• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.225-237
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• 2016

In conventional transportation problem (TP), all the parameters are always certain. But, many of the real life situations in industry or organization, the parameters (supply, demand and cost) of the TP are not precise which are imprecise in nature in different factors like the market condition, variations in rates of diesel, traffic jams, weather in hilly areas, capacity of men and machine, long power cut, labourer's over time work, unexpected failures in machine, seasonal changes and many more. To counter these problems, depending on the nature of the parameters, the TP is classified into two categories namely type-2 and type-4 fuzzy transportation problems (FTPs) under uncertain environment and formulates the problem and utilizes the trapezoidal fuzzy number (TrFN) to solve the TP. The existing ranking procedure of Liou and Wang (1992) is used to transform the type-2 and type-4 FTPs into a crisp one so that the conventional method may be applied to solve the TP. Moreover, the solution procedure differs from TP to type-2 and type-4 FTPs in allocation step only. Therefore a simple and efficient method denoted by PSK (P. Senthil Kumar) method is proposed to obtain an optimal solution in terms of TrFNs. From this fuzzy solution, the decision maker (DM) can decide the level of acceptance for the transportation cost or profit. Thus, the major applications of fuzzy set theory are widely used in areas such as inventory control, communication network, aggregate planning, employment scheduling, and personnel assignment and so on.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.167-172
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• 2006

As ubiquitous devices are fast spreading, the communication problem between humans and these devices is on the rise. The use of context is important in interactive application such as handhold and ubiquitous computing. Context is not crisp data, so it is necessary to introduce the fuzzy concept. The proxity relation is represented by the degree of closeness or similarity between data objects of a scalar domain. A context manager of context-awareness system evaluates imprecise queries with the proximity relations. in this paper, a systematic proximity elicitation method are proposed. The proposed generation method is simple and systematic. It is based on the well-known fuzzy set theory and applicable to the real world applications because it has tuning parameter and weighting factor. The proposed representations of proximity relation is more efficient than the ordinary matrix representation since it reflects some properties of a proximity relation to save space. We show an experiments of quantitative calculate for the proximity relation. And we analyze the time complexity and the space occupancy of the proposed representation method.

• 한국수자원학회논문집
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• 제46권2호
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• pp.171-182
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• 2013

본 논문은 퍼지집합이론을 이용한 다기준 의사결정(MCDM) 과정에 관한 연구이며, 농업용 저수지의 수자원계획 평가에 관한 가장 합리적이고 효율적인 방법을 찾기 위해 FAHP를 이용하여 각각의 대안들에 대한 우선순위를 정하였다. 농업용 저수지의 추가저수량 확보를 위해 의사결정자 및 수혜자가 동시에 만족하는 조건을 조사하여 6개의 주 기준을 설정하고 이에 따른 10개의 대안을 설정하여 우선순위를 결정하고자 하였으며, 우선순위를 결정에서의 불확실성과 모호성을 규명하기 위해 퍼지수와 언어변수를 정의하였다. 또한 적절한 의사결정모형의 제시를 위해 의사결정 방법에 따른 분석결과를 비교 검토하였으며 FAHP 기법 적용의 타당성을 논의하였다.

• 한국지능시스템학회논문지
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• 제1권2호
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• pp.16-34
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• 1991

It has been widely accepted that expert systems must reason from multiple sources of information that is to some degree evidential - uncertain, imprecise, and occasionally inaccurate - called evidential information. Evidence theory (Dempster/Shafet theory) provides one of the most general framework for representing evidential information compared to its alternatives such as Bayesian theory or fuzzy set theory. Many expert system applications require evidence to be specified in the continuous domain - such as time, distance, or sensor measurements. However, the existing evidence theory does not provide an effective approach for dealing with evidence about continuous variables. As an extension to Strat's pioneeiring work, this paper provides a new combination rule, a new method for mass function transffrmation, and a new method for rendering joint mass fuctions which are of great utility in evidence theory in the continuous domain.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1647-1651
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• 2009

