A Model to Improve the Quality Products

The topic of this paper is to present a solution who can improve product quality following the idea: “Unlike people who have verbal skills, machines use "sign language" to communicate what hurts or what has invaded their system’. Recognizing the "signs" or symptoms that the machine conveys is a required skill for those who work with machines and are responsible for their care and feeding. The acoustic behavior of technical products is predominantly defined in the design stage, although the acoustic characteristics of machine structures can be analyze and give a solution for the actual products and create a new generation of products. The paper describes the steps in technological process for a product and the solution who will reduce the costs with the non-quality of product and improve the management quality.improvement quality, iceberg quality costs, virtual simulation, model product design, low design product

Active planning for underwater inspection and the benefit of adaptivity

We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). Unlike a large body of prior work, we focus on planning the views of the AUV to improve the quality of the inspection, rather than maximizing the accuracy of a given data stream. We formulate the inspection planning problem as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We rigorously analyze the benefit of adaptive re-planning for such problems, and we prove that the potential benefit of adaptivity can be reduced from an exponential to a constant factor by changing the problem from cost minimization with a constraint on information gain to variance reduction with a constraint on cost. Such analysis allows the use of robust, non-adaptive planning algorithms that perform competitively with adaptive algorithms. Based on our analysis, we propose a method for constructing 3D meshes from sonar-derived point clouds, and we introduce uncertainty modeling through non-parametric Bayesian regression. Finally, we demonstrate the benefit of active inspection planning using sonar data from ship hull inspections with the Bluefin-MIT Hovering AUV.United States. Office of Naval Research (ONR Grant N00014-09-1-0700)United States. Office of Naval Research (ONR Grant N00014-07-1-00738)National Science Foundation (U.S.) (NSF grant 0831728)National Science Foundation (U.S.) (NSF grant CCR-0120778)National Science Foundation (U.S.) (NSF grant CNS-1035866