• 대한조선학회논문집
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• 제53권2호
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• pp.85-91
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• 2016

The most important factor in the structural design of ships and offshore structures operating in arctic region is ice load, which results from ice-structure interaction during the ice collision process. The mechanical properties of ice related to strength and failure, however, show very complicated aspect varying with temperature, volume fraction of brine, grain size, strain rate and etc. So it is nearly impossible to establish a perfect material model of ice satisfying all the mechanical characteristics completely. Therefore, in general, ice collision analysis was carried out by relatively simple material models considering only specific aspects of mechanical characteristics of ice and it would be the most significant cause of inevitable errors in the analysis. Especially, it is well-known that the most distinctive mechanical property of ice is high dependency on strain rate. Ice shows brittle attribute in higher strain rate while it becomes ductile in lower strain rate range. In this study, the simulation method of ice collision to ship hull using the nonlinear dynamic FE analysis was dealt with. To consider the strain rate effects of ice during ice-structural interaction, strain rate dependent constitutive model in which yield stress and hardening behaviors vary with strain rate was adopted. To reduce the huge amount of computing time, the modeling range of ice and ship structure were restricted to the confined region of interest. Under the various scenario of ice-ship hull collision, the structural behavior of hull panels and failure modes of ice were examined by nonlinear FE analysis technique.

• Earthquakes and Structures
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• 제18권5호
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• pp.649-665
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• 2020

One of the most important issues in structural systems is evaluation of the margin of safety in low and mid-rise buildings against the progressive collapse mechanism due to the earthquake loads. In this paper, modeling of collapse propagation in structural elements of RC frame buildings is evaluated by tracing down the collapse points in beam and column structural elements, one after another, under earthquake loads and the influence of column removal is investigated on how the collapse expansion in beam and column structural members. For this reason, progressive collapse phenomenon is studied in 3-story and 5-story intermediate moment resisting frame buildings due to the corner and edge column removal in presence of the earthquake loads. In this way, distribution and propagation of the collapse in progressive collapse mechanism is studied, from the first element of the structure to the collapse of a large part of the building with investigating and comparing the results of nonlinear time history analyses (NLTHA) in presence of two-component accelograms proposed by FEMA_P695. Evaluation of the results, including the statistical survey of the number and sequence of the collapsed points in process of the collapse distribution in structural system, show that the progressive collapse distribution are special and similar in low-rise and mid-rise RC buildings due to the simultaneous effects of the column removal and the earthquake loads and various patterns of the progressive collapse distribution are proposed and presented to predict the collapse propagation in structural elements of similar buildings. So, the results of collapse distribution patterns and comparing the values of collapse can be utilized to provide practical methods in codes and guidelines to enhance the structural resistance against the progressive collapse mechanism and eventually, the value of damage can be controlled and minimized in similar buildings.

• Earthquakes and Structures
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• 제18권6호
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• pp.691-707
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• 2020

One of the most important issues in structural systems is evaluation of the margin of safety in low and mid-rise buildings against the progressive collapse mechanism due to the earthquake loads. In this paper, modeling of collapse propagation in structural elements of RC frame buildings is evaluated by tracing down the collapse points in beam and column structural elements, one after another, under earthquake loads and the influence of column removal is investigated on how the collapse expansion in beam and column structural members. For this reason, progressive collapse phenomenon is studied in 3-story and 5-story intermediate moment resisting frame buildings due to the corner and edge column removal in presence of the earthquake loads. In this way, distribution and propagation of the collapse in progressive collapse mechanism is studied, from the first element of the structure to the collapse of a large part of the building with investigating and comparing the results of nonlinear time history analyses (NLTHA) in presence of two-component accelograms proposed by FEMA_P695. Evaluation of the results, including the statistical survey of the number and sequence of the collapsed points in process of the collapse distribution in structural system, show that the progressive collapse distribution are special and similar in low-rise and mid-rise RC buildings due to the simultaneous effects of the column removal and the earthquake loads and various patterns of the progressive collapse distribution are proposed and presented to predict the collapse propagation in structural elements of similar buildings. So, the results of collapse distribution patterns and comparing the values of collapse can be utilized to provide practical methods in codes and guidelines to enhance the structural resistance against the progressive collapse mechanism and eventually, the value of damage can be controlled and minimized in similar buildings.

• 한국전산구조공학회논문집
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• 제24권6호
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• pp.691-697
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• 2011

본 연구에서는 방호공의 최대방호능력을 산정하기 위하여 선박충돌방호공과 선박을 수치적으로 모델링하고 준정적해석으로 충돌해석을 수행하였다. 방호공은 구조물의 비선형 거동과 지반의 지지효과 및 인발을 고려하여 모델링되었다. 충돌선박은 비선형거동이 집중되는 선수부분을 정밀하게 모델링하고 효율적인 해석을 위해 mass scaling기법을 사용하였다. 동일한 해석모델에 대하여 동적해석을 추가적으로 수행하여 두 해석방법의 차이점과 효율성을 평가하였다. 선박과 방호공의 에너지소산곡선을 바탕으로 충돌선박이 교량하부구조에 충돌력을 전달되는 시점을 추정하고, 이를 바탕으로 대상선박의 최대충돌허용속도를 산정하였다. 이러한 추정방법이 방호공의 에너지소산한계를 명확히 판단할 수 있어 공학적으로 효율적인 산정방법임을 보였다.

