• Journal of Magnetics
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• 제17권4호
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• pp.291-297
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• 2012

This paper presents an efficient methodology for accurate reliability assessment of electromagnetic devices. To achieve the goal, elaborate surrogated models to approximate constraint functions of interest are generated based on the dynamic Kriging method and a hypercube local window. Then, the Monte Carlo simulation scheme is applied to the surrogate models. This leads to reducing computational cost dramatically without degrading accuracy of the reliability analysis. The validity of the proposed method is tested and examined with a mathematical example and a loudspeaker design.

• 소성∙가공
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• 제12권5호
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• pp.465-472
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• 2003

Engineering design can be viewed as a decision making process, which involves the nonlinear tradeoffs task among the multiple conflicting attributes and considers the robustness of design. In order to obtain best engineering design, methodology for accurate assessment of his/her preference about the multiple attributes is required. Conventionally, intuitive procedures based on lottery questions are used to elicit the designer's preference structure: however, they can lead to inconsistent and inexact preference results due to the rank reversal problems derived from the designer's big cognitive burden. In this paper, alternatively, a design methodology based on multiattribute utility function through the pairwise comparison among alternatives is presented. The proposed procedure is applied to an actual injection mold design with the aid of the CAE simulation and the result is discussed.

• 정보과학회 논문지
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• 제44권9호
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• pp.992-1004
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• 2017

이미지 품질을 정확히 평가하기 위해 이미지 평가 도구는 인간 시각 시스템을 반영해야 한다. 즉, 이미지의 구조, 색, 명암 비 등 여러 가지 요소들을 고려하여 평가해야 한다. 또한 스마트 폰과 같은 모바일 임베디드 기기의 폭넓은 사용에 따라 빠른 수행 속도를 갖는 것이 중요하다. 본 논문에서는 인간 시각 만족과 빠른 계산속도 달성을 동시에 얻기 위하여 색 유사도, 변화율 유사도, 위상 유사도를 상승적으로 결합하였고 최적화된 이미지 풀링 및 양자화 기반으로 설계하였다. 제안하는 기법은 기존에 존재하는 13개의 기법과 비교하였고 네 가지 검증 도구를 사용하여 성능을 검증하였다. 실험 결과 세 검증 도구에서 가장 우수한 성능을 보였고 한 검증 도구에서 기존 최고 기법인 VSI에 이어 두 번째로 좋은 성능을 보였으며 실행 속도는 VSI에 대해 평균 약 20% 개선된 결과를 얻었다. 또한 기존의 기법들 보다 더 인간 시각 시스템과 제안 기법의 품질 평가 값의 연관성이 크게 존재함을 확인하였다.

• 한국수자원학회논문집
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• 제50권2호
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• pp.121-128
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• 2017

본 연구는 강건성 지수와 불확실성 분석기법을 활용하여 기후변화 취약성 평가과정에서 발생하는 불확실성을 정량화하였다. 본 연구는 우리나라의 6개 광역시(부산, 대구, 인천, 광주, 대전, 울산)를 대상으로 다기준 의사결정기법 중 하나인 TOPSIS 기법을 이용하여 용수공급 취약성 순위를 산정하였다. 강건성 지수는 두 대상 도시의 순위가 가중치의 변화로 인해 순위역전현상이 발생할 수 있는 가능성을 정량화하고 불확실성 분석 기법은 두 도시 사이에 순위역전이 발생할 수 있는 가중치의 최소 변화량을 산정한다. 그 결과 인천과 대구는 용수공급 측면에서 취약한 것으로 나타났으며, 대구와 부산은 용수공급 취약성에 민감한 것으로 나타났다. 따라서 대구는 다른 대안에 비해 상대적으로 용수공급이 취약한 지역으로 나타났으나, 취약성에 민감하기 때문에 기후변화 적응대책 수립 및 시행을 통해 취약성이 크게 향상될 수 있을 것으로 판단된다. 본 연구는 기후변화와 용수공급 측면에서의 적응전략을 계획하고 수립하는데 있어서 우선적으로 고려해야하는 방향을 제안하는 데 사용될 수 있다.

• Smart Structures and Systems
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• 제31권1호
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1997년도 봄 학술발표회 논문집
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• pp.89-96
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• 1997

Most civil engineering structures, such as highway bridges, towers, power plants and offshore structures suffer structural damages over their service lives caused by adverse loading such as heavy transportation loads, machine vibrations, earthquakes, wind and wave forces. Especially, if excessive load would be acted on the structure, general or partial stiffness should be degraded suddenly and service lives should be shortened eventually For realistic damage assessment of these civil structures, System Identification method using only structure dynamic response data with unknown input excitation is required and thus becoming more challenging problem. In this paper, an improved Iterative Least Squares method is proposed, which seems to be very efficient and robust method, because only the dynamic response data such as acceleration, velocity and displacement is used without input data, and no information on the modal properties is required. The efficiency and robustness of the proposed method is proved by numerical problems and real single span beam model test.

• 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 Electrical Engineering and Technology
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• 제7권1호
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• pp.46-50
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• 2012

The current paper presents an effective methodology for assessing the reliability of electromagnetic designs when considering uncertainties of design variables. To achieve this goal, the reliability index approach based on the first-order reliability method is adopted to deal with probabilistic constraint functions, which are expressed in terms of random design variables. The proposed method is applied to three different designs of a superconducting magnetic energy storage system that corresponds to initial, deterministic, and roust designs. The validity and efficiency of the method is investigated with reference values obtained from Monte Carlo simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2253-2272
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• 2018

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.