We construct a fuzzy S 4 , utilizing the fact that CP 3 is an S 2 bundle over S 4. We find that the fuzzy S 4 can be described by a block-diagonal form whose embedding square matrix represents a fuzzy CP 3. We discuss some pending issues on fuzzy S 4 , i.e., precise matrix-function correspondence, associativity of the algebra, and, etc. Similarly, we also obtain a fuzzy S 8 , using the fact tha...

Estimation of the origin-destination trip demand matrix (O-D) plays a key role in travel analysis and transportation planning and operations. Many researchers have developed different O-D matrix estimation methods using traffic counts, which allow simple data collection as opposed to the costly traditional direct estimation methods based on home and roadside interviews. In this paper, a new fuz...

This paper aims at developing a multilevel fuzzy decision support model for urban rail transit planning schemes in China under the background that China is presently experiencing an unprecedented construction of urban rail transit. In this study, an appropriate model using multilevel fuzzy comprehensive evaluation method is developed. In the decision process, the followings are considered as th...

Risk Assessment for Health and Safety (RAH&S) of workers is a complex task that entails the consideration of many parameters which are, more often than not, difficult to quantify. RAH&S in the construction industry is rampant with inadequate data and/or imprecise and incomplete information, particularly in the design stage, for which traditional quantitative approaches do not give adequate answ...

The purpose of this study is to apply an integrated algorithm for knowledge structure analysis. The integrated algorithm includes Item Response Theory (IRT) and Fuzzy Structural Modeling (FSM). The authors use the psychometric model IRT for calculation the ability of task-takers. Based on original response data and item-concept matrix, the individualized fuzzy subordinate matrix is acquired. FS...

The Gustafson-Kessel fuzzy clustering algorithm is capable of detecting hyperellipsoidal clusters of different sizes and orientations by adjusting the covariance matrix of data, thus overcoming the drawbacks of conventional fuzzy c-means algorithm. In this paper, an adaptive version of the Gustafson-Kessel algorithm is proposed. The way to adjust the covariance matrix iteratively is introduced ...