ADJUSTED VITERBI TRAINING

Viterbi training (VT) provides a fast but inconsistent estimator of hidden Markov models (HMM). The inconsistency is alleviated with a little extra computation when we enable VT to asymptotically fix the true values of the parameters. This relies on infinite Viterbi alignments and associated with them limiting probability distributions. First in a sequel, this article is a proof of concept; it focuses on mixture models, an important but special case of HMM where the limiting distributions can be calculated exactly. A simulated Gaussian mixture shows that our central algorithm (VA1) can significantly improve the accuracy of VT with little extra cost. Next in the sequel, we present elsewhere a theory of the adjusted VT for the general HMMs, where the limiting distributions are more challenging to find. Here, we also present another, more advanced correction to VT and verify its fast convergence and high accuracy; its computational feasibility requires additional investigation.

Agent-Based NC Process Planning for Complete Machining on Turning Centers

The application of turning centers for complete machining greatly increases production efficiency by means of introducing new machining possibilities. At the same time, it makes the generation of NC process plans more complicated and as a result the current process planning methods often fail in dealing with the new requirements. Based on agent theory this paper presents a new strategy for distributing a complex planning task to different relatively small planning units, so-called agents, which are defined considering both machine and workpiece structure. Instead of a central algorithm the agent based planning method generates NC process plans through some cooperative activities between different agents. The activity model and the cooperation rule are also discussed in detail.