• Title/Summary/Keyword: fuzzy set model

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Cluster-based Information Retrieval with Tolerance Rough Set Model

  • Ho, Tu-Bao;Kawasaki, Saori;Nguyen, Ngoc-Binh
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.1
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    • pp.26-32
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    • 2002
  • The objectives of this paper are twofold. First is to introduce a model for representing documents with semantics relatedness using rough sets but with tolerance relations instead of equivalence relations (TRSM). Second is to introduce two document hierarchical and nonhierarchical clustering algorithms based on this model and TRSM cluster-based information retrieval using these two algorithms. The experimental results show that TRSM offers an alterative approach to text clustering and information retrieval.

Multi-vehicle Route Selection Based on an Ant System

  • Kim, Dong-Hun
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.8 no.1
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    • pp.61-67
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    • 2008
  • This paper introduces the multi-vehicle routing problem(MRP) which is different from the traveling sales problem(TSP), and presents the ant system(AS) applied to the MRP. The proposed MRP is a distributive model of TSP since many vehicles are used, not just one salesman in TSP and even some constraints exist. In the AS, a set of cooperating agents called vehicles cooperate to find good solutions to the MRP. To make the proposed MRP extended more, Tokyo city model(TCM) is proposed. The goal in TCM is to find a set of routes that minimizes the total traveling time such that each vehicle can reach its destination as soon as possible. The results show that the AS can effectively find a set of routes minimizing the total traveling time even though the TCM has some constraints.

Design of Hybrid Network Probe Intrusion Detector using FCM

  • Kim, Chang-Su;Lee, Se-Yul
    • Journal of information and communication convergence engineering
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    • v.7 no.1
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    • pp.7-12
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    • 2009
  • The advanced computer network and Internet technology enables connectivity of computers through an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We proposed a network based hybrid Probe Intrusion Detection model using Fuzzy cognitive maps (PIDuF) that detects intrusion by DoS (DDoS and PDoS) attack detection using packet analysis. A DoS attack typically appears as a probe and SYN flooding attack. SYN flooding using FCM model captures and analyzes packet information to detect SYN flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. For the performance evaluation, the "IDS Evaluation Data Set" created by MIT was used. From the simulation we obtained the max-average true positive rate of 97.064% and the max-average false negative rate of 2.936%. The true positive error rate of the PIDuF is similar to that of Bernhard's true positive error rate.

A Fuzzy-based Risk Assessment using Uncertainty Model (불확실성 모델을 사용한 퍼지 위험도분석)

  • Choi Hyun-Ho;Seo Jong-Won;Jung Pyung-Ki
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • autumn
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    • pp.473-476
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    • 2003
  • This paper presents a systematic risk assessment procedure with uncertainty modeling for general construction projects. Since the approach is able to effectively deal with all the related construction risks in terms of the assumed probability with conditional probability concept that systematically incorporate expert's experiences and subjective judgement, the proposed methods with uncertainty modeling is able to apply to all the construction projects inherent in lots of uncertain risk events. The fuzzy set theory is adopted to enhance risk assessment to effectively handle the vague and dynamic phenomenon of an event Therefore, the fuzzy-based risk assessment is very useful, for those countries, such as Korea, where objective probabilistic data for risk assessment is extremely rare, and thus the utilization of subjective judgmental data based on expert's experiences is inevitable.

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Data Pattern Estimation with Movement of the Center of Gravity

  • Ahn Tae-Chon;Jang Kyung-Won;Shin Dong-Du;Kang Hak-Soo;Yoon Yang-Woong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.3
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    • pp.210-216
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    • 2006
  • In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

An Adaptive Fuzzy Backstepping Approach to Robust Tracking Control of a Single-Link Flexible Joint Robot (적응형 퍼지 백스테핑 방식을 이용한 단일축 유연관절 로봇의 강인 제어)

  • 김은태;이희진
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.41 no.4
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    • pp.1-12
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    • 2004
  • This paper presents an adaptive fuzzy backstepping (AFB) controller for a single-link flexible joint robot in the Presence of Parametric uncertainties and external disturbances. Adaptive fuzzy logic systems are used as universal approximators to counteract the model uncertainties coming from robot dynamics and to compensate for the nonlinearities coming from adaptive backstepping method. The approach suggested herein does not require neither an additional supervisory nor a robustifying controller and guarantees that tracking error is uniformly ultimately bounded (UUB) within a sufficiently small residual set. Finally, a simulation result is given to demonstrate the robust tracking performance of proposed design method.

