1000 resultados para variable aggregation


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In stamping operations, the sliding of the sheet metal over the drawbeads is of great importance. The geometry of the drawbead and the degree of penetration both influence material flow and alter the frictional effects between the work and the tool. The effect of drawbead penetration over drawbeads has been studied using the Drawbead Simulator (DBS) test. The contact phenomenon between the sheet and drawbeads was analysed by examining deformed samples with an image fitting technique. The results were compared with an FE simulation and with an approximate geometric analysis. The results give a useful relationship between the rates of change of the contact angle with increasing bead penetration.

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Many methods to calculate message latencies for Controller Area Network (CAN) have previously been presented based upon the static worst-case behaviour of the system. With the use of modern simulation tools however, the behaviour of CAN networks can be simulated dynamically in order to find the likely worst-case response times for CAN messages. This paper shows the development of an automotive body control network model to be used as the basis for further simulations. A method to simulate the Worst-Case Response Time of this model is then presented, taking into account random queuing jitter.

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Aggregation operators model various operations on fuzzy sets, such as conjunction, disjunction and averaging. Recently double aggregation operators have been introduced; they model multistep aggregation process. The choice of aggregation operators depends on the particular problem, and can be done by fitting the operator to empirical data. We examine fitting general aggregation operators by using a new method of monotone Lipschitz smoothing. We study various boundary conditions and constraints which determine specific types of aggregation.

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Aggregation operators model various operations on fuzzy sets, such as conjunction, disjunction and aver aging. The choice of aggregation operators suitable for a particular problem is frequently done by fitting the parameters of the operator to the observed data. This paper examines fitting general aggregation operators by using a new method of Lipschitz approximation.

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We determine the affine equivalence classes of the eight variable degree three homogeneous bent functions using a new algorithm. Our algorithm applies to general bent functions and can systematically determine the automorphism groups. We provide a partial verification of the computer enumeration of bent functions by Meng et al.

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We propose a generalization of the notion of the absorbent element of aggregation operators. Our construction involves tuples of values that are absorbent, that is, that decide the result of aggregation. We analyze some basic properties of this generalization and determine the absorbent tuples of some popular classes of aggregation operators.

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This paper establishes tight upper and lower bounds on Lipschitz aggregation operators considering their diagonal, opposite diagonal and marginal sections. Also we provide explicit formulae to determine the bounds. These are useful for construction of these type of aggregation operators, especially using interpolation schemata.

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We generalize the notion of a neutral element of aggregation operators. Our construction involves tuples of values that are neutral with respect to the result of aggregation. Neutral tuples are useful to model situations in which information from different sources, or preferences of several decision makers, cancel each other. We examine many popular classes of aggregation operators in respect to their neutral sets, and also construct new aggregation operators with predefined neutral sets

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This paper introduces, a robust and stable algorithm based on artificial formation forces, for multi-agent system (MAS) aggregation in 2D space. The MAS model with artificial forces; consists of inter-member collision avoidance element, formation generation element and a velocity based damping element; is analysed for stability and convergence. Computer simulations are used to illustrate stability and convergence, and to demonstrate effectiveness of the algorithm.