• Title/Summary/Keyword: Fuzzy Logic Model

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Study of Discharge in Point-Plane Air Interval Using Fuzzy Logic

  • Bourek, Yacine;Mokhnache, Leila;Nait Said, Nacereddine;Kattan, Rafik
    • Journal of Electrical Engineering and Technology
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    • v.4 no.3
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    • pp.410-417
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    • 2009
  • The objective of this paper is to study the discharge phenomenon for a point-plane air interval using an original fuzzy logic system. Firstly, a physical model based on streamer theory with consideration of the space charge fields due to electrons and positive ions is proposed. To test this model we have calculated the breakdown threshold voltage for a point-plane air interval. The same model is used to determine the discharge steps for different configurations as an inference data base. Secondly, using results obtained by the numerical simulation of the previous model, we have introduced the fuzzy logic technique to predict the breakdown threshold voltage of the same configurations used in the numerical model and make estimation on the insulating state of the air interval. From the comparison of obtained results, we can conclude that they are in accordance with the experimental ones obtained for breakdown discharges in different point-plane air gaps collected from the literature. The proposed study using fuzzy logic technique shows a good performance in the analysis of different discharge steps of the air interval.

Vehicle Traction Control System using Fuzzy Logic Theory (퍼지논리를 이용한 차량 구동력 제어 시스템)

  • 서영덕;여문수;이승종
    • Transactions of the Korean Society of Automotive Engineers
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    • v.6 no.5
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    • pp.138-145
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    • 1998
  • Recently, TCS(Traction Control System) is attracting attention, because it maintains traction ability and steerability of vehicles on low-$\mu$ surface roads by controlling the slip rate between tire and road surface. The development of TCS control law is difficult due to the highly nonlinearity and uncertainty involved in TCS. A fuzzy logic approach is appealing for TCS. In this paper, fuzzy logic controller for TCS is introduced and evaluated by the computer simulation with 8 DOF vehicle model. The result indicate that the fuzzy logic TCS improves vehicle's stability and steerability.

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Fuzzy logic approach for estimating bond behavior of lightweight concrete

  • Arslan, Mehmet E.;Durmus, Ahmet
    • Computers and Concrete
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    • v.14 no.3
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    • pp.233-245
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    • 2014
  • In this paper, a rule based Mamdani type fuzzy logic model for prediction of slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes were discussed. In the model steel rebar diameters and development lengths were used as inputs. The FL model and experimental results, the coefficient of determination R2, the Root Mean Square Error were used as evaluation criteria for comparison. It was concluded that FL was practical method for predicting slippage at maximum tensile strength and slippage at rupture of structural lightweight concretes.

Image Segmentation of Fuzzy Deep Learning using Fuzzy Logic (퍼지 논리를 이용한 퍼지 딥러닝 영상 분할)

  • Jongjin Park
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.23 no.5
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    • pp.71-76
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    • 2023
  • In this paper, we propose a fuzzy U-Net, a fuzzy deep learning model that applies fuzzy logic to improve performance in image segmentation using deep learning. Fuzzy modules using fuzzy logic were combined with U-Net, a deep learning model that showed excellent performance in image segmentation, and various types of fuzzy modules were simulated. The fuzzy module of the proposed deep learning model learns intrinsic and complex rules between feature maps of images and corresponding segmentation results. To this end, the superiority of the proposed method was demonstrated by applying it to dental CBCT data. As a result of the simulation, it can be seen that the performance of the ADD-RELU fuzzy module structure of the model using the addition skip connection in the proposed fuzzy U-Net is 0.7928 for the test dataset and the best.

Fuzzy Logic-based Grid Job Scheduling Model for omputational Grid (계산 그리드를 위한 퍼지로직 기반의 그리드 작업 스케줄링 모델)

  • Park, Yang-Jae;Jang, Sung-Ho;Cho, Kyu-Cheol;Lee, Jong-Sik
    • Journal of the Korea Society of Computer and Information
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    • v.12 no.5
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    • pp.49-56
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    • 2007
  • This paper deals with grid job allocation and grid resource scheduling to provide a stable and quicker job processing service to grid users. In this paper, we proposed a fuzzy logic-based grid job scheduling model for an effective job scheduling in computational grid environment. The fuzzy logic-based grid job scheduling model measures resource efficiency of all grid resources by a fuzzy logic system based on diverse input parameters like CPU speed and network latency and divides resources into several groups by resource efficiency. And, the model allocates jobs to resources of a group with the highest resource efficiency. For performance evaluation, we implemented the fuzzy logic-based grid job scheduling model on the DEVS modeling and simulation environment and measured reduction rates of turnaround time, job loss, and communication messages in comparison with existing job scheduling models such as the random scheduling model and the MCT(Minimum Completion time) model. Experiment results that the proposed model is useful to improve the QoS of the grid job processing service.