• 한국지능시스템학회논문지
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• 제19권5호
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• pp.689-694
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• 2009

본 논문은 일별 및 주별로 시계열 주가를 예측할 수 있는 퍼지 모델을 구성하는 방법을 제안한다. 전통적인 시계열 분석으로 주가를 예측하는 것은 어렵지만 퍼지 모델은 비선형적인 주가 데이터의 특성을 잘 기술할 수 있는 장점을 갖고 있다. 주가 예측 모델에 사용될 입력 정보를 결정하는 데는 상당한 수고가 필요한데, 본 논문에서는 전통적인 캔들 스틱 차트의 정보를 입력변수로 고려한다. 주가 예측 퍼지 모델은 사다리꼴 멤버쉽함수를 갖는 전건부와 비선형식인 후건부로 된 퍼지 규칙으로 구성된다. 차분 진화를 통해 퍼지 모델은 최적화된다. 일별 및 주별로 코스피 지수의 시가, 고가, 저가 및 종가를 예측하는 모델을 만들고 그 성능을 평가한다.

• 비파괴검사학회지
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• 제28권2호
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• pp.119-124
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• 2008

최근 비파괴 검사를 위한 테라헤르츠 전자기파 기술에 대한 관심이 높아지고 있으며 특히 비파괴검사에서 많은 응용이 기대된다. 테라헤르츠 시간 영역 분광법은 이러한 테라헤르츠 기술에 핵심이 되는 기술로 많은 실험이 이루어지고 있다. 본 논문에서는 테라헤르츠 시간 영역 분광에서 비선형 전광물질을 이용하는 광정류 방식을 통해 발생된 테라헤르츠 전자기파 스펙트럼이 비선형 맥스웰 방정식의 해와 실험에 의해 결정되는 흡수, 회절, 표면간섭 효과 등을 고려한 본문의 모델을 통하여 예측가능하며 실제 티타늄:사파이어 레이저 펄스를 $400{\mu}m$ CdTe에 조사하여 발생된 테라헤르츠파 주파수 스펙트럼의 측정 결과와 비교하여 매우 유사하다는 사실을 보여준다. 이를 통하여 본문에서 소개된 모델은 다른 전광물질을 통해 발생된 테라헤르츠 스펙트럼에도 확장되어 적용 될 수 있다.

• Steel and Composite Structures
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• 제50권6호
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• pp.627-641
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• 2024

This paper presents an experimental and numerical study to investigate the behavior of the precast segmental concrete beams (PSCBs) utilizing high-strength concrete (HSC) connected in the zone of the maximum bending moment using steel extended endplate connections (EECs). The experimental study consisted of five beams as follows: The first beam was the control beam for comparison, which was an unconnected one-piece beam made of HSC. The other four other beams consisted of two identical pieces of precast concrete. An important point to be noted is that at the end of each piece, a steel plate was used with a thickness of 10 mm. Moreover, this steel plate was welded to the lower and upper reinforcing bars of the beam. Furthermore, the steel plate was made to connect the two pieces using the technique of EECs. Several variables were taken in these four beams, whether from the shape of the connection or enhancing the behavior of the connection using the post-tensioning technique. EECs without stiffeners were used for some of the tested beams. The behavior of these connections was improved using stiffeners and shear bolts. To get accurate results, a comparison was made between the behaviors of the five beams. Another important point to be noted is that Abaqus and SAP2000 programs were used to investigate the behavior of PSCBs and to ensure the accuracy of the modeling process which showed a good agreement with the experimental results. Additionally, the simplified modeling using SAP2000 was able to model the nonlinear behavior of PSCBs connected using steel EECs. It was found that the steel pre-tensioned bolted EECs, reinforced with steel stiffeners and shear anchors, could be used to connect the precast HSC segmental beams via the internal pre-stressing technique.

• 전기학회논문지
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• 제60권4호
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• 한국정보통신학회논문지
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• 제23권2호
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• pp.117-126
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• 2019

부정맥 분류를 위한 기존 연구들은 분류의 정확성을 높이기 위해 신경망, 퍼지, 시계열 주파수 분석, 비선형 분석법 등이 연구되어 왔다. 이러한 방법들은 분류율를 향상시키기 위해 정확한 특징점과 많은 양의 신호를 처리해야 하기 때문에 데이터의 가공 및 연산이 복잡하며, 다양한 부정맥을 분류하는데 어려움이 있다. 본 연구에서는 AR(Auto Regressive) 모델링 기반의 특징점 추출과 SVM(Support Vector Machine)을 통한 조기수축 부정맥 분류 방법을 제안한다. 이를 위해 잡음을 제거한 ECG 신호에서 R파를 검출하고 QRS와 RR 간격의 특정 파형 구간을 모델링하였다. 이후 최적 세그먼트 길이(n1, n2), 최적 차수( p1, p2)의 4가지 AR 모델링 변수를 추출하고 SVM을 통해 Normal, PVC, PAC를 분류하였다. 연구의 타당성을 입증하기 위해 MIT-BIH 부정맥 데이터베이스를 대상으로 한 R파의 평균 검출 성능은 99.77%, Normal, PVC, PAC 부정맥은 각각 99.23%, 97.28, 96.62의 평균 분류율을 나타내었다.

• Computers and Concrete
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• 제11권4호
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.