• Title/Summary/Keyword: Fuzzy Fusion

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Recognition of contact surfaces using optical tactile and F/T sensors integrated by fuzzy fusion algorithm (광촉각 센서와 힘/역학센서의 퍼지융합을 통한 접촉면의 인식)

  • 고동환;한헌수
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.628-631
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    • 1996
  • This paper proposes a surface recognition algorithm which determines the types of contact surfaces by fusing the information collected by the multisensor system, consisted of the optical tactile and force/torque sensors. Since the image shape measured by the optical tactile sensor system, which is used for determining the surface type, varies depending on the forces provided at the measuring moment, the force information measured by the f/t sensor takes an important role. In this paper, an image contour is represented by the long and short axes and they are fuzzified individually by the membership function formulated by observing the variation of the lengths of the long and short axes depending on the provided force. The fuzzified values of the long and short axes are fused using the average Minkowski's distance. Compared to the case where only the contour information is used, the proposed algorithm has shown about 14% of enhancement in the recognition ratio. Especially, when imposing the optimal force determined by the experiments, the recognition ratio has been measured over 91%.

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A sensor fusion method on local homing robot navigation using omnidirectional sensor-based model and fuzzy arithmetic (전방향 센서 기반의 모델과 퍼지 연산을 이용한 국부 유도 로봇 항법용 센서 융합 방법)

  • Bang, Seok-Won;Jeong, Myeong-Jin
    • Journal of Institute of Control, Robotics and Systems
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    • v.1 no.1
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    • pp.43-49
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    • 1995
  • 본 논문에서는 초음파 센서와 시각 센서에서 얻은 전방향 센서 데이타를 이용하여 실내 이동 로봇용 국부 유도 항법을 위한 새로운 환경 모델링 방법을 제안한다. 그리고 이 두 종류의 센서 데이타에 포함된 불확실성을 주관적 지식과 퍼지 연산법을 사용하여 정량적으로 다룰 수 있는 센서 융합법을 제안한다. 이 방법을 사용하여, 로봇의 현재 위치와 목표 위치간의 기하학적 관계를 더욱 정확하게 얻을 수 있다. 실험 결과를 통하여 제안된 모델링과 센서 융합법이 실내 이동 로봇 항법에 효과적임을 보였다.

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A hybrid navigation system of underwater vehicles using fuzzy inferrence algorithm (퍼지추론을 이용한 무인잠수정의 하이브리드 항법 시스템)

  • 이판묵;이종무;정성욱
    • Journal of Ocean Engineering and Technology
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    • v.11 no.3
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    • pp.170-179
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    • 1997
  • This paper presents a hybrid navigation system for AUV to locate its position precisely in rough sea. The tracking system is composed of various sensors such as an inclinometer, a tri-axis magnetometer, a flow meter, and a super short baseline(SSBL) acoustic position tracking system. Due to the inaccuracy of the attitude sensors, the heading sensor and the flowmeter, the predicted position slowly drifts and the estimation error of position becomes larger. On the other hand, the measured position is liable to change abruptly due to the corrupted data of the SSBL system in the case of low signal to noise ratio or large ship motions. By introducing a sensor fusion technique with the position data of the SSBL system and those of the attitude heading flowmeter reference system (AHFRS), the hybrid navigation system updates the three-dimensional position robustly. A Kalman filter algorithm is derived on the basis of the error models for the flowmeter dynamics with the use of the external measurement from the SSBL. A failure detection algorithm decides the confidence degree of external measurement signals by using a fuzzy inference. Simulation is included to demonstrate the validity of the hybrid navigation system.

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On-Line Travel Time Estimation Methods using Hybrid Neuro Fuzzy System for Arterial Road (검지자료합성을 통한 도시간선도로 실시간 통행시간 추정모형)

  • 김영찬;김태용
    • Journal of Korean Society of Transportation
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    • v.19 no.6
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    • pp.171-182
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    • 2001
  • Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.

