• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.4
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• pp.48-59
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• 1993

Intellignet control of the dyeing machine is a central part to improve the productivity of autonomous dyeing systems. Recently, many number of control methods are introuduced. One of them is fuzzy logic algorithm. Fuzzy logic based controller has many desirable advantages, which are simple to implement on the real time and need not the information of dynamic characteristics of the systems. In this paper we propose a new dyeing machine control simulator using fuzzy logic algorithm as an approach to develop the intellingent auto-dyeing control system. This developing approach of the fuzzy control simulator is based on linguistic control stratege of experts.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.94-104
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• 2004

This paper proposed a new method to evaluate service quality attribute of perceived service quality in public sectors, using triangle fuzzy numbers and hamming distance. Our method measured the ratio of the expected and perceived service for the customers' perceived service quality. By using fuzzy numbers, This method not only overcomes linguistic variable problems but also provides more objective and direct information for service quality attributes. The discrepancy rate between expected service and perceived service that is perceived service quality is evaluated by hamming distance. To evaluate the discrepancy rate from hamming distance, we induced general solutions to compute the intersection area between two triangle fuzzy numbers and the weak or strong attributes in public sectors are clarified.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1359-1361
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• 1996

This paper describes a design of real time fuzzy controller using Minimum fuzzy control Rule Selection Method(MRSM). The control algorithm of dynamic systems needs less computation time and memory. To reduce the computation time of fuzzy logic controller, minimum number of rules are to be selected for the fuzzy input variable. The universe of discourse is divided by the number of linguistic labels to allocate the assigned membership function to the fuzzy input variables. In this case, since fuzzy input variables are continuous, scale factor SU is tuned independently. According to increment of SU control surface is improved to adapt the change of system parameter. At this, crisp control surface is increased. With the increament of crisp control surface, fuzzy control surface is reduced. When error state deviates from desirable error state, crisp control surface is more useful than fuzzy control surface for obtaining fast rising time.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.7
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• pp.29-40
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• 2009

This paper proposes a modeling and controller design approach for a hybrid wind power generation system that considers a fixed wind-turbine and a dump load. Since operating conditions are kept changing, it is challenge to design a control for reliable operation of the overall system To consider variable operating conditions, Takagi-Sugeno (TS) fuzzy model is taken into account to represent time-varying system by expressing the local dynamics of a nonlinear system through sub-systems, partitioned by linguistic rules. Also, each fuzzy model has uncertainty. Thus, in this paper, a modem nonlinear control design technique, the sliding mode nonlinear control design, is utilized for robust control mechanism In the simulation study, the proposed controller is compared with a proportional-integral (PI) controller. Simulation results show that the proposed controller is more effective against disturbances caused by wind speed and load variation than the PI controller, and thus it contributes to a better quality wind-hybrid power generation system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.278-282
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• 2001

The discovery of tacit knowledge from domain experts is one of the most exciting challenges in today\`s knowledge management. The nature of decision knowledge in determining the quality a firm\`s short-term liquidity is full of abstraction, ambiguity, and incompleteness, and presents a typical tacit knowledge extraction problem. In dealing with knowledge discovery of this nature, we propose a scheme that integrates both knowledge elicitation and knowledge discovery in the knowledge engineering processes. The knowledge elicitation component applies the Verbal Protocol Analysis to establish industrial cases as the basic knowledge data set. The knowledge discovery component then applies fuzzy clustering to the data set to build a fuzzy knowledge based system, which consists of a set of fuzzy rules representing the decision knowledge, and membership functions of each decision factor for verifying linguistic expression in the rules. The experimental results confirm that the proposed scheme can effectively discover the expert\`s tacit knowledge, and works as a feedback mechanism for human experts to fine-tune the conversion processes of converting tacit knowledge into implicit knowledge.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.701-706
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• 1998

A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.45-50
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• 2002

퍼지 균등화(fuzzy equalization)는 어의론적으로(semantically) 의미있고, 실험적으로 (experimentally) 의미있는 언어라벨(linguistic labels)을 붙이도록 하는 조건이다. 지금까지 발표된 퍼지 균등화조건을 갖는 퍼지분할을 생성하는 알고리듬은 주어진 데이터에 대하여, 오직 하나의 퍼지분할만을 생성할 수 있다. 만일 생성된 퍼지 분할이 더 이상 유용하지 못한 것으로 판명되면, 이 알고리듬은 주어진 데이터에 대한 퍼지 균등화조건을 갖는 퍼지분할을 생성할 수 없다. 이는 생성된 퍼지분할을 사용하여 탐색적 발견을 수행하는 데이터마이닝인 경우 더 이상 프로세스가 진행되지 못함을 의미한다. 본 연구에서는 주어진 데이터에 대한 퍼지 균등화조건을 갖는 서로 다른 두 퍼지분할이 존재한다면, 어떠한 관계가 있는지를 증명하고, 위치적 특성을 서술하였다. 이 특성은 추후 퍼지 균등화조건을 갖는 퍼지분할을 원하는 만큼 생성할 수 있는 알고리듬을 만드는데 유용하게 사용 될 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.174-179
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• 2015

Failure Mode and Effects Analysis (FMEA) has been used by Dynamic Positioning (DP) system for risk and reliability analysis. However, there are limitations associated with its implementation in offshore project. 1) since the failure data measured from the SCADA system is missing or unreliable, assessments of Severity, Occurrence, Detection are based on expert's knowledge; 2) it is not easy for experts to precisely evaluate the three risk factors. The risk factors are often expressed in a linguistic way. 3) the relative importance among three risk factors are rarely even considered. To solve these problems and improve the effectiveness of the traditional FMEA, we suggest a Fuzzy-FMEA method for risk and failure mode analysis in Dynamic Positioning System of offshore. The information gathered from DP FMEA report and DP FMEA Proving Trials is expressed using fuzzy linguistic terms. The proposed method is applied to an offshore Dynamic Positioning system, and the results are compared with traditional FMEA.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.566-568
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• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.61-68
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• 1998

It has become important to evaluate the qualitive reliability and condition assessment of existing structural systems in order to establish a rational program for repair and maintenance. Since most of if existing structural system may suffer from defect corrosion and damage, it is necessary to account for their effects in fuzzy reliability analysis, In this paper, an attempt is made to develope a reliability analysis for damaged structural systems using failure possibility theory. Damage state is specified in terms of linguistic valiables using natural language information and numerical information, which are defined by fuzzy sets. Using a subjective condition index of failure possibility and information of the damage state is introduced into the calculation of failure probability. The subjective condition index of quantitative and qualitative analysis method is newly proposed based on the fuzzy set operations, namely logical product, drastic product, logical sum and drastic sum