The aim of this study is to develop an automatic control loop monitoring system. The first step includes the detection of changes and of oscillations in the control error and in the control signal. To determine the parameters of the statistical change detection test (a CUSUM test), the average run length (ARL) function is exploited. To detect oscillations, a modified version of Hägglund’s appro...

there are miscellaneous quality characteristics in healthcare which are interested to be monitored. however, monitoring each characteristic needs a special statistical method. there are some characteristics with very small incidence rates that it’s usually considered not to be necessary to monitoring them, since their incidence rates are so small that p and np charts are not able to monitor the...

usually, in monitoring schemes the nominal value of the process parameter is assumed known. however, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. on the other hand, applying a fixed control limit for the risk-adjusted bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar...

In these notes we describe how to measure the confidence we have in our Monte-Carlo estimates. In particular, we use he Central Limit Theorem to construct confidence intervals for the quantity we are truing to estimate. We also describe methods to estimate the number of samples that are required to achieve a given confidence level.

Recovery is a hard problem in environments where transactions perform work in a cooperative style (e.g., design environments). We propose concepts to control cooperation and recovery within nested transaction hierarchies. By allowing different nodes to run different protocols, we can build so-called recovery spheres with well-defined properties. We characterize those properties and illustrate t...