• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.279-282
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• 2006

네트워크를 기반으로 한 컴퓨터 시스템이 현대 사회에 있어서 더욱 더 불가결한 역할을 하는 것에 따라, 네트워크 기반 컴퓨터 시스템은 침입자의 침입 목표가 되고 있다. 이를 보호하기 위한 침입탐지시스템(Intrusion Detection System : IDS)은 점차 중요한 기술이 되었다. 침입탐지시스템에서 패턴들을 분석한 후 정상/비정상을 판단 및 예측하기 위해서는 초기단계인 특징추출이나 선택이 매우 중요한 부분이 되고 있다. 본 논문에서는 IDS에서 중요한 부분인 feature selection을 Data Mining 기법인 Genetic Algorithm(GA)과 Decision Tree(DT)를 적용해서 구현했다.

• Geomechanics and Engineering
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• 제20권3호
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.416-421
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• 2008

A Metropolis genetic algorithm (MGA) is a newly-developed hybrid algorithm combining simple genetic algorithm (SGA) and simulated annealing (SA). In the algorithm, favorable features of Metropolis criterion of SA are incorporated in the reproduction operations of SGA. This way, MGA alleviates the disadvantages of finding imprecise solution in SGA and time-consuming computation in SA. It has been successfully applied and the efficiency has been verified for the practical structural design optimization. However, applicability of MGA for the wider range of problems should be rigorously proved through the solution of mathematical optimization problems. Thus, performances of MGA for the typical mathematical problems are investigated and compared with those of conventional algorithms such as SGA, micro genetic algorithm (${\mu}GA$), and SA. And, for better application of MGA, the effects of acceptance level are also presented. From numerical Study, it is again verified that MGA is more efficient and robust than SA, SGA and ${\mu}GA$ in the solution of mathematical optimization problems having various features.

• 한국정보통신학회논문지
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• 제11권11호
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• pp.2158-2165
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• 2007

본 논문에서는 비선형 블라인드 채널등화기의 구현을 위하여 가우시안 가중치(gaussian weights)를 이용한 개선된 퍼지 클러스터(Modified Fuzzy C-Means with Gaussian Weights: MFCM_GW) 알고리즘을 제안한다. 제안된 알고리즘은 기존 FCM 알고리즘의 유클리디언 거리(Euclidean distance) 값 대신 Bayesian Likelihood 목적 함수(fitness function)와 가우시안 가중치가 적용된 멤버십 매트릭스(partition matrix)를 이용하여, 비선형 채널의 출력으로 수신된 데이터들로부터 최적의 채널 출력 상태 값(optimal channel output states)들을 직접 추정한다. 이렇게 추정된 채널 출력 상태 값들로 비선형 채널의 이상적 채널 상태(desired channel states) 백터들을 구성하고, 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용함으로써 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우시안 잡음이 추가된 데이터를 사용하여 기존의 Simplex Genetic Algorithm(GA), 하이브리드 형태의 GASA(GA merged with simulated annealing(SA)), 그리고 과거에 발표되었던 MFCM 등과 그 성능을 비교 분석하였으며, 가우시안 가중치가 적용된 MFCM_GW를 이용한 채널등화기가 상대적으로 정확도와 속도 면에서 우수함을 보였다.

• 한국산업정보학회논문지
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• 제25권6호
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• pp.159-169
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• 2020

본 연구에서는 중국에서 유통되고 있는 냉장식품 (-1℃ to 8℃)에 대한 콜드체인 물류(Cold chain logistics: CCL) 모델이 개발되었다. CCL 모델은 분배센터 (Distribution center: DC)와 배포대상지점 (Distribution target points: DT)으로 구성되어 있으며, CCL 모델의 목적함수는 모든 유통업체의 총 유통경로를 최소화하는 것이다. 목적함수를 통한 최적결과 도출을 위해 혼합유전알고리즘(Hybrid genetic algorithm: HGA) 접근법을 제안한다. HGA 접근법은 개선된 K-means 방법과 유전알고리즘을 결합하여 구성된다. 사례연구에서는 유통경로와 유통 가능한 거리 기준으로 CCL 모델에 대해 3개의 시나리오를 고려하였으며, 이를 제안된 HGA접근법을 사용하여 해결하였다. 결과분석을 통해 제안된 HGA접근법을 사용할 경우 유통비용이 절감되고, 마일리지가 약 19%, 20%, 16% 정도 감소됨을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.174-176
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• 1997

This paper proposes a unit commitment scheduling method based on hybrid genetic algorithm(GA). When the systems are scaled up, conventional genetic algorithms suffer from computational time limitations because of the growth of the search space. So greatly reduce the search space of the GA and to efficiently deal with the constraints of the problem, priority list unit ordering scheme are incorporated as the initial solution and the minimum up and down time constraints of the units are included. The violations of other constraints are handled by integrating penalty factors. To show the effectiveness of the proposed method. test results for system of 10 units is compared with results obtained using other methods.

• 대한전기학회논문지:전력기술부문A
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• 제49권2호
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• pp.70-77
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• 2000

This paper proposes a method of solving a unit commitment problem using tabu search (TS) which is heuristic algorithm. Ts is a local search method that starts from any initial solution and attempts to determine a better solution using memory structures. In this paper, to reduce the computation time for finding the optimal solution, changing tabu list size as intensification strategy and path relinking method as diversification strategy are proposed. To show the usefulness of the proposed method, we simulated for 10 units system and 110 units system. Numerical results show improvements in the generation costs and the computation time compared with priority list, genetic algorithm(GA), and hybrid GA.

• Structural Engineering and Mechanics
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• 제16권5호
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• pp.557-580
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• 2003

Laminated composite structures find wide range of applications in many branches of technology. They are much suited for weight sensitive structures (like aircraft) where thinner and lighter members made of advanced fiber reinforced composite materials are used. The orientations of fiber direction in layers and number of layers and the thickness of the layers as well as material of composites play a major role in determining the strength and stiffness. Thus the basic design problem is to determine the optimum stacking sequence in terms of laminate thickness, material and fiber orientation. In this paper, a new optimization technique called Cellular Automata (CA) has been combined with Genetic Algorithm (GA) to develop a different search and optimization algorithm, known as Cellular Genetic Algorithm (CGA), which considers the laminate thickness, angle of fiber orientation and the fiber material as discrete variables. This CGA has been successfully applied to obtain the optimal fiber orientation, thickness and material lay-up for multi-layered composite hybrid beams plates and shells subjected to static buckling and dynamic constraints.

• 한국지능시스템학회논문지
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• 제8권6호
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• pp.58-69
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• 1998

This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.

• Journal of information and communication convergence engineering
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• 제20권1호
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• pp.31-40
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• 2022

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.