• Computers and Concrete
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• 제19권4호
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• pp.435-442
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• 2017

In recent years, the use of supplementary cementing materials, especially in addition to concrete, has been the subject of many researches. Rice husk ash (RHA) is one of these materials that in this research, is added to the roller compacted concrete as one of the pozzolanic materials. This paper evaluates how different contents of RHA added to the roller compacted concrete pavement specimens, can influence on the strength and permeability. The results are compared to the control samples and determined optimal level of RHA replacement. As it was expected, RHA as supplementary cementitious materials, improved mechanical properties of roller compacted concrete pavement (RCCP). Also, the application of adaptive neuro-fuzzy inference system (ANFIS) in predicting the permeability and compressive strength is investigated. The obtained results shows that the predicted value by this model is in good agreement with the experimental, which shows the proposed ANFIS model is a useful, reliable, fast and cheap tool to predict the permeability and compressive strength. A mean relative error percentage (MRE %) less than 1.1% is obtained for the proposed ANFIS model. Also, the test results and performed modeling show that the optimal value for obtaining the maximum compressive strength and minimum permeability is offered by substituting 9% and 18% of the cement by RHA, respectively.

• 대한토목학회논문집
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• 제28권6B호
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• pp.743-750
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• 2008

최근 들어 지구 온난화로 인한 이상기후의 영향으로 우리나라뿐만 아니라 전 세계적으로 가뭄 및 홍수가 빈번하게 발생하고 있다. 물로 인한 각종 재해는 농촌 지역에서도 피해를 발생시키지만, 특히 인구가 밀집되어 있는 도시 지역에서 큰 피해를 발생시킨다. 도시하천 유역에서의 유출과정은 도달시간이 짧고 홍수량은 급격히 증가하는 특성을 보이므로 호우가 발생하면 대처할 시간이 충분하지 않아 피해가 크게 발생할 가능성이 높다. 도시하천 유역에서 호우로 인한 피해를 경감시키기 위한 하나의 대안으로 홍수예 경보를 위한 의사결정시스템을 개발하였다. 도시하천의 홍수예 경보를 위한 의사결정시스템은 ANFIS (Adaptive Neuro Fuzzy Inference System)을 이용한 모형관리시스템, 실시간 자료를 구축할 수 있는 데이터베이스 관리시스템, 그리고 사용자가 이용하기 편리한 인간과 컴퓨터 사이의 대화관리시스템로 구성되어 있다. 개발된 시스템을 탄천 유역에 적용한 결과 호우로 인한 하천의 유량을 실시간으로 예측하여 사전에 경보를 발생하고 피해를 경감시킬 수 있었다.

• Journal of Power Electronics
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• 제17권6호
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• pp.1587-1599
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• 2017

This paper proposes a real-time implementation of an optimal operation of a double stage grid connected wind power system incorporating an active power filter (APF). The system is used to supply the nonlinear loads with harmonics and reactive power compensation. On the generator side, a new adaptive neuro fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) control is proposed to track the maximum wind power point regardless of wind speed fluctuations. Whereas on the grid side, a modified predictive current control (PCC) algorithm is used to control the APF, and allow to ensure both compensating harmonic currents and injecting the generated power into the grid. Also a type 2 fuzzy logic controller is used to control the DC-link capacitor in order to improve the dynamic response of the APF, and to ensure a well-smoothed DC-Link capacitor voltage. The gained benefits from these proposed control algorithms are the main contribution in this work. The proposed control scheme is implemented on a small-scale wind energy conversion system (WECS) controlled by a dSPACE 1104 card. Experimental results show that the proposed T2FLC maintains the DC-Link capacitor voltage within the limit for injecting the power into the grid. In addition, the PCC of the APF guarantees a flexible settlement of real power exchanges from the WECS to the grid with a high power factor operation.

