• 콘크리트학회논문집
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• 제13권4호
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• pp.305-313
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• 2001

본 연구에서는 콘크리트 열전도율의 영향인자에 대하여 TLPP원리를 응용한 QTM-D3 장비를 이용하여 실험을 실시하였고, 이들 실험결과를 이용하여 콘크리트의 열전도율을 예측하는 모델식을 제안하였다. 본 연구를 통하여 얻은 결론은 다음과 같다. 콘크리트, 모르타르 및 페이스트의 열전도율에 미치는 주요 영향인자를 구명하기 위해 본 연구에서 선택된 실험변수는 재령, 골재 함유량, 시멘트 함유량, 결합재 종류, 잔골재율, 시편의 온도 및 함수상태로 총 7가지이다. 이중에서 골재 함유량과 함수상태가 콘크리트 열전도율의 주요 영향인자임을 알 수 있었다. 그리고 시멘트 사용량이 많은 페이스트나 모르타르의 경우 시멘트 함유량이나 결합재 종류에 의해서도 열전도율이 영향을 받고 있다. 그러나 재령은 콘크리트의 열전도율에 큰 영향을 미치지 않음을 알 수 있었다. 콘크리트 열전도율에 주요 영향인자인 골재 함유량, 잔골재율, 시편의 온도 및 함수상태를 이용하여 콘크리트의 열전도율을 계산할 수 있는 모델식을 제안하였다.

• Computers and Concrete
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• 제22권4호
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• pp.419-437
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• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• International Journal of Control, Automation, and Systems
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• 제1권3호
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2911-2913
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• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Computers and Concrete
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• 제10권2호
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• pp.197-217
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• 2012

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

• The Journal of Asian Finance, Economics and Business
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• 제4권1호
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• pp.19-23
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• 2017

Based on a simultaneous-equation model consisting of aggregate demand and short-run aggregate supply, this paper estimates a reduced-form equation specifying that the equilibrium real GDP is a function of the real effective exchange rate, the government deficit as a percent of GDP, the real interest rate, foreign income, labor productivity, the real oil price, the expected inflation rate, and the interactive and intercept binary variables accounting for a potential change in the slope of the real effective exchange rate and shift in the intercept. Applying the exponential GARCH technique, it finds that aggregate output in Australia has a positive relationship with the real effective exchange rate during 2003.Q3 - 2013.Q2, the government deficit as a percent of GDP, U.S. real GDP, labor productivity and the real oil price and a negative relationship with the real effective exchange rate during 2013.Q3 - 2016.Q1, the real lending rate and the expected inflation rate. These results suggest that real appreciation was expansionary before 2013.Q3 whereas real depreciation was expansionary after 2013.Q2 and that more government deficit as a percent of GDP would be helpful to stimulate the economy. Hence, the impact of real appreciation or real depreciation on real GDP may change overtime.

• The Journal of Asian Finance, Economics and Business
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• 제5권1호
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• pp.5-9
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• 2018

Many developing countries have attempted to depreciate their currencies in order to make their products cheaper, stimulate exports, shift aggregate demand to the right, and increase aggregate output. However, currency depreciation tends to increase import prices, raise domestic inflation, reduce capital inflows, and shift aggregate supply to the left. The net impact is unclear. The paper incorporates the monetary policy function in the model, which is determined by the inflation gap, the output gap, the real effective exchange rate, and the world real interest rate. Applying an extended IS-MP-AS model (Romer, 2000), the paper finds that real depreciation raised real GDP during 1997.Q1-2005.Q3 whereas real appreciation increased real GDP during 2005.Q4-2017.Q2. In addition, a higher government debt-to-GDP ratio, a lower U.S. real federal funds rate, a higher real stock price, a lower real oil price or a lower expected inflation rate would help increase real GDP. Hence, real depreciation or real appreciation may increase or reduce aggregate output, depending upon the level of economic development. Although expansionary fiscal policy is effective in stimulating the economy, caution needs to be exercised as there may be a debt threshold beyond which a further increase in the debt-to-GDO ratio would hurt economic growth.

