds out all the peaks in the problem domain with as few function calls as possible. The proposed algorithm is applied to the magnet shape optimization of an axial flux permanent magnet synchronous machine and the cogging torque was reduced to 79.8% of the initial one.