• Smart Structures and Systems
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• 제15권2호
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• pp.355-373
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• 2015

An optimal sensor placement (OSP) method based on structural subsection technique (SST) and model reduction technique was proposed for modal identification of truss structures, which was conducted using genetic algorithm (GA). The constraints of GA variables were determined by SST in advance. Subsequently, according to model reduction technique, the optimal group of master degrees of freedom and the optimal objective function value were obtained using GA in a case of the given number of sensors. Correspondingly, the optimal number of sensors was determined according to optimal objective function values in cases of the different number of sensors. The proposed method was applied on a scaled jacket offshore platform to get its optimal number of sensors and the corresponding optimal sensor layout. Then modal kinetic energy and modal assurance criterion were adopted to evaluate vibration energy and mode independence property. The experiment was also conducted to verify the effectiveness of the selected optimal sensor layout. The results showed that experimental modes agreed reasonably well with numerical results. Moreover the influence of the proposed method using different optimal algorithms and model reduction technique on optimal results was also compared. The results showed that the influence was very little.

• Smart Structures and Systems
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• 제13권1호
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• pp.99-109
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• 2014

The goal of any Model Order Reduction (MOR) technique is to build a model of order lower than the one of the real model, so that the computational effort is reduced, and the ability to estimate the input-output mapping of the original system is preserved in an important region of the input space. Actually, since only a subset of the input space is of interest, the matching is required only in this subset of the input space. In this contribution, the consequences on the achieved accuracy of adopting different reduction technique patterns is discussed mainly with reference to a linear case study.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 추계종합학술대회
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• pp.383-386
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• 2005

This study proposes a case based reasoning system with two dimensional reduction techniques. In this study, vertical and horizontal dimensions of the research data are reduced through hybrid feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of typical CBR system.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.180-185
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• 2008

A new method for relative error continuous and discrete time model order reduction is proposed. The reduction technique is based on two recently developed methods, namely frequency domain balanced truncation within a frequency bound and inner-outer factorization techniques. The proposed method is of interest for practical model order reduction because in this context it shows to keep the accuracy of the approximation as high as possible without sacrificing the computational efficiency. Numerical results show the accuracy and efficiency enhancement of the method.

• Wind and Structures
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• 제9권6호
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• pp.441-455
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• 2006

A more applicable optimization model for extracting flutter derivatives of bridge decks is presented, which is suitable for time-varying weights for fitting errors and different lengths of vertical bending and torsional free vibration data. A stochastic search technique for searching the optimal solution of optimization problem is developed, which is more convenient in understanding and programming than the alternate iteration technique, and testified to be a valid and efficient method using two numerical examples. On the basis of the section model test of Sutong Bridge deck, the flutter derivatives are extracted by the stochastic search technique, and compared with the identification results using the modified least-square method. The Empirical Mode Decomposition method is employed to eliminate noise, trends and zero excursion of the collected free vibration data of vertical bending and torsional motion, by which the identification precision of flutter derivatives is improved.

• 한국음향학회지
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• 제20권6호
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• pp.46-50
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• 2001

본 논문은 HMM (Hidden Markov Model) 음성 인식 시스템에 적용할 수 있는 새로운 인식 시간 알고리즘인 스코아 캐쉬기법을 제안한다. 다른 많은 기법들이 인식 시간을 줄이면서 계산량을 줄이기 위하여 어느 정도의 인식율 저하를 감수하는 반면에 제안하는 스코아 캐쉬기법은 인식율 저하를 전혀 일으키지 않으면서 인식 시간을 상당량 줄일 수 있는 기법이다. 단독어 인식 시스템에 적용 가능할 뿐 아니라 연속어 인식에도 적용이 가능하며, 기존에 이미 설계된 인식 시스템의 구조를 전혀 흩트리지 않고 간단히 하나의 함수만 대치함으로서 인식시간을 크게 감축할 수 있다 또한 기존의 계산량 감축 알고리즘과 함께 적용 가능하므로 추가의 계산량 감소를 얻을 수 있다. 스코아 캐쉬 기법을 적용한 결과 최대 54% 만큼 계산량을 줄일 수 있었다.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.413-419
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• 1998

A novel order-reduction technique for model predictive control(MPC) is proposed based on the singular value decomposition(SVD) of a pulse response circulant matrix(PRCM) of a concerned system. It is first investigated that the PRCM (in the limit) contains a complete information of the frequency response of a system and its SVD decomposes the information into the respective principal directions at each frequency. This enables us to isolate the significant modes of the system and to devise the proposed order-reduction technique. Though the primary purpose of the proposed technique is to diminish the required computation in MPC, the clear frequency decomposition of the SVD of the PRCM also enables us to improve the robustness through selective excitation of frequency modes. Performance of the proposed technique is illustrated through two numerical examples.

• International Journal of Control, Automation, and Systems
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• 제5권2호
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• pp.208-211
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• 2007

In a recently proposed method of model reduction for discrete interval systems, the denominator polynomial of a reduced model is computed by applying interval arithmetic to dominant poles of the original system. However, the denominator polynomial obtained via interval arithmetic usually has poles with larger intervals than desired ones. Hence an unstable polynomial can be derived from the stable polynomial. In this paper a simple technique is presented to partially overcome such a stability problem by accurately preserving desired real dominant poles.

• 한국지반신소재학회논문집
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• 제20권2호
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• pp.35-43
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• 2021

본 논문에서는 고속철도 교량-열차 상호작용 해석을 위한 단순 3 차원 상호작용 해석모델을 기반으로 하여 정식화한 부구조화 기법 적용 상호작용 해석모델을 제시한다. 부구조화 기법에서는 철도 교량의 상부 구조와 지지 구조를 각각 부구조로 모델링하고, 열차-교량 상호작용 해석을 효율적으로 수행할 수 있다. 열차 해석 모델로는 2차원 열차 모델을 사용하고, Lagrange 운동방정식을 적용하여 2차원 열차의 운동방정식을 유도한다. 부구조화 기법에서는 응축 방법을 사용하여 자유도(Degree of freedom)의 수를 줄일 수 있으므로 고유 값 및 고유 벡터 계산을 위한 소요 시간 및 비용과 후속 계산의 소요시간 및 비용이 줄어든다. 본 논문에서는 부구조화 기법으로 Guyan 감소 방법을 사용한다. 단순 3 차원 교량-열차 상호작용 해석과 Guyan 감소 방법을 결합하여 효율적이고 정확한 교량-열차 상호작용 해석을 수행할 수 있다.

• Structural Engineering and Mechanics
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• 제61권6호
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• pp.807-816
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• 2017

In this paper, we introduce an efficient new model reduction method, named the automated static condensation method, which is developed for the local analysis of large finite element models. The algebraic multilevel substructuring procedure is modified appropriately, and then applied to the original static condensation method. The retained substructure, which is the local finite element model to be analyzed, is defined, and then the remaining part of the global model is automatically partitioned into many omitted substructures in an algebraic perspective. For an efficient condensation procedure, a substructural tree diagram and substructural sets are established. Using these, the omitted substructures are sequentially condensed into the retained substructure to construct the reduced model. Using several large practical engineering problems, the performance of the proposed method is demonstrated in terms of its solution accuracy and computational efficiency, compared to the original static condensation method and the superelement technique.