• 대한건축학회논문집:구조계
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• 제34권7호
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• pp.19-26
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• 2018

This study tested 25 lightweight aggregate concrete (LWAC) mixtures using the expanded bottom ash and dredged soil granules to examine the compressive strength gain of such concrete with different ages. The test parameters investigated were water-to-cement ratios and the natural sand content for the replacement of lightweight fine aggregate. The compressive strength gain rate in the basic equation specified in fib model code was experimentally determined in each mixture and then empirically formulated as a function of the water-to-cement ratio and oven-dried density of concrete. When compared with 28-day compressive strength, the tested LWAC mixtures exhibited relatively low gain ratios (0.49~0.82) at an age of 3 days whereas the gain ratios (1.16~1.41) at 91 days were higher than that (1.05~1.15) of the conventional normal-weight concrete. Thus, the fib model equations tend to overestimate the early strength gain of LWAC but underestimate the long-term strength gain. The proposed equations are in good agreement with the measured compressive strength development of LWAC at different ages, indicating that the mean and standard deviation of the normalized root mean square errors determined in each mixture are 0.101 and 0.053, respectively.

• 한국지능시스템학회논문지
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• 제10권5호
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• pp.487-496
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• 2000

본 논문에서는, HCM 클러스러팅 방법과 유전자 알고리즘을 이용하여 다중 FNN 모델을 동정하고 최적화 한다. 제안된 다중 FNN은 Yamakawa의 FNN을 기본으로 하며, 퍼지 추론 방법으로 간략 추론을, 학습으로는 오류 역전파 알고리즘을 사용한다. 다중 FNN 모델의 구조와 파라미터를 동정하기 위해 HCM 클러스터링과 유전자 알고리즘을 사용한다. 여기서, 시스템 모델링을 위해 데이터 전처리 기능을 수행하는 HCM클러스터링 방법은 I/O 프로세서 공정 데이터를 이용하여 입출력 공간분할에 의한 다중 FNN 구조를 결정하기 위해 사용된다. 또한 유전자 알고리즘을 사용하여 멤버쉽함수의 정점, 학습율, 모멘텀 계수와 같은 다중 FNN 모델의 파라미터들을 동조한다. 모델의 근사화와 일반화 능력 사이에 합히적 균형을 얻기 위해 하중계수를 가진 합성 성능지수를 사용한다. 이 합성 성능지수는 근사화 및 예측 능력사이의 상호 균형과 의존성을 고려한 하중계수를 가진 합성 목적함수를 의미한다. 데이터 개수, 비선형성의 정도에 의존하는 이 합성 목적함수의 하중계수의 선택, 조절을 통하여 최적의 다중 FNN 모델을 설계하는 것이 유용하고 효과적임을 보인다. 제안된 모델의 성능 평가를 위하여 가스로 공정의 시계열 데이터와 비선형 함수의 수치 데이터를 사용한다.

• Computers and Concrete
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• 제17권5호
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• pp.629-638
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• 2016

Optimization of the concrete mixture design is a process of search for a mixture for which the sum of the cost of the ingredients is the lowest, yet satisfying the required performance of concrete. In this study, a statistical model was carried out to model a cost effective optimal mix proportioning of high strength self-compacting concrete (HSSCC) using the Response Surface Methodology (RSM). The effect of five key mixture parameters such as water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content on the properties and performance of HSSCC like compressive strength, passing ability, segregation resistance and manufacturing cost were investigated. To demonstrate the responses of model in quadratic manner Central Composite Design (CCD) was chosen. The statistical model showed the adjusted correlation coefficient R2adj values were 92.55%, 93.49%, 92.33%, and 100% for each performance which establish the adequacy of the model. The optimum combination was determined to be $439.4kg/m^3$ cement content, 35.5% W/B ratio, 50.0% fine aggregate, $49.85kg/m^3$ fly ash, and $7.76kg/m^3$ superplasticizer within the interest region using desirability function. Finally, it is concluded that multiobjective optimization method based on desirability function of the proposed response model offers an efficient approach regarding the HSSCC mixture optimization.