• Smart Structures and Systems
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• 제16권1호
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• pp.1-26
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• 2015

Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

• 디지털융복합연구
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• 제16권6호
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• pp.85-93
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• 2018

대표적인 종단자료 분석방법인 잠재성장모형(Latent Growth Modeling)은 무조건적 모델과 조건적 모델로 구분되는데, 이중 무조건적 모델은 초기값과 기울기를 추정하여 적합도가 높은 모델을 추정해야 한다. 그렇지만 기존 잠재성장모형에는 종단자료의 형태가 단순선형함수 등 특정 함수가 아닐 경우 기울기를 추정하는 체계적인 방법론이 없었다. 본 연구에서는 뮤조건적 모델의 기울기를 추정하는데 연관규칙(Association Rule Mining)의 순차패턴(Sequential Pattern)을 사용하였다. 데이터는 한국고용정보원의 2001년~2006년에 조사한 청년 패널 데이터를 사용하였다. 제안한 방법론은 기존 단순선형함수를 가정할 때와 비교하여 적합도가 상승하는 것을 확인할 수 있었으며, 기울기 추정 과정을 시각화할 수 있는 부수적인 장점이 있었다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 하계학술대회 논문집 Vol.4 No.1
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• pp.252-255
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• 2003

The pulsed laser deposition process modeling is investigated using neural networks based on radial basis function networks and multi-layer perceptron. Two input factors are examined with respect to the PL intensity. In order to minimize the joint confidence region of fabrication process with varying the conditions, D-optimal experimental design technique is performed and photoluminescence intensity is characterized by neural networks. The statistical results were then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can be optimized process conditions for pulsed laser deposition process.

• 한국전자파학회논문지
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• 제30권4호
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• pp.261-269
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• 2019

본 논문은 레이돔과 같은 다층구조의 주파수 선택적 표면(frequency selective surfaces: FSS)을 설계하는데, 편파나 입사각 등 다양한 고려사항에 대한 유연성을 갖는 픽셀 형태의 주파수 선택적 표면을 설계하는 것에 관한 것이다. 픽셀 형태의 FSS를 설계할 때 이산 공간 문제를 해결할 수 있는 다양한 방법 중 이진 입자 군집 최적화(binary particle swarm optimization: BPSO) 알고리즘은 FSS의 주기구조 패턴을 결정하는데 쉽게 적용 가능한 기술 중 하나이며, 따라서 향상된 BPSO 알고리즘을 통해 롤 오프 전파 투과특성을 갖는 FSS를 효율적으로 설계하는 기법을 제안하였다. 원하는 솔루션에 입자를 유도하기 위한 적합성 함수 설계에 대하여 수렴속도 문제를 해결하기 위해, '기울기'를 입력 변수로 한 적합성 함수를 적용할 경우 쉽게 원하는 전파특성을 갖는 FSS를 얻을 수 있었다.

• 한국음향학회지
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• 제28권4호
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• pp.363-369
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• 2009

본 논문에서는 인접된 다중모드 공진점을 갖는 수중 음향 압전 트랜스듀서의 전기적 등가회로 모델을 추정하는 방법을 제안하였다. 트랜스듀서의 실측된 임피던스와 추정된 등가모델의 임피던스 오차가 최소가 되도록 공진모드간 결합 영향을 고려한 적합도 함수를 제안하고, 미립자 집단 최적화 (PSO:Particle Swarm Optimization) 알고리즘을 이용하여 등가회로의 미지상수를 추정하였다. 3개의 공진점을 갖는 샌드위치형 예제 트랜스듀서에 대하여 제안된 방법을 적용하여 등가회로를 모델링하고, 수중에서의 임피던스 측정치와 추정된 등가모델의 임피던스를 비교함으로써 제안된 기법의 타당성을 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.93_94
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• 2009

Load modeling has a significant impact on power system analysis and control. Estimating model parameters can be considered as important as stability analysis itself for accurate analysis and control. This paper presents a method for estimating parameters for load models, which include static and dynamic parts, based on particle swarm optimization. The method effectively searches a suitable set of parameters minimizing the fitness function. This paper applies the method to simulation data obtained from 8-bus test system including induction motors.

• 소성∙가공
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• 제30권1호
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• pp.27-34
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• 2021

In this study, response surface method (RSM) was used in modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goals of optimization were the maximum forming angle, minimum thickness reduction, and minimum surface roughness, with varying values in response to changes in production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model for modeling the variations in the forming angle, thickness reduction, and surface roughness in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process based on experimental design. The results showed that RSM can be effectively used to control the forming angle, thickness reduction, and surface roughness.

• 환경영향평가
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• 제20권3호
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• pp.357-365
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• 2011

The QUAL2K has the same basic characteristics as the QUAL2E model, which has been widely used in stream water quality modeling; in QUAL2K, however, various functions are supplemented. The QUAL2Kw model uses a genetic algorithm(GA) for automatic calibration of QUAL2K, and it can search for optimum water quality parameters efficiently using the calculation results of the model. The QUAL2Kw model was applied to the Gangneung Namdaecheon River on the east side of the Korean Peninsula. Because of the effluents from the urban area, the middle and lower parts of the river are more polluted than the upper parts. Moreover, the hydraulic characteristics differ between the lower and upper parts of rivers. Thus, the river reaches were divided into seven parts, auto-calibration for the multiple reaches was performed using the function of the user-defined automatic calibration of the rates worksheets. Because GA parameters affect the optimal solution of the model, the impact of the GA parameters used in QUAL2Kw on the fitness of the model was analyzed. Sensitivity analysis of various factors, such as population size, crossover probability, crossover mode, strategy for mutation and elitism, mutation rate, and reproduction plan, were performed. Using the results of this sensitivity analysis, the optimum GA parameters were selected to achieve the best fitness value.

• 소성∙가공
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• 제30권3호
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.223-228
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• 2021

As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios.