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Adding User Interface Items To be useful, most COM add-ins add some custom buttons, menu items, or other items to the user interface of the application that is hosting the addin These UI lements give your users a way to interact with your COM add-in Adding UI elements in InfoPath 2007 is typically done by using the CommandBars collection and CommandBar object Therefore, as we show you how to create COM add-ins for InfoPath, along the way, we ll also show you how to use the CommandBars collection to add your own UI However, there is one thing you should keep in mind if you plan to create a COM add-in to be shared by multiple applications As we re sure you ve noticed by now, certain applications in the 2007 Microsoft Of ce system now include a new user interface the Ribbon Certain other applications, including InfoPath, do not Therefore, the process of creating user interface elements for add-ins that are shared across multiple applications is a little more dif cult Since our discussion of COM add-ins is limited to InfoPath 2007, however, we will only show you how to add UI using the CommandBars collection As we ve mentioned, since a full discussion of creating UI, whether you re using command bars or the Ribbon, is outside the scope of this book, we will once again refer you to the MSDN online documentation However, once we walk through the samples together, you should have enough knowledge to add any user interface elements to InfoPath that you like Now, without further ado, let s create our rst COM add-in for InfoPath.

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Abate et al [1] showed this problem to be complementary to the problem of optimizing the control policy of an SHS such that the reach probability of some prescribed unsafe set remains below some given maximum level, and that the same dynamic programming-based computation of maximal safe sets can be used The dynamic programming approach becomes computationally intractable when the SHS considered is of large-scale type Prandini and Hu [39] developed a Markov chain approximation based method for the computation of reach probabilities for a continuous-time SHS This way the dynamic programming challenge is avoided, but the computational load of their method prohibits its application to a large-scale SHS Prajna et al [38] developed an approach which obtains an upper bound of the reach probability, but this cannot handle large-scale SHS either In theory, reach probability estimation can be done by simulating many trajectories of the process considered, and counting the fraction of cases where the simulated trajectory reaches the unsafe set within some given period T When the reach probability value is very small then the number of straightforward Monte Carlo (MC) simulations needed is impractically large The rare event estimation literature forms a potentially rich source of information for speeding up MC simulation, for example by combining methods from large-deviation and importance sampling theories [11, 29, 31] An early successful development in this area is sequential MC simulation for the estimation of the intensity of radiation that penetrates a shield of absorbing material in nuclear physics (see [10]) More recently this approach has also found application in non-nominal delay time and loss estimation in telecommunication networks [3] L Ecuyer et al [36] provide a very good recent overview of these sequential MC simulation developments In order to exploit rare event estimation theory within probabilistic reachability analysis of controlled SHS, we need to establish a theoretically unambiguous connection between the two concepts Implicitly, this connection has recently been elaborated by Del Moral and co-workers [16 18, 20, 21] They embedded theoretical physics equations, which supported the development of advanced MC simulations, within the stochastic analysis setting that is typically used for probabilistic reachability analysis They subsequently showed that this embedding provides a powerful background for the development and analysis of sequential MC simulation for rare event simulation In 3 of the present volume this novel development is well explained in the broader context of splitting techniques in rare event simulation The aim of this chapter is to present a part of the framework developed by Del Moral et al [16 18, 20, 21] in a probabilistic reachability setting, to further develop this for a large-scale SHS, and to demonstrate its practical use for safety veri cation of an advanced air traf c operation In [8, 9], the practical use of the approach of Del Moral [16 18, 20, 21] for safety veri cation of an advanced air traf c operation has already been demonstrated for some speci c scenarios In these scenarios, the main contributions to the reach probability value came from diffusion behavior It also became clear that the same sequential MC simulation approach failed to work for scenarios of the same air traf c.

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