The uncertainty or imprecision associated with vague parameters and weighting sets, reduces the ability to decide what alternative is better for a particular location. To efficiently reduce the effect of imprecision frequently arising in available information, fuzzy theory has been used to improve consideration of imprecision in a Multi-Criteria Decision Analysis (MCDA) problem. Fuzzy logic offers a way to represent and handle imprecision present in continuous real world applications. A GIS implementing fuzzy set theory, (referred to in this paper as the "Spatial Fuzzy Approach") enables decision makers to express imprecise concepts associated with geographic data and provides decision makers the ability to have even more definition and discrimination in terms of the best alternatives for a particular spatial location. This study is focused on addressing questions pertaining to the methodology of floodplain analysis using GIS and Spatial Fuzzy MCDA to evaluate flood damage reduction alternatives. The issues will be examined in a case study of the Suyoung River Basin in Pusan, Korea.

• 산업경영시스템학회지
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• 제15권26호
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• pp.13-19
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• 1992

MODM(multiobjective decision-making)problem is very complex system for the analysist and decision maker. Therefore, it requires suitable MODM method to solve multiobjective decision-making problem. This paper presents an interactive fuzzy decision making method for solving multiobjective nonlinear programming problems with fuzzy goals and $\alpha$-cut set of fuzzy numbers. In our interactive method, if the decision maker specifies the degree $\alpha$of the objective value and the imprecise goals, λ-mux problem is solved. To examplify the proposed method, an interactive computer programming written in FORTRAN and an illustrate numerical example along with computer outputs are presented.

• 공학논문집
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• 제3권1호
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• pp.117-121
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• 1998

본 논문에서는 실시간 프로그래밍 언어와 적절히 연동할 수 있는 자원관리 모델을 제시한다. 시스템의 자원을 계층적으로 관리하여 일관된 인터페이스 환경을 유지하면서 미세조정까지 가능한 자원 이용환경을 제공하고자 하는 것이다. 이와 같은 객체지향 자원관리 기법은 계산 시간 부족을 처리하는 근사값 접근방법의 효과를 더욱 상승시킬 것이며, 또한 동적 실시간 시스템을 구축함도 용이롭게 할 수 있을 것이다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.145-146
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• 2007

This paper presents the sensitivity analysis of the leasst qsuares velocity estimation of an omnidirectional mobile robot using a regular polygonal array of optical mice. First, the velocity kinematics from a mobile robot to an array of optical mice is derived as an overdetermined linear system. Then, for a given set of optical mouse readings, the least squares velocity estimation of a mobile robot is obtained as the simple average. Finally, the sensitivity analysis of the proposed least squares velocity estimation to imprecise installation is made.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3060-3062
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• 2000

A fast optimal path planning algorithm using the analog Cellular Nonlinear Circuits (CNC) is proposed. The proposed algorithm compute the optimal path using subgoal-based dynamic programming algorithm. In the algorithm, the optimal paths are computed regardless of the distance between the initial and the goal position. It begins to find subgoals starting from the initial position when the output of the initial cell becomes nonzero value. The suboal is set as the initial position to find the next subgoal until the final goal is reached. Simulations have been done considering the imprecise hardware fabrication and the limitation of the magnitude of input value.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 Asia Navigation Conference
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• pp.176-183
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• 2006

A novel intelligent anti-collision decision-making supporting system is addressed in this paper. To obtain precise anti-collision information capability, an innovative neurofuzzy network is proposed and applied. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to train the parameters of the Fuzzy Inference System (FIS). The learning process is based on a hybrid learning algorithm and off-line training data. The training data are obtained by trial manoeuvre. This neurofuzzy network can be considered to be a self-learning system with the ability to learn new information adaptively without forgetting old knowledge. This supporting system can decrease ship operators' burden to deal with bridge data and help them to make a precise anti-collision decision.