• 한국지진공학회논문집
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• 제9권2호통권42호
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• pp.7-15
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• 2005

본 연구에서는 하이브리드 면진장치가 설치된 단자유도 구조물의 동적거동을 예측할 수 있는 수치해석모델을 제안한다. 하이브리드 면진장치는 MR 감쇠기와 마찰진자시스템(FPS)으로 구성된다. MR감쇠기의 동적거동을 모형화하기 위하여 뉴로-퍼지 모델을 사용한다. 다양한 변위, 속도, 전압의 조합을 사용하여 MR 감쇠기의 성능실험을 수행한 후 얻어진 데이터를 이용하여 MR 감쇠기 뉴로-퍼지 모델을 ANFIS로 학습시킨다. FPS의 모형화는 본 연구에서 유도한 비선형 모델식에 근거하여 뉴로-퍼지 모형화방법을 사용하여 이루어진다. 본 연구에서는 MR 감쇠기로 전달되는 제어전압을 조절하기 위하여 퍼지논리제어기를 사용한다. 다양한 지진하중을 사용한 진동대 실험을 통하여 얻은 실험체의 동적응답과와 뉴로-퍼지 모형화방법을 사용한 수치해석의 결과를 비교한다. 뉴로-퍼지 모델을 사용하여 MR 감쇠기와 FPS를 모형화해서 수치해석을 수행한 결과 하이브리드 면진장치의 동적거동을 매우 정확하게 예측할 수 있었다.

• Journal of Mechanical Science and Technology
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• 제19권8호
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• pp.1670-1681
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• 2005

In-situ diagnosis of chiller performance is an essential step for energy saving business. The main purpose of the in-situ diagnosis is to predict the performance of a target chiller. Many models based on thermodynamics have been proposed for the purpose. However, they have to be modified from chiller to chiller and require profound knowledge of thermodynamics and heat transfer. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). The effect of sample data distribution on training the ANFIS is investigated. It is found that the data sampling over 10 days during summer results in a reliable ANFIS whose performance prediction error is within measurement errors. The reliable ANFIS makes it possible to prepare an energy audit and suggest an energy saving plan based on the diagnosed chilled water supply system.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.144-147
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• 2000

In this paper, ANFIS intelligence control of a semi-active suspension system is investigated. The strength of the ER damper is controlled by a high voltage power supply. This paper deals with a two-degree-of-freedom suspension using the damper with ERF for a quarter vehicle system. The control law for semi-active suspensions modeled in this study is developed using passive and ANFlS control method. Computer simulation results show that the semi-active suspension with ERF damper has good performances of ride quality

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집B
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• pp.89-95
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• 2001

On-site diagnosis of chiller performance is an essential step for energy saving business. The main purpose of the on-site diagnosis is to predict the COP of a target chiller. Many models based on thermodynamics background have been proposed for the purpose. However, they have to be modified from chiller to chiller and require deep insight into thermodynamics that most of field engineers are often lacking in. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). Quality of the training data for ANFIS, sampled over June through September, is assessed by checking COP prediction errors. The architecture of the ANFIS, its error bounds, and collection of training data are described in detail.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• 상하수도학회지
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• 제19권6호
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• pp.738-748
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• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2011년도 한국우주과학회보 제20권1호
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• pp.32.2-32.2
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• 2011

State dependent Riccati equation (SDRE) control technique has been widely used in the control society. Although it solves nonlinear optimal control problems, which minimizes state error and control efforts simultaneously, it has drawbacks when it is to be applied to the real time systems in that it requires much computational efforts. So the real time system whose computational ability is limited (for example, satellites) cannot afford to use SDRE controller. To solve this problem, a hybrid controller which is based on MSDRE (Modified SDRE) and ANFIS (Adaptive Neuro-Fuzzy Inference System) has been proposed by Abdelrahman et al. (2010). We propose a hybrid controller based on SDRE and ANFIS, and apply the hybrid controller to the hardware attitude simulator to perform a HIL (Hardware-In-the-Loop) simulation. Through HIL simulation, it is demonstrated that the hybrid controller satisfies the control requirement and the computation load is reduced significantly. In addition, the effects of statistical properties of the ANFIS training data to the performance of the ANFIS controller have been analyzed.