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Uncertain Rule-based Fuzzy Technique: Nonsingleton Fuzzy Logic System for Corrupted Time Series Analysis

  • Kim, Dongwon;Park, Gwi-Tae
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.3
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    • pp.361-365
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    • 2004
  • In this paper, we present the modeling of time series data which are corrupted by noise via nonsingleton fuzzy logic system. Nonsingleton fuzzy logic system (NFLS) is useful in cases where the available data are corrupted by noise. NFLS is a fuzzy system whose inputs are modeled as fuzzy number. The abilities of NFLS to approximate arbitrary functions, and to effectively deal with noise and uncertainty, are used to analyze corrupted time series data. In the simulation results, we compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system.

Design of a Fuzzy Re-adhesion Controller for Wheeled Robot (이동 로봇의 퍼지 재점착 제어기 설계)

  • Kwon Sun-Ku;Huh Uk-Youl;Kim Jin-Hwan
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.1
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    • pp.48-55
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    • 2005
  • Mobility of an indoor wheeled robot is affected by adhesion force that is related to various floor conditions. When the adhesion force between driving wheels and floor decreases suddenly, the robot begins slip. In order to overcome this slip problem, optimal slip velocity must be decided for stable movement of wheeled robot. First of all, this paper shows that conventional PI control can not be applied to a wheeled robot of the light weight. Secondly, proposed fuzzy logic is applied to the Takagi-Sugeno model for the configuration of fuzzy sets. For the design of Takagi-Sugeno model and fuzzy rule, proposed algorithm uses FCM(Fuzzy c-mean clustering method) algorithm. In additionally, this algorithm adjusts the driving torque for restraining re-slip. The proposed fuzzy logic controller(FLC) is pretty useful with prevention of the slip phenomena for the controller performance in the re-adhesion control strategy, These procedures are implemented using a Pioneer 2-DXE wheeled robot parameter.

Fuzzy Re-adhesion Control for Wheeled Robot (이동 로봇의 퍼지 재점착 제어)

  • Kwon, Sun-Ku;Huh, Uk-Youl;Kim, Jin-Hwan
    • Proceedings of the KIEE Conference
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    • 2005.05a
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    • pp.30-32
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    • 2005
  • Mobility of an indoor wheeled robot is affected by adhesion force that is related to various floor conditions. When the adhesion force between driving wheels and floor decreases suddenly, the robot begins slip. In order to overcome this slip problem, optimal slip velocity must be decided for stable movement of wheeled robot. First of all, this paper shows that conventional PI control can not be applied to a wheeled robot of the light weight. Secondly, proposed fuzzy logic is applied to the Takagi-Sugeno model for the configuration of fuzzy sets. For the design of Takagi-Sugeno model and fuzzy rule, proposed algorithm uses FCM(Fuzzy c-mean clustering method) algorithm. The proposed fuzzy logic controller(FLC) is pretty useful with prevention of the slip phenomena for the controller performance in the re-adhesion control strategy.

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Development of intelligent model to predict the characteristics of biodiesel operated CI engine with hydrogen injection

  • Karrthik, R.S.;Baskaran, S.;Raghunath, M.
    • Advances in Computational Design
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    • v.4 no.4
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    • pp.367-379
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    • 2019
  • Multiple Inputs and Multiple Outputs (MIMO) Fuzzy logic model is developed to predict the engine performance and emission characteristics of pongamia pinnata biodiesel with hydrogen injection. Engine performance and emission characteristics such as brake thermal efficiency (BTE), brake specific energy consumption (BSEC), hydrocarbon (HC), carbon monoxide (CO), carbon dioxide ($CO_2$) and nitrous oxides ($NO_X$) were considered. Experimental investigations were carried out by using four stroke single cylinder constant speed compression ignition engine with the rated power of 5.2 kW at variable load conditions. The performance and emission characteristics are measured using an Exhaust gas analyzer, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends (Diesel, B10, B20 and B30) and engine load conditions. Fuzzy logic model uses triangular and trapezoidal membership function because of its higher predictive accuracy to predict the engine performance and emission characteristics. Computational results clearly demonstrate that, the proposed fuzzy model has produced fewer deviations and has exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.99136 to 1 with experimental values. The developed fuzzy logic model has produced good correlation between the fuzzy predicted and experimental values. So it is found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

Design of Adaptive Fuzzy Control for High Performance of PMSM Drive (PMSM 드라이브의 고성능 제어를 위한 적응 퍼지제어기의 설계)

  • 정동화;이홍균;이정철
    • The Transactions of the Korean Institute of Electrical Engineers B
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    • v.53 no.2
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    • pp.107-113
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    • 2004
  • This paper develops a adaptive fuzzy controller based fuzzy logic control for high performance of permanent magnet synchronous motor(PMSM) drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for PMSM drive system.