• Computers and Concrete
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• v.21 no.1
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• pp.103-110
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• 2018

This study introduces the supervised committee fuzzy model as a hybrid fuzzy model to predict compressive strength (CS) of geopolymers prepared from alumina-silica products. For this purpose, more than 50 experimental data that evaluated the effect of $Al_2O_3/SiO_2$, $Na_2O/Al_2O_3$, $Na_2O/H_2O$ and Na/[Na+K] on (CS) of geopolymers were collected from the literature. Then, three different Fuzzy Logic (FL) models (Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL)) were adopted to overcome the inherent uncertainty of geochemical parameters and to predict CS. After validating the model, it was found that the SFL model is superior to MFL and LFL models, but each of the FL models has advantages to predict CS. Therefore, to achieve the optimal performance, the supervised committee fuzzy logic (SCFL) model was developed as a hybrid method to combine the benefits of individual FL models. The SCFL employs an artificial neural network (ANN) model to re-predict the CS of three FL model predictions. The results also show significant fitting improvement in comparison with individual FL models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1205-1223
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• 2018

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.325-328
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• 2008

본 논문에서는 비선형 모델의 설계를 위해 Type-2 퍼지 논리 집합을 이용하여 불확실성 문제를 다룬다. 퍼지 논리 시스템의 멤버쉽 함수와 규칙의 구조는 불확실성이 존재하는 언어적인 정보 또는 수치적 데이터를 바탕으로 설계된다. 기존의 Type-1 퍼지 논리 시스템은 외부의 노이즈와 같은 불확실성을 효율적으로 취급할 수 없다. 그러나 Type-2 퍼지 논리 시스템은 불확실한 정보까지 멤버쉽 함수로 표현함으로서 불확실성을 효과적으로 다룰 수 있다. 따라서 본 논문에서는 규칙의 전 ${\cdot}$ 후반부가 Type-2 퍼지 집합으로 구성된 Type-2 퍼지 논리 시스템을 설계하고 불확실성의 변화에 대한 비선형 모델의 성능을 비교한다. 여기서 규칙 전반부 멤버쉽 함수의 정점 선택은 C-means 클러스터링 알고리즘을 이용하고, 규칙 후반부 퍼지 집합의 정점 결정에는 입자 군집 최적화(PSO : Particle Swarm Optimization) 알고리즘을 사용한다. 마지막으로, 비선형 모델 평가에 대표적으로 이용되는 가스로 시계열 데이터를 제안된 모델에 적용하고, 입력 데이터에 인위적인 노이즈가 포함되었을 경우 Type-2 퍼지 논리 시스템이 기존의 Type-1 퍼지 논리 시스템보다 우수함을 보인다.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1271-1279
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• 2017

A highly accurate model-free controller is proposed for trajectory tracking control of robot manipulators. The proposed controller incorporates time-delay estimation (TDE) to estimate and cancel continuous nonlinearities of robot dynamics, and exploits fuzzy logic systems to suppress the effect of the TDE error, which is due to discontinuous nonlinearities such as friction. To this end, integral sliding mode is defined using desired error dynamics, and a Mamdani-type fuzzy inference system is constructed. As a result, the proposed controller achieves the desired error dynamics well. Implementation of the proposed controller is easy because the design of the controller is intuitive and straightforward, and calculations of the complex robot dynamics are not required. The tracking performance of the proposed controller is verified experimentally using a 3-degree of freedom PUMA-type robot manipulator.

• Journal of Internet of Things and Convergence
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• v.4 no.1
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• pp.1-5
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• 2018

This paper is concerned with the modeling and identification of time series data corrupted by noise. As modeling techniques, nonsingleton fuzzy logic system (NFLS) is employed for the modeling of corrupted time series. Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data or the input data to the fuzzy logic system are corrupted by noise. Simulation results of the Mackey-Glass time series data will be demonstrated to show the performance of the modeling methods. As a result, NFLS does a much better job of modeling noisy time series data than does a traditional Mamdani FLS.

