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APPENDIX
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telecommunications smart card speci cation, it is proposed that SMG9 extends its role to act as the central focus point for all next generation telecommunications smart cards. This statement agreed by the 16 delegates to the 2G/3G SIM, USIM & R-UIM of cials meeting held on 1 November 1999 in Austin, Texas, was going to change the role of SMG9 altogether. It was also an expression of the trust SIMEG and SMG9 had built up over the years in the world as a group dedicated to the advancement of smart cards and their use in mobile communications. The sentence is contained in the Committee Correspondence 40 which was sent as an output document of that meeting to all the major players in the telecommunications sector involved in specifying the use of a smart card for their systems. The document continues: SMG9 should be invited to extend its scope and take the role of managing common aspects of mobile telecommunication smart card in the following areas:
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where z denotes the position on the conductor, o is the angular frequency, and Ayf and Ayr are the amplitudes of the forward and backward voltage waves in the y mode. The mode wave amplitudes Ayf and Ayr can be determined using the known terminal conditions at both ends of each strip conductor. The values of voltage and current in the time domain can be found by an inverse Laplace transformation of the above equations.
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message "ready to run SUM.PAR\n"; run; message "SUM.PAR finished."; :****************************************************** 10.4.2 Running sum.par
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where Vinf x; t; m denotes the voltage waveform along the semi-in nite line given by " Vinf x; t; m VS  p !m=2  q  p t x lc r r= 2l t p e I0 t2 x lc 2 2l t x lc
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from the case data to partition the cases into subsets. However, in real-life situations, it is sometimes impossible to de ne a classifying concept in a precise (crisp) manner. For example, given a new case, it may not be possible to know to which class it belongs. The best knowledge derived from past cases may only give us enough information to say that this new case belongs to a boundary between certain cases, which may consist of various possible solutions. The formulation of these lower- and upper-set approximations can be generalized to some arbitrary level of precision which forms the basis of rough concept approximations. Some basic de nitions and examples of rough set theory are described in Appendix D. Let us now look at an example (see Table 2.2) that illustrates the concept of rough sets. Let us consider a patient information system consisting of six patients, where each one is described by three health conditions: C1 , C2 , and C3 , and an illness by y1 or y2. Now the question is how to describe an illness uniquely (i.e., y1 or y2 ) in terms of the health conditions. After analyzing the data, it is clear that this question cannot be answered uniquely. For example, patients 3 and 6 have the same conditions, but patient 3 suffers from y1 , whereas patient 6 suffers from y2 . Hence, it is impossible to give a unique description of illness y1 or y2. The best we could decide is that patients 1, 2, and 5 surely suffer from illness y1 , whereas patients 1, 2, 3, 5, and 6 possibly suffer from illness y1 . Similarly, patient 4 surely suffers from illness y2 , whereas patients 3, 4, and 6 possibly suffer from illness y2 . Since it is impossible to give the unique characteristics of y1 or y2, a rough set based technique can be used. Two sets corresponding to the concepts of surely and possibly, the lower and upper approximations for each illness, respectively, describe the characteristics of y1 and y2 . The formal de nition of rough sets given by Pawlak [4] is as follows: Any subset B of A determines a binary relation IB on U, called an indiscernibility relation, and de ned as x IB y if and only if Attr(x) Attr(y) for every Attr 2 B, where Attr(x) denotes the value of attribute a for element x. IB is obviously an equivalence relation. The family of all equivalence classes of IB (i.e., the partition determined by B) will be denoted by U/IB, or simply U/B; an equivalence class of IB (i.e., the block of the partition U/B) containing x will be denoted by [x]B. If (x,y) belongs to IB, x and y will be considered as B-indiscernible. Equivalence classes of the relation IB or
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nor the first two rules of step 3 will direct any edges. Hence there must be a default rule to ensure that all edges will eventually be directed. Note that the actual learning takes place in the first two steps of this algorithm. In order to understand this, recall that all Markov equivalent directed acyclic graphs share the same skeleton33 and the same set of v-structures34 (cf. Theorem 4.1.24 on page 113). Obviously, the skeleton of the graph is determined in the first step of the algorithm, while the second identifies the v-structures. Therefore, after these two steps, the graph is identified up to Markov equivalence. In the third step it is only ensured that no directed cycles (first rule of the third step) and no additional v-structures are introduced (second rule of the third step). That is, these two rules fix edge directions that are implied by the v-structures found in the second step. Hence, if we cancel the third rule and thus implicitly accept that the algorithm may stop with a graph with some undirected edges, it yields a compact representation of the class of all Markov equivalent graphs that are perfect maps. Although this algorithm appears to be simple and convenient, there are some problems connected with it, which we are going to discuss, together with attempts at their solution, in the remainder of this section. The first problem is that in order to make sure that there is no set SAB that renders two attributes A and B conditionally independent, it is, in principle, necessary to check all subsets of U {A, B}, of which there are
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Handbook of Applied Algorithms: Solving Scienti c, Engineering and Practical Problems Edited by Amiya Nayak and Ivan Stojmenovi Copyright 2008 John Wiley & Sons, Inc. c
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