Approximate Kalman-Bucy filter for continuous-time semi-Markov jump linear systems

The aim of this paper is to propose a new numerical approximation of the Kalman-Bucy filter for semi-Markov jump linear systems. This approximation is based on the selection of typical trajectories of the driving semi-Markov chain of the process by using an optimal quantization technique. The main advantage of this approach is that it makes pre-computations possible. We derive a Lipschitz property for the solution of the Riccati equation and a general result on the convergence of perturbed solutions of semi-Markov switching Riccati equations when the perturbation comes from the driving semi-Markov chain. Based on these results, we prove the convergence of our approximation scheme in a general infinite countable state space framework and derive an error bound in terms of the quantization error and time discretization step. We employ the proposed filter in a magnetic levitation example with markovian failures and compare its performance with both the Kalman-Bucy filter and the Markovian linear minimum mean squares estimator

THE MASSIFICATION PROCESS IN CHINESE HIGHER EDUCATION

This paper demonstrates the massification process in higher education using as reference China, which reached in a few years the largest university system in the world. To do this, we present in the theoretical reference the Government intervention and its economic responsibilities, the main challenges of global higher education and the effects of globalization on this level of education. As regards the methodology, this study is designed on the principles of explanatory research, with qualitative approach. Data were collected through documentary and bibliographic research, and subsequently analyzed and interpreted to record the findings that were correlated with other data collected. This research shows at its end how was the expansion of Chinese higher education, which was a elite system and became a mass system, becoming a reference for other nations that also seek to expand this educational level

Explicit fairness in testing semantics

In this paper we investigate fair computations in the pi-calculus. Following Costa and Stirling's approach for CCS-like languages, we consider a method to label process actions in order to filter out unfair computations. We contrast the existing fair-testing notion with those that naturally arise by imposing weak and strong fairness. This comparison provides insight about the expressiveness of the various `fair' testing semantics and about their discriminating power.Comment: 27 pages, 1 figure, appeared in LMC