• Title/Summary/Keyword: Vague Information

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ON THE MODAL OPERATORS OVER THE GENERALIZED INTERVAL VALUED INTUITIONISTIC FUZZY SETS

  • JAMKHANEH, EZZATALLAH BALOUI
    • Journal of applied mathematics & informatics
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    • v.35 no.5_6
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    • pp.459-476
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    • 2017
  • Interval valued intuitionistic fuzzy sets (IVIFSs) is widely used to model uncertainty, imprecise, incomplete and vague information. In this paper, newly defined modal operators over an extensional generalized interval valued intuitionistic fuzzy sets ($GIVIFS_Bs$) are proposed. Some of the basic properties of the new operators are discussed and few theorems were proved. The actual contribution in this paper is to discuss ten operators on $GIVIFS_Bs$.

An Aggregate Detection of Event Correlation using Fuzzy Control (퍼지제어를 이용한 관련성 통합탐지)

  • 김용민
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.13 no.3
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    • pp.135-144
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    • 2003
  • An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

A Note on Computing the Crisp Order Context of a Fuzzy Formal Context for Knowledge Reduction

  • Singh, Prem Kumar;Kumar, Ch. Aswani
    • Journal of Information Processing Systems
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    • v.11 no.2
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    • pp.184-204
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    • 2015
  • Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

Measures to use the Land Price Information System in Connection with the PDA Technology for Investigation of Land Characteristics (토지특성 조사를 위한 지가정보시스템과 PDA의 연계 활용 기법)

  • Lee Kye-Dong;Jeong Tea-Su;Hahm Chang-Hahk;Lee Jea-Kee
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2006.04a
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    • pp.521-526
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    • 2006
  • As a result of establishing the regional self-government system, regional residents came to take more interest in their land ownership rights and relevant areas. The administrative bodies in South Korea are trying their best to meet the complicated and diversified demands of the residents regarding land and construction administration. However, governmental agencies are having difficulties identifying land characteristics on the field as part of standard lands change every year. Also, the vague boundaries of agricultural and mountainous land areas are causing surveyors problems in spotting the right areas. Thus, this study intends to provide information and guidance on an accurate land price calculation system in connection between the land information system and PDA technology for distribution of accurate information regarding lands and their management.

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A Study on Bayesian Reliability Evaluation of IPM using Simple Information (단순 수명정보를 이용한 IPM의 베이지안 신뢰도 평가 연구)

  • Jo, Dong Cheol;Koo, Jeong Seo
    • Journal of the Korean Society of Safety
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    • v.36 no.2
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    • pp.32-38
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    • 2021
  • This paper suggests an approach to evaluate the reliability of an intelligent power module with information deficiency of prior distribution and the characteristics of censored data through Bayesian statistics. This approach used a prior distribution of Bayesian statistics using the lifetime information provided by the manufacturer and compared and evaluated diffuse prior (vague prior) distributions. To overcome the computational complexity of Bayesian posterior distribution, it was computed with Gibbs sampling in the Monte Carlo simulation method. As a result, the standard deviation of the prior distribution developed using simple information was smaller than that of the posterior distribution calculated with the diffuse prior. In addition, it showed excellent error characteristics on RMSE compared with the Kaplan-Meier method.

A Fuzzy Logic System for Detection and Recognition of Human in the Automatic Surveillance System (유전자 알고리즘과 퍼지규칙을 기반으로한 지능형 자동감시 시스템의 개발)

  • 장석윤;박민식;이영주;박민용
    • Proceedings of the IEEK Conference
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    • 2001.06c
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    • pp.237-240
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    • 2001
  • An image processing and decision making method for the Automatic Surveillance System is proposed. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas if the segmented region are used in this paper. These features are not the unique and decisive characteristics for representing human Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from the information, we use a fuzzy rules base system ai an approximate reasoning method. The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic approach and then, trained and tasted with input/output data Experimental result are shown, demonstrating the validity of our system.

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Effective Construction Method of Defect Size Distribution Using AOI Data: Application for Semiconductor and LCD Manufacturing (AOI 데이터를 이용한 효과적인 Defect Size Distribution 구축방법: 반도체와 LCD생산 응용)

  • Ha, Chung-Hun
    • IE interfaces
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    • v.21 no.2
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    • pp.151-160
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    • 2008
  • Defect size distribution is a probability density function for the defects that occur on wafers or glasses during semiconductor/LCD fabrication. It is one of the most important information to estimate manufacturing yield using well-known statistical estimation methods. The defects are detected by automatic optical inspection (AOI) facilities. However, the data that is provided from AOI is not accurate due to resolution of AOI and its defect detection mechanism. It causes distortion of defect size distribution and results in wrong estimation of the manufacturing yield. In this paper, I suggest a size conversion method and a maximum likelihood estimator to overcome the vague defect size information of AOI. The methods are verified by the Monte Carlo simulation that is constructed as similar as real situation.

Fuzzy Logic Based Neural Network Models for Load Balancing in Wireless Networks

  • Wang, Yao-Tien;Hung, Kuo-Ming
    • Journal of Communications and Networks
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    • v.10 no.1
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    • pp.38-43
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    • 2008
  • In this paper, adaptive channel borrowing approach fuzzy neural networks for load balancing (ACB-FNN) is presented to maximized the number of served calls and the depending on asymmetries traffic load problem. In a wireless network, the call's arrival rate, the call duration and the communication overhead between the base station and the mobile switch center are vague and uncertain. A new load balancing algorithm with cell involved negotiation is also presented in this paper. The ACB-FNN exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding better performance compared with other algorithms. It aims to efficiently satisfy their diverse quality-of-service (QoS) requirements. The results show that our algorithm has lower blocking rate, lower dropping rate, less update overhead, and shorter channel acquisition delay than previous methods.

A Study on Fuzzy-Rough sets (퍼지-Rough 집합에 관한 연구)

  • 정구범;김명순
    • Journal of the Korea Society of Computer and Information
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    • v.1 no.1
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    • pp.183-188
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    • 1996
  • Fuzzy sets Introduced by Zadeh is a concept which can process, and reson a vague Information using membership functions. The notion of rough sets introduced by Pawlak is based on the ability to classify. reduce. and perform approximation reasoning for the Indiscernible data.A comparison between fuzzy sets and rough sets has been given In Pawlak where it is shown that these concepts are different and can't combine each other. The purpose of this paper Is to Introduce and define the notion of fuzzy-rough sets which joins the membership function of fuzzy sets to the rough sets.

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A Hybrid Approach Using Case-based Reasoning and Fuzzy Logic for Corporate Bond Rating

  • Kim, Hyun-jung;Shin, Kyung-shik
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2003.05a
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    • pp.474-483
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    • 2003
  • A number of studies for corporate bond rating classification problems have demonstrated that artificial intelligence approaches such as Case-based reasoning (CBR) can be alternative methodologies to statistical techniques. CBR is a problem solving technique in that the case specific knowledge of past experience is utilized to find a most similar solution to the new problems. To build a successful CBR system to deal with human information processing, the representation of knowledge of each attribute is an important key factor We propose a hybrid approach of using fuzzy sets that describe the approximate phenomena of the real world because it handles inexact knowledge represented by common linguistic terms in a similar way as human reasoning compared to the other existing techniques. Integration of fuzzy sets with CBR is important to develop effective methods for dealing with vague and incomplete knowledge to statistical represent using membership value of fuzzy sets in CBR.

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