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Multi-Modal Biometrics Recognition Method of Face Recognition using Fuzzy-EBGM and Iris Recognition using Fuzzy LDA (Fuzzy-EBGM을 이용한 얼굴인식과 Fuzzy-LDA를 이용한 홍채인식의 다중생체인식 기법 연구)

  • Go Hyoun-Joo;Kwon Mann-Jun;Chun Myung-Ceun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2005.11a
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    • pp.299-301
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    • 2005
  • 본 연구는 생체정보를 이용하여 개인을 인증하고 확인하기 위한 방법으로 기존 단일 생체인식 기법의 단점을 보완하기 위해 홍채와 얼굴을 이용한 다중생체인식(Multi-Modal Biometrics Recognition)기법을 연구하였다. 중국 홍채 데이터베이스 CASIA(Chinese Academy of Science)에 Gabor Wavelet과 FLDA(Fuzzy Linear Discriminant Analysis)를 사용하여 특징벡터를 획득하였으며, FERET(FERET(Face Recognition Technology) 얼굴영상데이터를 사용하여 FERET 연구에서 매우 우수한 성능을 보인 EBGM알고리듬으로 특징벡터를 획득하였다. 이로부터 얻어진 두 score 값에 대하여 다양한 균등화 과정을 시도해 보았으며, 등록자와 침입자를 구분하기 위한 Fusion Algorithm으로 Bayesian Classifier, Support vector machine, Fisher's linear discriminant를 사용하였다. 또한, 널리 사용되는 방법 중 Weighted Summation을 이용하여 다중생체인식의 성능을 비교해 보았다.

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Intelligent Control of Nonlinear dynamic system Using Immune Fuzzy Fusion

  • Kim, Dong-Hwa
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.1
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    • pp.70-78
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    • 2004
  • This paper proposes non-linear control method using immune algorithm based fuzzy logic. Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because gain of the PID controller has to be manually tuned by trial and error. An inverted pendulum control problem is selected to illustrate the efficiency of the proposed method and defines relationship state variables $\chi$, $\chi$, $\theta$, $\theta$ using immune fuzzy.

Design of Self-Organizing Networks with Competitive Fuzzy Polynomial Neuron (경쟁적 퍼지 다항식 뉴론을 가진 자기 구성 네트워크의 설계)

  • Park, Ho-Sung;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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    • 2000.11d
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    • pp.800-802
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    • 2000
  • In this paper, we propose the Self-Organizing Networks(SON) based on competitive Fuzzy Polynomial Neuron(FPN) for the optimal design of nonlinear process system. The SON architectures consist of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as FPN which includes either the simplified or regression Polynomial fuzzy inference rules. The proposed SON is a network resulting from the fusion of the Polynomial Neural Networks(PNN) and a fuzzy inference system. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as liner, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. Chaotic time series data used to evaluate the performance of our proposed model.

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A Design of GA-based Fuzzy Controller and Truck Backer-Upper Control (GA 기반 퍼지 제어기의 설계 및 트럭 후진제어)

  • Kwak, Keun-Chang;Kim, Ju-Sik;Jeong, Su-Hyun
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.51 no.2
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    • pp.99-104
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    • 2002
  • In this paper, we construct a hybrid intelligent controller based on a fusion scheme of GA(Genetic Algorithm) and FCM(Fuzzy C-Means) clustering-based ANFIS(Adaptive Neuro-Fuzzy Inference System). In the structure identification, a set of fuzzy rules are generated for a given criterion by FCM clustering algorithm. In the parameter identification, premise parameters are optimally searched by adaptive GA. On the other hand, consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. Finally, we applied the proposed method to the truck backer-upper control and obtained a better performance than previous works.

A Study on the Adaptive Polynomial Neuro-Fuzzy Networks Architecture (적응 다항식 뉴로-퍼지 네트워크 구조에 관한 연구)

  • Oh, Sung-Kwun;Kim, Dong-Won
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.50 no.9
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    • pp.430-438
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    • 2001
  • In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

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Reflectance estimation for infrared and visible image fusion

  • Gu, Yan;Yang, Feng;Zhao, Weijun;Guo, Yiliang;Min, Chaobo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.8
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    • pp.2749-2763
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    • 2021
  • The desirable result of infrared (IR) and visible (VIS) image fusion should have textural details from VIS images and salient targets from IR images. However, detail information in the dark regions of VIS image has low contrast and blurry edges, resulting in performance degradation in image fusion. To resolve the troubles of fuzzy details in dark regions of VIS image fusion, we have proposed a method of reflectance estimation for IR and VIS image fusion. In order to maintain and enhance details in these dark regions, dark region approximation (DRA) is proposed to optimize the Retinex model. With the improved Retinex model based on DRA, quasi-Newton method is adopted to estimate the reflectance of a VIS image. The final fusion outcome is obtained by fusing the DRA-based reflectance of VIS image with IR image. Our method could simultaneously retain the low visibility details in VIS images and the high contrast targets in IR images. Experiment statistic shows that compared to some advanced approaches, the proposed method has superiority on detail preservation and visual quality.