• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.43-51
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• 1996

Fuzzy rules in fuzzy logic control play a major role in deciding the control dynamics of a fuzzy logic controller. Thus, control performance is mainly determined by the quality of fuzzy rules. This paper introduces an optimization method for fuzzy rules using GAS with adaptive probabilies of crossover and mutation. Also we design two fitness measures to satisfy control objectives by partitioning the response of a plant into two parts. An initial population is generated by an automatic fuzzy rule generation method instead of random selection for fast a.pproaching to the final solution. We employed a nonlinear plant to simulate our method. It is shown through simulation that our method is reasonable and can be useful for optimizing fuzzy rules.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.29-37
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• 2017

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

• Proceedings of the KSRS Conference
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• v.2
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• pp.717-720
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• 2006

In this study, we predicted locations vulnerable to ground subsidence hazard using fuzzy logic and geographic information system (GIS). Test was carried out at an abandoned underground coal mine in Samcheok City, Korea. Estimation of relative ratings of eight major factors influencing subsidence and determination of effective fuzzy operators are presented. Eight major factors causing ground subsidence were extracted and constructed as a spatial database using the spatial analysis and the probability analysis functions. The eight factors include geology, slope, landuse, depth of mined tunnel, distance from mined tunnel, RMR, permeability, and depth of ground water. A frequency ratio model was applied to calculate relative rating of each factor, and the ratings were integrated using fuzzy membership function and five different fuzzy operators to produce a ground subsidence susceptibility map. The ground subsidence susceptibility map was verified by comparing it with the existing ground subsidences. The obtained susceptibility map well agreed with the actual ground subsidence areas. Especially, ${\gamma}-operator$ and algebraic product operator were the most effective among the tested fuzzy operators.

• Journal of the Korean Society for Library and Information Science
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• v.21
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• pp.249-290
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• 1991

Theoretical developments in the information retrieval have offered a number of alternatives to traditional Boolean retrieval. Probability theory and fuzzy set theory have played prominent roles here. Fuzzy set theory is an attempt to generalize traditional set theory by permitting partial membership in a set and this means recognizing different degrees to which a document can match a request. In this study, an experimentation of a document retrieval system using the fuzzy relation matrix of the keywords is described and the results are offered. The queries composed of keywords and Boolean operaters AND, OR, NOT were processed in the retrieval method, and the method was implemented on the PC of 32bit level (30 MHz) in an experimental system. The measurement of the recall ratio and precision ratio verified the effectiveness of the proposed fuzzy relation matrix of keywords and retrieval method. Compared to traditional crisp method in the same document database, the recall ratio increased $10\%$ high although the precision ratio decreased slightly. The problems, in this experiment, to be resolved are first, the design of the automatic data input and fuzzy indexing modules, through which the system . can have the ability of competition and usefulness. Second, devising a systematic procedure for assigning fuzzy weights to keywords in documents and in queries.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.653-658
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• 2006

Trust can be defined as the level of subjective probability with which an agent will perform a particular action. This paper proposes a comprehensive fuzzy trust model which incorporates dispositional trust, general trust, and situational trust and reputation information. In the model, the preference degrees for the interaction outcomes with respect to the evaluation criteria are expressed in a fuzzy set, and Sugeno's fuzzy integral is employed to aggregate the satisfaction degrees with respect to the importance of evaluation criteria which can be assigned in a way to preserve the properties of the ${\lambda}-fuzzy$ measure.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.221-235
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• 2015

Risk assessment is an important phase of risk management. It is the stage in which risk is measured thoroughly to achieve effective management. Some factors such as probability and impact of risk have been used in the literature related to construction projects. Because in high-rise projects safety issues are paramount, this study has tried to develop a quantifying technique that takes into account three factors: probability, impact and Safety Performance Index (SPI) where the SPI is defined as the capability of an appropriate response to reduce or limit the effect of an event after its occurrence with regard to safety pertaining to a project. Regarding risk-related literatures which cover an uncertain subject, the proposed method developed in this research is based on a fuzzy logic approach. This approach entails a questionnaire in which the subjectivity and vagueness of responses is dealt with by using triangular fuzzy numbers instead of linguistic terms. This method returns a Risk Critical Point (RCP) on a zoning chart that places risks under categories: critical, critical-probability, critical-impact, and non-critical. The high-rise project in the execution phase has been taken as a case study to confirm the applicability of the proposed method. The monitoring results showed that the RCP method has the inherent ability to be extended to subsequent applications in the phases of risk response and control.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.624-629
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• 2016

Recently various types of disaster monitoring system using smart-phones are under active studying. In this paper, we propose a system that automatically performs the disaster and fire detection. Additionally we implement the Arduino-based smart image sensor system in the web platform. When a fire is detected, an SMS is sent to the Fire and Disaster Management Agency. In order to improve fire detection probability, we proposed a smart Arduino fire detection sensor simulation which searches the smart sensor inference algorithm using fuzzy rules.

• Journal of information and communication convergence engineering
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• v.14 no.1
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• pp.1-8
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• 2016

Spectrum sensing in cognitive radio networks allows secondary users to sense the unused spectrum without causing interference to primary users. Cognitive radio requires more accurate sensing results from unused portions of the spectrum. Accurate spectrum sensing techniques can reduce the probability of false alarms and misdetection. In this paper, a two-stage spectrum sensing scheme is proposed for cooperative spectrum sensing in cognitive radio networks. In the first stage, spectrum sensing is executed for each secondary user using energy detection based on double adaptive thresholds to determine the spectrum condition. If the energy value lies between two thresholds, a fuzzy logic scheme is applied to determine the channel conditions more accurately. In the second stage, a fusion center combines the results of each secondary user and uses a fuzzy logic scheme for combining all decisions. The simulation results show that the proposed scheme provides increased sensing accuracy by about 20% in some cases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.756-761
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• 1998

This paper describes an in-line lossless data compression method using adaptive binary arithmetic coding. To achieve better compression efficiency , we employ an adaptive fuzzy -tuning modeler, which uses fuzzy inference to deal with the problem of conditional probability estimation. The design is simple, fast and suitable for VLSI implementation because we adopt the table -look-up approach. As compared with the out-comes of other lossless coding schemes, our results are good and satisfactory for various types of source data.