Current Issue : October - December Volume : 2013 Issue Number : 4 Articles : 5 Articles
A sensor resource management system that employs fuzzy logic to provide the utility functions to a game theoretic approach is\r\ndeveloped.Theapplication looks at a virtual fence problem where several unattended ground sensors are placed in remote locations\r\nto act as virtual sentries. The goal of the approach is to maximize the battery life while tracking targets of interest. This research also\r\nconsiders the incorporation of uncertainty into the fuzzy membership functions. Both type-2 fuzzy logic and the use of conditional\r\nfuzzy membership function are employed. The type-2 fuzzy logic is employed in the case of acoustical sensor tracking accuracy\r\ndegradation, while the condition-based membership functions are used to adapt to different conditions, such as environmental\r\nconditions and sensor performance degradation, over time.The resourcemanagement process uses fuzzy logic to determine which\r\nof the sensor systems on a sensor pod is used to provide initial classification of the target and which sensor or sensors are to be\r\nused in tracking and better classifying the target if it is determined to be of value to the mission. The three different approaches are\r\ncompared to determine when the best times for the more complex approaches are warranted....
This paper presents two new algorithms that speed up the centroid computation of an interval type-2 fuzzy set. The algorithms\r\ninclude precomputation of the main operations and initialization based on the concept of uncertainty bounds. Simulations over\r\ndifferent kinds of footprints of uncertainty reveal that the new algorithms achieve computation time reductions with respect to\r\nthe Enhanced-Karnik algorithm, ranging from 40 to 70%. The results suggest that the initialization used in the new algorithms\r\neffectively reduces the number of iterations to compute the extreme points of the interval centroid while precomputation reduces\r\nthe computational cost of each iteration...
The quality characteristics in the wafer fabrication process are diverse, variable, and fuzzy in nature. How to effectively deal with\r\nmultiresponse quality problems in the wafer fabrication process is a challenging task. In this study, the fuzzy technique for order\r\npreference by similarity to an ideal solution (TOPSIS), one of the fuzzy multiattribute decision-analysis (MADA) methods, is\r\nproposed to investigate the fuzzy multiresponse quality problem in integrated-circuit (IC) wafer fabrication process. The fuzzy\r\nTOPSIS is one of the effective fuzzy MADA methods for dealing with decision-making problems under uncertain environments.\r\nFirst, a fuzzy TOPSIS methodology is developed by considering the ambiguity between quality characteristics. Then, a detailed\r\nprocedure for the developed fuzzy TOPSIS approach is presented to show how the fuzzy wafer fabrication quality problems can be\r\nsolved. Real-world data is collected from an IC semiconductor company and the developed fuzzy TOPSIS approach is applied to\r\nfind an optimal combination of parameters. Results of this study show that the developed approach provides a satisfactory solution\r\nto the wafer fabrication multiresponse problem. This developed approach can be also applied to other industries for investigating\r\nmultiple quality characteristics problems...
We introduce a novel concept of multiaspect soft set which is an extension of the ordinary soft set by Molodtsov. Some basic concepts, operations, and properties of the multiaspect soft sets are studied. We also define a mapping on multiaspect soft classes and investigate several properties related to the images and preimages of multiaspect soft sets....
Fuzzy set theory, rough set theory, and soft set theory are three effective mathematical tools for dealing with uncertainties and have\r\nmany wide applications both in theory and practise.Meng et al. (2011) introduced the notion of soft fuzzy rough sets by combining\r\nfuzzy sets, rough sets, and soft sets all together. The aim of this paper is to study the parameter reduction of fuzzy soft sets based\r\non soft fuzzy rough approximation operators. We propose some concepts and conditions for two fuzzy soft sets to generate the\r\nsame lower soft fuzzy rough approximation operators and the same upper soft fuzzy rough approximation operators. The concept\r\nof reduct of a fuzzy soft set is introduced and the procedure to find a reduct for a fuzzy soft set is given. Furthermore, the concept\r\nof exclusion of a fuzzy soft set is introduced and the procedure to find an exclusion for a fuzzy soft set is given....
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