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2. E1 , E2 : (E1 E2 ) = max{ (E1 ), (E2 )}. Note that this definition differs from Definition 3.2.6 on page 67 only in the range of values of the measure. Note also that the measure of Definition 2.4.8 on page 46 satisfies the axioms of the above definition. By the transition to a (general) possibility measure carried out above it is explained why there is no axiom R( ) = 1 for binary possibility measures: it would have been necessary to revoke this axiom now. With degrees of possibility, ( ) = max ({ }) need not be 1. Adding this constraint would introduce the normalization condition (cf. Definition 2.4.3 on page 37), which we rejected in Section 2.4.6. Due to the close formal proximity of binary and general possibility measures there is not much left to be said. Everything developed following the definition of a binary possibility measure in Definition 3.2.6 on page 67 carries over directly to (general) possibility measures, since the fact that binary possibility measures can assume only the values 0 and 1 was not exploited.

Right-sided spinal cord lesion No neurotransmission in : Right pyramidal tract \ UMN signs right leg Right posterior column \ position and vibration sense loss right leg Right Left Right spinothalamic tract \ pain and temperature sense loss left leg Effect upon bladder variable, probably just intact

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In addition to the technical groups the 3GPP organisation has a Project Coordination Group (PCG). However, the role of this PCG cannot be compared to the role the SMG plenary played. The SMG plenary was an open technical group with the approving authority in all technical questions including approval of new work items. The 3GPP PCG is a closed group with a de ned membership consisting of a limited number representative of each of the partners (SDOs, MRPs) and the leadership (chairman and two vice-chairmen) of each TSG. Thus as a closed group the role of the PCG becomes more like a board overlooking the overall well being of the project. This structure made many long-term SMG delegates concerned about how the overall coordination of the project could be ensured. This new structure was not introduced to overcome known de cits of the SMG organisation, but in my opinion, by political considerations to ensure than no single individual, individual member, organisational partner could obtain a controlling position in the project.

The SPICE results for the step response of coupled crossing lines at adjacent levels in the SiO2 medium, including the effects of distributed as well as lumped couplings, are shown in Fig. 3.6.6. For these results, line lengths are 3 mm, separation is 10 mm, layer thickness is 7 mm, and terminations are 100 each. Figure 3.6.7 shows the schematic, the SPICE model, and the step response of a pair of coupled interconnections on the semi-insulating GaAs substrate including the skin effect losses. The losses are modeled in terms of the RL circuits (see Fig. 3.6.3) represented by the impedances Z1 and Z2 in the inset of Fig. 3.6.7. Other parameters are W S 10 mm, T 2 mm, H1 2 mm, H2 100 mm, 2 mm, and terminating impedances 100 each. The results for a general lossy layered structure consisting of three interconnection lines on a GaAs system, including the frequency-dependent skin effect losses, and the dielectric losses, are shown in Fig. 3.6.8. The interconnection structure cross section and its schematic are shown in the insets. Input signal is a 100-ps pulse with Z1 0 and all other Z s are 100 each.

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changes of case representation structures, and other house keeping tasks. So far, many policies and techniques have been applied to maintaining case bases, some of them based on the soft computing paradigm [22,23]. In this chapter we provide a brief review of different methods of case-base maintenance, explain the signi cance of soft computing, and demonstrate some maintenance algorithms based on soft computing. In Section 5.2 the background of knowledge-based system maintenance is provided for the convenience of readers. The meaning of case-base maintenance, from both the qualitative and quantitative points of view, is explained in Section 5.3. In Sections 5.4 and 5.5, respectively, we describe in detail a roughfuzzy hybridization method and a fuzzy integral based competence model for case-base maintenance.

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