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There is a wide eld of applications of network reliability techniques We nd these problems in evaluations of electrical power networks, transportation systems (especially urban transportation systems; see [56]), interconnection networks (ie, networks connecting processors, memory and other devices inside a multiprocessor computer), fault-tolerant computer architectures, etc As already stated, a central area of application is in the evaluation of communication ystems The usefulness of connectivity measures such as those ones presented above is clear, for instance, in packet switching communication networks using dynamic routing which allows rerouting of data in the event of the failure of a link It must be said that many modern packet switching networks are rather dense and that the reliability measures considered tend to be close to unity The computation of the unreliability of the system systematically corresponds then to the evaluation of the probability of a rare event In this section, we give some examples of applications in different contexts, such as the design of telecommunication networks and other systems [5, 20 25, 43, 44, 47, 50, 57], the design and evaluation of mobile ad hoc networks and of tactical radio networks (specially in military contexts) [19], the evaluation of transport networks, and the assessment of the reliability of road networks with respect to seismic hazards and other disasters [35, 45, 49, 61] The aim of this section is to underline the wide range of application of these problems, and thus, of the methods proposed to solve them Once again, let us recall that in most of the cases, the events of interest are rare The design of the topology of telecommunication and computer networks is one of the settings where the application of reliability models is more direct As such, there are a number of papers which tackle different variants of this problem, which in general involves deciding which components (links, and sometimes nodes) to include in the network so that the communication among terminals is reliable and the cost is as low as possible Such network design problems are in general NP-hard, so that most literature includes the use of combinatorial optimization heuristics (most often genetic algorithms) to nd approximate solutions One of the rst papers on applying genetic algorithms to solve reliable network design problems was published by Kumar et al [43] These authors tackled three different network design problems: maximization of reliability under a diameter constraint, maximization of diameter under a degree constraint, and maximization of average distance under a degree constraint The solution method applied was based on a genetic algorithm, which solved very small instances of.

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6 Apr 2005 ... Demonstrates a method to draw UPC-A barcodes using C#. ... NET 2003 - 7.87 Kb. Image 1 for Drawing UPC-A Barcodes with C# ...

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UPC-A C# class that will generate UPC-A codes. ... Background. I originally built this application in VB. NET . While I was learning C#. NET , I decided to re-write it ...

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UPC-A Barcode Generator for ASP . NET Web Application
This ASP . NET barcode library could easily create and print barcode images using .Net framework or IIS. UPC-A ASP . NET barcode control could be used as a  ...

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the differences in the forecast generated by the combined set (containing total of 100 examples) and the forecast generated by 50 ef cient and 50 inef cient cases Based on the results plotted in Figure 312, it can be seen that the difference between the combined forecast and the forecast generated by learning from ef cient cases is lower (RMS error 00245) than the difference between the combined forecast and the inef cient forecast (RMS 00667) This shows that for larger data sets, ANNs have a tendency to learn patterns from ef cient cases Inef cient cases in the training set lead to poor forecasting accuracy, hurt the monotonicity assumption, and foster inconsistent learning Among the key issues in the DEA assessment of technical ef ciency are: 1 Comparison of sets of DMUs that perform uniform operations 2 Assumption that, for ef cient DMUs, higher values of input should lead to higher values of output 3 No assumption made concerning the functional form that links the inputs and outputs The following are similarities between ANNs and DEA models: 1 DEA and ANNs are nonparametric models 2 Neither DEA nor ANNs make assumptions about the functional form that links its inputs to outputs 3 DEA seeks a set of weights to maximize the technical ef ciency, whereas ANNs seek a set of weights to derive the best possible t through observations of the training data set In the case of using DEA for preprocessing data for ANNs, DEA eliminates some of the inconsistencies in the data that may hurt the monotonicity assumption For example, if the training data set consists of two examples with the same inputs but different outputs, the example with a lower output receives a lower technical ef ciency The lower technical ef ciency increases the likelihood of elimination of the case with the lower ef ciency For a slight difference in outputs, the difference in ef ciency between the two examples may be low and both examples may be included in the training data However, for large differences in outputs, the case with a lower ef ciency may clearly be considered as an outlier (as this may hurt the monotonicity assumption) and has a higher likelihood of elimination from the ef cient training data Whether or not the low ef ciency case is nally eliminated from the ef cient data set depends on its actual ef ciency, number of total examples, and number of inputs, among other things The possibility of eliminating a low ef ciency case the from training data set is important when two examples show inconsistency; keeping both examples in the raining data set would impair the ANN learning Because ANN learning is through nonparametric regression-type learning of a nonlinear curve, outliers and inconsistent data impair its learning DEA models, on the.

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