• Smart Structures and Systems
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• v.19 no.4
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• pp.403-413
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• 2017

The present article encompasses a nonlinear finite element (FE) and genetic algorithm (GA) based optimal vibration energy harvesting from nonprismatic piezo-laminated cantilever beams. Three cases of cross section profiles (such as linear, parabolic and cubic) are modelled to analyse the geometric nonlinear effects on the output responses such as displacement, voltage, and power. The simultaneous effects of taper ratios (such as breadth and height taper) on the output power are also studied. The FE based nonlinear dynamic equation of motion has been solved by an implicit integration method (i.e., Newmark method in conjunction with the Newton-Raphson method). Besides this, a real coded GA based constrained optimization scheme has also been proposed to determine the best set of design variables for optimal harvesting of power within the safe limits of beam stress and PZT breakdown voltage.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.1
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• pp.47-51
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• 2018

This paper is concerned with an optimization design of Incremental Granular Model(IGM) based Genetic Algorithm (GA) as an evolutionary approach. The performance of IGM has been successfully demonstrated to various examples. However, the problem of IGM is that the same number of cluster in each context is determined. Also, fuzzification factor is set as typical value. In order to solve these problems, we develop a design method for optimizing the IGM to optimize the number of cluster centers in each context and the fuzzification factor. We perform energy analysis using 12 different building shapes simulated in Ecotect. The experimental results on energy efficiency data set of building revealed that the proposed GA-based IGM showed good performance in comparison with LR and IGM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.121-126
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• 2003

In this paper, we propose a new wavelet-based fuzzy modeling using a DNA coding method. Generally, it is well known that the DNA coding method is more diverse in the knowledge expression and better in the optimization performance than the genetic algorithm (GA) because it can encode more plentiful genetic informations based on the biological DNA. The proposed method can construct a fuzzy model using the wavelet transform, in which the coefficients are identified by the DNA coding method. Thus, we can effectively get the fuzzy model of the nonlinear system by using the advantages of both wavelet transform and DNA coding method. In order to demonstrate the superiority of the proposed method, it is compared with modeling method using the conventional GA.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.366-369
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• 2006

본 논문에서는 계층적 공정 경쟁 유전자 알고리즘을 통한 비선형시스템의 정보입자 기반 퍼지집합 퍼지집합 모델의 최적화 방법을 제안한다. 퍼지집합 모델은 주로 전문가의 경험에 기반을 두어 얻어지기 때문에 동정과 최적화 과정이 필요하며 GAs를 이용하여 퍼지모델을 최적화한 연구가 많이 있다. GAs는 전역 해를 찾을 수 있는 최적화 알고리즘으로 잘 알려져 있지만 조기 수렴 문제를 포함하고 있다. 병렬유전자 알고리즘(PGA)은 조기수렴를 더디게 하고 전역 해를 찾기 위한 진화알고리즘이다. 적응형 계층적 공정 경쟁기반 유전자 알고리즘(AHFCGA)을 이용하여 퍼지모델의 입력변수, 멤버쉽함수의 수, 멤버쉽함수의 정점 등의 전반부 구조와 파라미터를 동정하였고, LSE를 사용하여 후반부 파라미터를 동정하였으며 실험적 예제를 통하여 제안된 방법의 성능을 평가한다.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.11
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• pp.889-897
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• 2014

This paper presents a method for finding an optimized result by using a genetic algorithm (GA) based on a PEMFC analysis result. The conventional analysis method designs fuel cells one-by-one, and each result is compared to obtain the best performance. Because the computational burden of the conventional analysis is enormous, the present optimization process provides an inefficient tool by automatically setting the boundary and material properties and mesh generation. As the change can be reflected automatically in the channel geometry with GA, the fuel cell analysis result with various sizes can be obtained easily. Therefore, the global maximum performance can be obtained through a GA optimization procedure.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.221-230
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• 2021

This paper proposes full waveform inversion (FWI) for estimating the physical properties of a layered half-space. An FWI solution is obtained using a genetic algorithm (GA), which is a well-known global optimization approach. The dynamic responses of a layered half-space subjected to a harmonic vertical disk load are measured and compared with those calculated using the estimated physical properties. The responses are calculated using the thin-layer method, which is accurate and efficient for layered media. Subsequently, a numerical model is constructed for a layered half-space using mid-point integrated finite elements and perfectly matched discrete layers. An objective function of the global optimization problem is defined as the L2-norm of the difference between the observed and estimated responses. A GA is used to minimize the objective function and obtain a solution for the FWI. The accuracy of the proposed approach is applied to various problems involving layered half-spaces. The results verify that the proposed FWI based on a GA is suitable for estimating the material properties of a layered half-space, even when the measured responses include measurement noise.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.882-891
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• 2010

Many automated dynamic test data generation technique have been proposed. The techniques evaluate fitness of test data through executing instrumented Software Under Test (SUT) and then generate new test data based on evaluated fitness values and optimization algorithms. Previous researches and experiments have been showed that these techniques generate effective test data. However, optimization algorithms in these techniques incur much time to generate test data, which results in huge test case generation cost. In this paper, we propose a technique for reducing the time of evaluating a fitness of test data among steps of dynamic test data generation methods. We introduce the concept of Fitness Evaluation Program (FEP), derived from a path constraint of SUT. We suggest a test data generation method based on FEP and implement a test generation tool, named ConGA. We also apply ConGA to generate test cases for C programs, and evaluate efficiency of the FEP-based test case generation technique. The experiments show that the proposed technique reduces 20% of test data generation time on average.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.820-824
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• 2010

Genetic algorithm is known to be an effective method to solve a global nonlinear optimization. However, a huge amount of calculation is needed to improve the dependability of the solution and thus Ga is not adequate for online implementation. In this paper, we propose an online nonlinear system identification scheme which employs population feedback genetic algorithm. The effectiveness of our scheme is shown by several simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.232-235
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• 2000

In this paper, we introduce a new genetic reordering operator based on the concept of schema to solve the Traveling Salesman Problem(TSP). Because TSP is a well-known combinatorial optimization problem and belongs to a NP-complete problem, there is a huge solution space to be searched. For robustness to local minima, the operator separates selected strings into two parts to reduce the destructive probability of good building blocks. And it applies inversion to the schema part to prevent the premature convergence. At the same time, it searches new spaces of solutions. In addition, we have the non-schema part to be applied to inversion as well as for robustness to local minima. By doing so, we can preserve diversity of the distributions in population and make GA be adaptive to the dynamic environment.