• Computers and Concrete
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• 제32권1호
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• pp.27-44
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• 2023

The current study targets to apply the adaptive neuro-fuzzy inference system (ANFIS) for the estimation of the shear resistance offered by the externally bonded fiber-reinforced polymer (EB-FRP) U-jackets. A total of 202 groups of data cumulated from previous investigations, were employed for the development and evaluation of the ANFIS model. A relative appraisal between the ANFIS predictions and the results of experiments has shown that the assessments by current ANFIS model are in good concurrence with the latter. In addition, assessment of the accuracy of the ANFIS model was done by relating the ANFIS predictions with the forecasts of eight extensively used design guidelines. Based on the examination of various performance measures, it has been derived that the adequacy of the ANFIS model is better than the available guidelines. A parametric investigation has additionally been done to reconnoiter the influence of individual parameters as well as their combined effects on the shear contribution of EB-FRP. Based on the observations made from the parametric study, it has been witnessed that the ANFIS model has incorporated the effect of different parameters more competently than the considered design guidelines.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1251-1253
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• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neuro-fuzzy control(NFC) and estimation of speed using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 한국정보통신학회논문지
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• 제19권3호
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• pp.558-566
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• 2015

차세대 조명광원인 LED가 급격하게 발전하고 저 전력, 고효율, 장수명의 장점을 가지고 있어 LED를 이용한 조명에 관심이 높아지고 있다. LED를 이용하여 감성조명을 구현하게 되면 기존의 단색의 조명과는 다르게 빛의 3원색을 이용한 모든 색상을 구현할 수 있다. 이러한 장점으로 인간의 감정까지 다스릴 수 있는 LED 감성조명이 지속적으로 개발되고 있다. 본 논문에서는 실내 환경에서의 집중 및 휴식상황에 맞는 색상을 추출하고 사용자가 느끼는 온도의 색상과 배합하여 상황과 온도에 대한 LED 감성조명을 표현하기 위해 알고리즘을 설계한다. 그리하여 뉴로-퍼지시스템을 이용하여 설계된 LED 감성조명은 사용자에게 집중 및 휴식에 대한 감성에 효과적인 영향을 나타낼 수 있을 것이다.

• 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.

• Advances in nano research
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• 제12권5호
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.115-120
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• 2006

This article propose a new idea of developing a hybrid scheme to achieve faster INS alignment with higher accuracy using a novel procedure to estimate the initial attitude angles that combines a Kalman filter and Adaptive Neuro-Fuzzy Inference System architecture. A tactical grade inertial measurement unit was applied to verify the performance of proposed scheme in this study. The preliminary results indicated the outstanding improvements in both time consumption for fine alignment process and accuracy of estimated attitude angles, especially in heading angles. In general, the improvement in terms of time consumption and the accuracy of estimated attitude estimated accuracy reached 80% and 70% respectively during alignment process after compensating the attitude angles estimated by an extended Kalman filter with 15 states using proposed approach. It is worth mentioned that the proposed approach can be implemented in general real time navigation applications.

• 제어로봇시스템학회논문지
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• 제8권7호
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1314-1317
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• 1999

In this paper, we design an Adaptive Neuro-Fuzzy Inference System(ANFIS) Precompensator for the performance improvement of conventional proportional integral derivative (PID) controller that the governor system of power plant constantly maintains the load frequency of two-area power system. The ANFIS Precompensator is expressed as the membership functions of premise parameters and the linear combination of consequent parameters by Sugeno's fuzzy if-then rules using nonlinear input-output relation for the set point automatic modification maintaining conventional PID controller. The proposed compensation design technique is hoped to be satisfactory method overcome difficulty of exact modelling and arising problems by the complex nonlinearities of power system, and our design shows merit that is easily implemented by adding an ANFIS precompenastor to an existing PID controller without replacement.

• 콘크리트학회논문집
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• 제14권6호
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• pp.1022-1031
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• 2002

본 연구에서는 분규ㆍ회귀목-적응 뉴고 퍼지추론 시스템을 사용하여 교량 구조물에 대한 유용한 모델을 제시하였다. 퍼지결정목은 데이터집합의 입력영역이 서로 다른 영역으로 분류되고 하나의 부호나 값으로 나타내지며 데이터 정점에서 특정화시키기 위한 활동영역으로 할당되기도 한다. 분류문제로 사용되는 결정목은 가끔 퍼지결정목이라고 불려지는데, 각 최종점은 주어진 특정백터의 예측등급을 나타낸다. 회귀문제에 사용되는 결정목을 가끔 퍼지회귀목이라고 하는데, 이 때 최종점 영역은 주어진 입력백터의 예측 출력 값을 상수나 방정식으로 나타낼 수 있다. 분류ㆍ회귀목은 관련된 입력값을 선택하여 입력구역에서 분류 할 수 있는 반면에 적응 뉴로 퍼지추론 시스템은 회귀문제를 수정하고 이틀의 회귀문제를 보다 연속적이면서 간략하게 만들 수 있음을 주목해야 한다. 따라서 분류ㆍ회귀목과 적응 뉴로 퍼지추론 시스템은 서로 상보적인 것이며, 이들의 조합은 퍼지모델링을 위해 실직적인 근사식으로 구성된다.

• Smart Structures and Systems
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• 제5권2호
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• pp.139-152
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• 2009

Globally, civil infrastructures are deteriorating at an alarming rate caused by overuse, overloading, aging, damage or failure due to natural or man-made hazards. With such a vast network of deteriorating infrastructure, there is a growing interest in continuous monitoring technologies. In order to provide a true distributed sensor and control system for civil structures, we are developing a Structural Nervous System that mimics key attributes of a human nervous system. This nervous system is made up of building blocks that are designed based on mechanoreceptors as a fundamentally new approach for the development of a structural health monitoring and diagnostic system that utilizes the recently developed piezo-fibers capable of sensing and actuation. In particular, our research has been focused on producing a sensory nervous system for civil structures by using piezo-fibers as sensory receptors, nerve fibers, neuronal pools, and spinocervical tract to the nodal and central processing units. This paper presents up to date results of our research, including the design and analysis of the structural nervous system.