In this study, a Bayesian Network (BN) is considered to represent a nuclear plant mechanical system degradation. It describes a causal representation of the phenomena involved in the degradation process. Inference from such a BN needs to specify a great number of marginal and conditional probabilities. As, in the present context, information is based essentially on expert knowledge, this task becomes very complex and rapidly impossible. We present a solution which consists of considering the BN as a log-linear model on which simplification constraints are assumed. This approach results in a considerable decrease in the number of probabilities to be given by experts. In addition, we give some simple rules to choose the most reliable probabilities. We show that making use of those rules allows to check the consistency of the derived probabilities. Moreover, we propose a feedback procedure to eliminate inconsistent probabilities. Finally, the derived probabilities that we propose to solve the equations involved in a realistic Bayesian network are expected to be reliable. The resulting methodology to design a significant and powerful BN is applied to a reactor coolant sub-component in EDF Nuclear plants in an illustrative purpose.

An increasingly popular approach to support military forces deployed in urban environments consists in using autonomous robots to carry on critical tasks such as mapping and surveillance. In order to cope with the complex obstacles and structures found in this operational context, robots should be able to perceive and analyze their world in 3D. The method presented in this paper uses a 3D volumetric sensor to efficiency map and explore urban environments with an autonomous robotic platform. A key feature of our work is that the 3D model of the environment is preserved all along the process using a multiresolution octree. This way, every module can access the information it contains to achieve its tasks. Simulation and real word tests were performed to validate the performance of the integrated system and are presented at the end of the paper.

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