Kai Ding Knihy

Model-based design is a common methodology in the development of embedded complex control systems. Control system engineers typically prefer to use MATLAB Simulink and suitable automatic code generators for the development and deployment of software. Embedded systems are subject to random hardware faults. This thesis proposes an analytical method for the evaluation of the reliability properties of control systems that are designed with Simulink models. The method is based on a transformation of the assembly code, which is generated from the Simulink model, into a formal stochastic error propagation model as well as its quantification through underlying Markov chain models and state-of-the-art probabilistic model-checking techniques. In the case of model-based development, redundancy mechanisms are preferable for direct application at the model level (Simulink model level). This thesis introduces a systematic classification of fault-tolerant design patterns. Such patterns can be applied to the Simulink model to tolerate random hardware faults, and taken into account during the control system design. This thesis proposes a model-level reliability evaluation of Simulink models. The efficiency of the proposed model-level evaluation is verified by a comparison of the reliability properties that are assessed at the assembly and model levels.

This book presents methods for investigating linear versus nonlinear relationships and adaptively fitting appropriate models when nonlinearities are present. Data analysts will learn to incorporate nonlinearity in predictor variables into regression models for various outcome types. Nonlinear relationships, often overlooked in applied research, are common and warrant attention, as standard linear analyses can lead to misleading conclusions. Nonlinear analyses can yield insights not achievable through linear methods. Throughout the book, various examples illustrate the advantages of modeling nonlinear relationships. The techniques discussed involve fractional polynomials based on real-valued power transformations of primary predictor variables, along with model selection using likelihood cross-validation. The book details adaptive fractional polynomial modeling within standard, logistic, and Poisson regression contexts for continuous, discrete, and count outcomes, both univariate and multivariate. It also compares adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. Customized SAS macros for conducting adaptive regression modeling are provided, with code available on the first author's website and the book’s Springer page. Detailed instructions on using these macros and interpreting their outputs are included, and the methods can be implemented using o