• Journal of the Korean Institute of Navigation
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• v.14 no.4
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• pp.17-30
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• 1990

Since L.A. Zadeh introduced the theory of fuzzy sets in 1965, E.H. Mamdani applied the theory to the steam engine control in 1974. Since then, scientists have shown a great deal of interests in its application to practical problems and the possibility of the application of the theory a more complicate system has been increasing greatly. In the fuzzy control, the qualitative knowledge and intuition that the operators of a system has acquired through their experience can be logically described by the Linguistic Control Rule(LCR). The algorithm of th control is made of the LCR, and th control of an object is performed by processing this algorithm implementing a computer. in this thesis, the fuzzy controller of the ship's steering system is devided into two systems, namely FC1 and FC2, according to their control function. FC1 is for the course keeping steering, wheress FC2 is for the altering of s ship's course. The characteristics of the control system were investigated through the digital computer simulation and the results were compared with those of the conventional steering system. It was found that the fuzzy control was more efficient than the conventional auto pilot system.

• Fire Science and Engineering
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• v.18 no.4
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• pp.42-51
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• 2004

This paper presents a study on the analysis of fire detection system using fuzzy logic with input variables of temperature and smoke density. The input variables for the fuzzy logic algorithm are measured by fire experiment of small scale with temperature detector and smoke detector. The antecedent part of fuzzy rules consists of temperature and smoke density, and the consequent part consists of fire possibility. Also the triangular fuzzy membership function is chosen for input variables and fuzzy rules to simplify computation. In order to calculate fuzzy values of such fuzzy system, a computer program is developed with Matlab based on graphics user interface. The experiment was conducted with paper and ethanol to simulate flaming fire and with plastic and sawdust to model smoldering fire. The results showed that the fire detection system presented here was able to diagnose fire very precisely. With the help of algorithms using fuzzy logic we could distinguish whether fire or not.

• International Journal of Safety
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• v.11 no.2
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• pp.15-21
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• 2012

This paper is aimed to develop an electrical fire detection system (EFDS) which can analyze the possibility of electrical fire for overcurrent, leakage current and arc signals of panel board in traditional market shop. The EFDS adopted fuzzy logic and precursory data for overcurrent, leakage current and arc signals to evaluate the possibility of electrical fire. The signals are obtained directly from panel board in traditional market shops and fuzzy membership function is obtained from experiment, simulation, expert's advice. The overcurrent data is acquired by thermal data of normal and abnormal states (partial disconnection) on the insulated electrical wire, in accordance with the increase of the current signal, The leakage current data is obtained under various environments. The arc signal is acquisited by waveforms of instantaneous value in time domain and frequency band in frequency domain. The Fuzzy algorithm for DB of EFDS consists of fuzzification, inference engine by Mamdani's method and defuzzification by center of gravity method. In order to verify the performance and reliability of EFDS, it was applied to Jeon-Ju traditional market shops (90 shops) in Korea. Results show that EFDS in this paper is useful in alarming the fire case, which will prevent severe damage to human beings and properties, and reduce the electrical fires in a vulnerable area of electrical disaster.

• Safety and Health at Work
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• v.11 no.3
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• pp.322-334
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• 2020

Background: Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage. Method: Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O₂, N₂, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB. Results: The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure. Conclusion: The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.5
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• pp.563-569
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• 2015

A chemical reaction occurring in CSTR (Continuous Stirred Tank Reactor) is significantly affected by the concentration, temperature, pressure, and reacting time of materials, and thus it has strong nonlinear and time-varying characteristics. Also, when an existing linear PID controller with fixed gain is used, the performance could deteriorate or could be unstable if the system parameters change due to the change in the operating point of CSTR. In this study, a technique for the design of a fuzzy PD plus I controller was proposed for the temperature control of a CSTR process. In the fuzzy PD plus I controller, a linear integral controller was added to a fuzzy PD controller in parallel, and the steady-state performance could be improved based on this. For the fuzzy membership function, a Gaussian type was used; for the fuzzy inference, the Max-Min method of Mamdani was used; and for the defuzzification, the center of gravity method was used. In addition, the saturation state of the actuator was also considered during controller design. The validity of the proposed method was examined by comparing the set-point tracking performance and the robustness to the parameter change with those of an adaptive controller and a nonlinear proportional-integral-differential controller.