• Structural Engineering and Mechanics
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• 제71권4호
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• pp.417-432
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• 2019

Seismic performance analysis of steel-brace reinforced concrete (RC) frame using topology optimization in highly seismic region was discussed in this research. Topology optimization based on truss-like material model was used, which was to minimum volume in full-stress method. Optimized bracing systems of low-rise, mid-rise and high-rise RC frames were established, and optimized bracing systems of substructure were also gained under different constraint conditions. Thereafter, different structure models based on optimized bracing systems were proposed and applied. Last, structural strength, structural stiffness, structural ductility, collapse resistant capacity, collapse probability and demolition probability were studied. Moreover, the brace buckling was discussed. The results show that bracing system of RC frame could be derived using topology optimization, and bracing system based on truss-like model could help to resolve numerical instabilities. Bracing system of topology optimization was more effective to enhance structural stiffness and strength, especially in mid-rise and high-rise frames. Moreover, bracing system of topology optimization contributes to increase collapse resistant capacity, as well as reduces collapse probability and accumulated demolition probability. However, brace buckling might weaken beneficial effects.

• 전기학회논문지P
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• 제61권2호
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• pp.67-73
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• 2012

This paper proposes optimal maintenance strategies for power distribution systems that involve the use of the reliability-centered maintenance (RCM) method. We developed an improved decision model based on the Markov process. This model can obtain the optimal inspection interval and maintenance method based on the total expected cost. We used ordinal optimization for solving the optimal problem. Optimal maintenance strategies were presented by applying the developed method to the RBTS model. A B/C analysis proved that these strategies offer maximum benefit-to-cost.

• 한국광학회:학술대회논문집
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• 한국광학회 1989년도 제4회 파동 및 레이저 학술발표회 4th Conference on Waves and lasers 논문집 - 한국광학회
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• pp.85-88
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• 1989

Introducing and optimizing it-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a 6x8 node system. Unlike many other neural networks models, this model has stronger error correction capability for correlated images such as "6", "8", "3", and "9". the bit-significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with another neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.uding the adaptive optimization networks is also introduced.

• Advances in nano research
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• 제13권6호
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• pp.587-597
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• 2022

In the present study, a novel optimization method in formation control of multi -system vehicles based on the trajectory of the nearest neighbor trajectory is presented. In this regard, the state equations of each vehicle and multisystem is derived and the optimization scheme based on minimizing the differences between actual positions and desired positions of the vehicles are conducted. This formation control is a position-based decentralized model. The trajectory of the nearest neighbor are optimized based on the current position and state of the vehicle. This approach aids the whole multi-agent system to be optimized on their trajectory. Furthermore, to overcome the cumulative errors and maintain stability in the network a semi-centralized scheme is designed for the purpose of checking vehicle position to its predefined trajectory. The model is implemented in Matlab software and the results for different initial state and different trajectory definition are presented. In addition, to avoid collision avoidance and maintain the distances between vehicles agents at a predefined desired distances. In this regard, a neural fuzzy network is defined to be utilized in conjunction with the control system to avoid collision between vehicles. The outcome reveals that the model has acceptable stability and accuracy.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.412-417
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• 2008

In a traditional topology optimization method, material properties are usually distributed by finite element density and visualized by a gray level image. The distribution method based on element density is adequate for a great mass of 2-D topology optimization problems. However, when it is used for 3-D topology optimization, it is always difficult to obtain a smooth model representation, and easily appears a virtualconnect phenomenon especially in a low-density domain. The 3-D structural topology optimization method has been developed using the node density instead of the element density that is based on SIMP (solid isotropic microstructure with penalization) algorithm. A computer code based on Matlab was written to validate the proposed method. When it was compared to the element density as design variable, this method could get a more uniform density distribution. To show the usefulness of this method, several typical examples of structure topology optimization are presented.

