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Abstract: In this paper, we present a new method for fuzzy risk analysis based on the proposed new fuzzy ranking method for ranking generalized fuzzy numbers with different left heights and right heights. First, we present a fuzzy ranking method for ranking generalized fuzzy numbers with different left heights and right heights. The proposed method considers the areas of the positive side, the areas of the negative side and the centroid values of generalized fuzzy numbers as the factors for calculating the ranking scores of generalized fuzzy numbers with different left heights and right heights. It can overcome the drawbacks of the existing fuzzy ranking methods. Then, we propose a new method for fuzzy risk analysis based on the proposed fuzzy ranking method, where the evaluating values are represented by generalized fuzzy numbers. The proposed fuzzy risk analysis method provides us with a useful way for fuzzy risk analysis based on generalized fuzzy numbers with different left heights and right heights. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new method for students’ learning achievement evaluation by automatically generating the importance degrees of the attributes of questions. The proposed method considers the “accuracy rate”, the “time rate”, the “importance” and the “complexity” for evaluating students’ learning achievement. First, the proposed method transforms the attributes “accuracy rate” and “time rate” into the “effect of accuracy rate” and the “effect of time rate”, respectively. Then, it generates the weights of the attributes “effect of accuracy rate”, “effect of time rate”, “importance” and “complexity”, respectively. Then, it generates the importance degrees of the attributes of questions based on the weights of the attributes. Then, it calculates the learning achievement indices of the students having the same total score. Finally, it determines the new ranking order of the students having the same original total score based on the learning achievement indices of the students. The proposed method is simpler than due to the fact that the proposed method is based on simple arithmetic calculations rather than the complicated fuzzy reasoning method presented in . The time complexity of the proposed method is O(mn), where m denotes the number of questions and n denotes the number of students. [Copyright &y& Elsevier]

Abstract: Radio-frequency identification (RFID) uses non-contact data access mechanism to read or write tag data through the radio frequency transmitted by the readers. Currently, the RFID systems are applied in different areas. However, the data access rate of a RFID system could be interfered by the environment furnishings, barriers, deployment method, and number of readers. The system stability heavily depends on the experiences of the deployment engineers especially for the active readers. This paper proposes an intelligent deployment system for RFID readers. The system uses the furnished simulation of environment and the best deployment of readers to construct an automatic deployment system for the RFID readers. The former constructs a fuzzy interference model of barriers according to the materials and inference degree of the furnishings. The latter uses the reader cluster and genetic algorithm to establish the best deployment plan of readers. The experimental results show that the system can reduce the interference problem of reader deployment as well as improve the reliability and stability of the RFID systems. [Copyright &y& Elsevier]

Abstract: One critical problem in the operations of chemical processes is the occurrence of abnormal events. Therefore, an effective process monitoring methodology that can help detect, diagnose and predict abnormal events becomes potentially very useful. For the purpose of knowledge representation of chemical abnormality, a specified type of timed fuzzy Petri net (tFPN) approach is explicitly introduced in this paper. The dominant feature of tFPN metrics can be recognized from the fact that a timing factor is assigned to each transition, as well as a degree of reliability is associated with each place, which allows accurately representing the dynamic nature of fuzzy knowledge pertaining to abnormal events. Following a procedure towards abnormal event monitoring, two efficient algorithms in terms of abnormality prognostication and diagnosis are exploited by means of reachability analysis of tFPN. The benefits of derived techniques and solutions are illustrated through a case study consisting in a polypropylene reactor. [Copyright &y& Elsevier]

Abstract: The index C pmk is a well-known loss-based process capability index. It can reveal more information about the location of the process mean compared with other classic process capability indices. This index is also more sensitive than other capability indices to any deviations from process mean. When there are some uncertainties in observations, fuzzy logic can be employed to manage these uncertainties. There is some research on different fuzzy process capability indices and this paper is an extension of for the process capability index C pmk of fuzzy numbers. In order to find the membership function of process capability index C pmk , the α-cuts of the fuzzy observation were employed. An example of fuzzy process capability C pmk calculator was illustrated and compared with other classic fuzzy process capability indices and . Results showed that fuzzy has the advantages of both and . Since the index C pmk is a more sensitive index compared with other classic indices, the fuzzy process capability index can be a more sensitive fuzzy index compared with and . [Copyright &y& Elsevier]

Abstract: In this paper, we present a new method for analyzing fuzzy risk based on a new method for ranking generalized fuzzy numbers. First, we present a new method for ranking generalized fuzzy numbers. It considers the areas on the positive side, the areas on the negative side and the heights of the generalized fuzzy numbers to evaluate ranking scores of the generalized fuzzy numbers. The proposed method can overcome the drawbacks of some existing methods for ranking generalized fuzzy numbers. Then, we apply the proposed method for ranking generalized fuzzy numbers to develop a new method for dealing with fuzzy risk analysis problems. The proposed method provides us with a useful way to deal with fuzzy risk analysis problems based on generalized fuzzy numbers. [ABSTRACT FROM AUTHOR]