• Journal of the Korean Geotechnical Society
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• v.35 no.4
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• pp.27-35
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• 2019

This study confirmed the applicability to the field of geotechnical engineering for relatively complicated space and many target design variables in back analysis. The Sharan's equation and the Blum's method were used for the tunnel field and the retaining wall as a model for the multi-variate problem of geotechnical engineering. Optimization methods are generally divided into a deterministic method and a stochastic method. In this study, Simulated Annealing Method (SA) was selected as a deterministic method and Differential Evolution Algorithm (DEA) and Particle Swarm Optimization Method (PSO) were selected as stochastic methods. The three selected optimization methods were compared by applying a multi-variate model. The problem of deterministic method has been confirmed in the multi-variate back analysis of geotechnical engineering, and the superiority of DEA can be confirmed. DEA showed an average error rate of 3.12% for Sharan's solution and 2.23% for Blum's problem. The iteration number of DEA was confirmed to be smaller than the other two optimization methods. SA was confirmed to be 117.39~167.13 times higher than DEA and PSO was confirmed to be 2.43~6.91 times higher than DEA. Applying a DEA to the multi-variate back analysis of geotechnical problems can be expected to improve computational speed and accuracy.

• Smart Media Journal
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• v.13 no.2
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• pp.129-135
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• 2024

In this paper, we propose a new approach that enhances the continuous optimization performance of the MAP-Elites algorithm. The existing self-referencing MAP-Elites algorithm employed the "DE/rand/1/bin" operator from the differential evolution algorithm, which, due to its lack of rotational invariance, led to a degradation in optimization performance when there were high correlations among variables. The proposed algorithm replaces the "DE/rand/1/bin" operator with the "DE/current-to-rand/1" operator. This operator, possessing rotational invariance, ensures robust performance even in cases where there are high correlations among variables. Experimental results confirm that the proposed algorithm performs better than the comparison algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.1
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• pp.211-224
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• 2018

It is important to design tunnel support system considering the various loads acting on the tunnel because they have a direct impact on the stability of tunnels. In Korea, standardized support patterns are defined based on the rock mass classification system depending on the project, and it is stated that it should be modified appropriately considering the behavior of tunnel during construction. In this study, the tunnel support pattern optimization method is suggested based on the convergence-confinement method, earth pressure, axial force of rock bolt, and moment acting on the shotcrete. The length and spacing of the rock bolts and the thickness of the shotcrete were optimized by using the differential evolution algorithm (DEA) and the results were compared to the standard support pattern III for railway tunnel. Rock bolt length can be reduced and the installation interval can be widened for shallow tunnel. As the depth of tunnel increases, the thickness of shotcrete increases linearly. Therefore, the thickness of shotcrete should be thicker than the standard support pattern as the depth of tunnel increases to secure the stability of tunnel.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.9-18
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• 2009

This paper introduces the application of DE (Differential Evolutionary) method for the estimation of tensile force of the externally prestressed tendon. The proposed technique, a SI (System Identification) method using the DE algorithm, can make global solution search possible as opposed to classical gradient-based optimization techniques. The numerical tests show that the proposed technique employing DE algorithm is a useful method which can detect the effective nominal diameters as well as estimate the exact tensile forces of the externally prestressed tendon with an estimation error less than 1% although there is no a priori information about the identification variables. In addition, the validity of the proposed technique is experimentally proved using a scale-down model test considering the serviceability state condition without and with the loss of the prestressed force. The test results prove that the technique is a feasible and effective method that can not only estimate the exact tensile forces and detect the effective nominal diameters but also inspect the damping properties of test model irrespective of the loss of the prestressed force. The 2% error of the estimated effective nominal diameter is due to the difference between the real tendon diameter with a wired section and the FE model diameter with a full-section. Finally, The accuracy and superiority of the proposed technique using the DE algorithm are verified through the comparative study with the existing theories.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1962-1963
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• 2011

본 연구에서는 패턴분류를 위해 최적화된 방사형 기저 함수 신경회로망(Radial Basis Function Neural Networks) 분류기를 제안한다. RBFNN은 입력층, 은닉층, 출력층의 3층 구조로 되어 있으며 Multi Dimension, Predictive ability, Robustness한 특징이 있다. RBFNN의 은닉층에는 기존의 활성함수가 아닌 Fuzzy C-means 클러스터링 알고리즘을 사용하여 입력 데이터의 특성을 고려한 적합도를 사용하였다. RBFNN은 은닉층의 노드수와 FCM 클러스터링의 퍼지화 계수, 연결가중치의 다항식 타입이 모델의 성능의 향상에 영향을 미치기 때문에 최적화가 필요하며 본 논문에서는 Differential Evolution(DE) 알고리즘을 사용하여 모델의 구조 및 파라미터를 최적화시켜 모델의 성능을 향상시켰다. 제안된 모델을 평가하기 위해 패턴분류에 많이 사용되는 Iris 데이터와 Wine 데이터를 이용하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.293-303
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• 2016

Analysis of bargaining games utilizing evolutionary computation in recent years has dealt with important issues in the field of game theory. In this paper, we investigated interaction and coevolution process among heterogeneous artificial agents using evolutionary computation in the bargaining game. We present three kinds of evolving-strategic agents participating in the bargaining games; genetic algorithms (GA), particle swarm optimization (PSO) and differential evolution (DE). The co-evolutionary processes among three kinds of artificial agents which are GA-agent, PSO-agent, and DE-agent are tested to observe which EC-agent shows the best performance in the bargaining game. The simulation results show that a PSO-agent is better than a GA-agent and a DE-agent, and that a GA-agent is better than a DE-agent with respect to co-evolution in bargaining game. In order to understand why a PSO-agent is the best among three kinds of artificial agents in the bargaining game, we observed the strategies of artificial agents after completion of game. The results indicated that the PSO-agent evolves in direction of the strategy to gain as much as possible at the risk of gaining no property upon failure of the transaction, while the GA-agent and the DE-agent evolve in direction of the strategy to accomplish the transaction regardless of the quantity.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.6
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• pp.569-579
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• 2016

In order to ensure the stability of tunnel portal slope, reinforcement method such as anchors, soil nails and rock bolts have been used in Korea. When selecting slope reinforcement methods in tunnel portal area such as reinforcement arrangement and length, trial and error method can be very time-consuming and it was also not easy to verify the selection of an optimum condition. In this study, using the FISH language embedded in the finite difference code FLAC3D program, the optimization technique was developed with the Differential Evolution Algorithm (DEA). After building a database on the soil nailing method in tunnel portal area, this system can be selected to an optimum arrangement plan based on the factor of safety through the FLAC3D analysis. Through the results of numerical analysis, it was confirmed that the number of analysis was decreased by about 8 times when DEA based optimization technique was used compared to the full combination (FC). In case of the design of slope reinforcement in tunnel portal area, if this built-system is used, it is expected that the selection of an optimum arrangement plan can be relatively easier.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.749-754
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• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.