A 9-Rule Fuzzy Logic Controller of the Nuclear Steam Generator (핵증기 발생기의 9룰 퍼지논리 제어기)

  • Lee, Jae-Young;No, Hee-Cheon
    • Nuclear Engineering and Technology
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    • v.25 no.3
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    • pp.371-380
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    • 1993
  • A model free controller utilizing a set of linguistic fuzzy logic of the human operator's experience is developed to control the steam generator water level in a pressurized water reactor. Only 9 rules for control action are generated from the inputs of water level error and mass flow error implicitly representing the time variation of the collapsed water level. The bell type membership functions of the premise side and the result side are tuned by the sensitivity study. This compact fuzzy logic controller shows a robust control during transient and no offset error and oscillation during steady state operation. For a multi-ramp power increase from start-up to full power, the proposed controller shows good performance for the entire range.

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Pattern Recognition Method Using Fuzzy Clustering and String Matching (퍼지 클러스터링과 스트링 매칭을 통합한 형상 인식법)

  • 남원우;이상조
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.17 no.11
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    • pp.2711-2722
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    • 1993
  • Most of the current 2-D object recognition systems are model-based. In such systems, the representation of each of a known set of objects are precompiled and stored in a database of models. Later, they are used to recognize the image of an object in each instance. In this thesis, the approach method for the 2-D object recognition is treating an object boundary as a string of structral units and utilizing string matching to analyze the scenes. To reduce string matching time, models are rebuilt by means of fuzzy c-means clustering algorithm. In this experiments, the image of objects were taken at initial position of a robot from the CCD camera, and the models are consturcted by the proposed algorithm. After that the image of an unknown object is taken by the camera at a random position, and then the unknown object is identified by a comparison between the unknown object and models. Finally, the amount of translation and rotation of object from the initial position is computed.

Fuzzy Linguistic Variable Based Approach for Safety Assessment of Human Body in ELF Electromagnetic Field Considering Power System States (계통상태를 고려한 ELF 전자계의 인체안전평가를 위한 퍼지언어변수 접근법)

  • 김상철;김두현;고은영
    • Journal of the Korean Society of Safety
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    • v.12 no.2
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    • pp.70-79
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    • 1997
  • This paper presents a study on the fuzzy linguistic variable based approach for safety assessment of human body in ELF electromagnetic field considering power system states. To cope with the demand in modern industry, the power system becomes larger in scale, higher in voltage. The advent of high voltage system has increased the relative importance of field effects. The analysis of ELF electromagnetic field based on Quasi-Static Method is introduced while the power system is included to model the expected and/or unexpected uncertainty caused by the load fluctuation and parameter changes. In order to analyze the power system, Monte Carlo simulation method and contingency analysis method are adopted in normal state and alert state, respectively. In the safety assessment of human body, the approach based on fuzzy linguistic variable is employed to overcome the shortcomings resulting from a crisp set concept. The suggested scheme is applied to a sample system(modified IEEE 14 bus system) to validate the usefulness.

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Robust Gain Scheduling Based on Fuzzy Logic Control and LMI Methods (퍼지논리제어와 LMI기법을 이용한 강인 게인 스케줄링)

  • Chi, Hyo-Seon;Koo, Kuen-Mo;Lee, Hungu;Tahk, Min-Jea;Hong, Sung-Kyung
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.1
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    • pp.1162-1170
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    • 2001
  • This paper proposes a practical gain-scheduling control law considering robust stability and performance of Linear Parameter Varying(LPV) systems in the presence of nonlinearities and uncertainties. The proposed method introduces LMI-based pole placement synthesis and also associates with a recently developed fuzzy control system based on Takagei-Sugenos fuzzy model. The sufficient conditions for robust controller design of linearized local dynamics and robust stabilization of fuzzy control systems are reduced to a finite set of Linear Matrix inequalities(LMIs) and solved by using co-evolutionary algorithms. The proposed method is applied to the longitudinal acceleration control of high performance aircraft with linear and nonlinear simulations.

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