• 전기학회논문지
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• 제60권1호
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• 한국공간구조학회논문집
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• 제20권4호
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• pp.53-62
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• 2020

This study examines the optimum shape of a trolley, the driving device of the retractable membrane roof. The closed-type trolley was determined as the model of the study, and a trolley composed of cylindrical-shaped inner and outer holders was selected as the basic model. Based on this model, a cylindrical-based optimal trolley model was proposed. In the basic trolley model, steel was used for the outer holder, and steel, titanium, and aluminum were used for the inner holder. In each case, the most economical shape for the external load of the basic model was newly proposed through the topology optimization process, and the finite element analysis results of the proposed model were compared to define the durability and economics. Here, topology optimization analysis and finite element analysis used the commercial software ANSYS. As a result of optimization, the volume of the outer holder of the trolley was reduced by 58.2% and the volume of the inner holder was reduced by 25.0% compared to the basic model. In the case of stress, a stress increase of 43.2 to 79.2% occurred depending on the material of the inner holder, but it was found to be significantly lower than the yield strength, thereby ensuring safety.

• 전자공학회논문지CI
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• 제45권3호
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• pp.75-85
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• 2008

임베디드 시스템에서 데이터베이스의 사용이 증가하고 있으며 이의 임베디드 시스템의 저장 장치로 낸드 플래시 메모리가 널리 사용되고 있다. 기존 데이터베이스 시스템의 질의 처리기의 최적화기는 저장 시스템을 디스크로 가정하고 설계되어 있다. 플래시 메모리는 디스크와는 달리 덮어 쓰기 연산을 하기 위해서는 기존 블록을 소거한 후 쓰기 연산을 해야하는 부담이 있다. 그러므로 기존 디스크 기반의 질의 최적화기는 임베디드 시스템에 적합하지 않다. 특히 임베디드 시스템은 전력 소모량을 최소화해야 하나 플래시에서의 빈번한 쓰기 연산은 추가적인 소거 연산으로 인한 전력 소비를 증진시킨다. 본 논문은 임베디드 데이터베이스에서 전력 기반 비용 모델을 새롭게 제시하고, 디스크 기반 비용 모델과 비교하여 제시한 비용 모델과의 차이를 보인다.

• Journal of Magnetics
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• 제21권4호
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• pp.660-665
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• 2016

The electromagnetic apparatus plays an important role in high power electrical systems. It is of great importance to provide an effective approach for the optimization of the high power electromagnetic apparatus. However, premature convergence and few Pareto solution set of the optimization for electromagnetic apparatus always happen. This paper proposed a modified multi-objective particle swarm optimization algorithm based on the niche sorting strategy. Applying to the modified algorithm, this paper guarantee the better Pareto optimal front with an enhanced distribution. Aiming at shortcomings in the closing bounce and slow breaking velocity of electromagnetic apparatus, the multi-objective optimization model was established on the basis of the traditional optimization. Besides, by means of the improved multi-objective particle swarm optimization algorithm, this paper processed the model and obtained a series of optimized parameters (decision variables). Compared with other different classical algorithms, the modified algorithm has a satisfactory performance in the multi-objective optimization problems in the electromagnetic apparatus.

• 산업경영시스템학회지
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• 제38권4호
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• pp.117-131
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• 2015

Conventional data envelopment analysis (DEA) models require that inputs and outputs are given as crisp values. Very often, however, some of inputs and outputs are given as imprecise data where they are only known to lie within bounded intervals. While a typical approach to addressing this situation for optimization models such as DEA is to conduct sensitivity analysis, it provides only a limited ex-post measure against the data imprecision. Robust optimization provides a more effective ex-ante measure where the data imprecision is directly incorporated into the model. This study aims to apply robust optimization approach to DEA models with imprecise data. Based upon a recently developed robust optimization framework which allows a flexible adjustment of the level of conservatism, we propose two robust optimization DEA model formulations with imprecise data; multiplier and envelopment models. We demonstrate that the two models consider different risks regarding imprecise efficiency scores, and that the existing DEA models with imprecise data are special cases of the proposed models. We show that the robust optimization for the multiplier DEA model considers the risk that estimated efficiency scores exceed true values, while the one for the envelopment DEA model deals with the risk that estimated efficiency scores fall short of true values. We also show that efficiency scores stratified in terms of probabilistic bounds of constraint violations can be obtained from the proposed models. We finally illustrate the proposed approach using a sample data set and show how the results can be used for ranking DMUs.