Hans-Dieter Kochs

Adapted variable precision rough set approach for EEG analysis

OBJECTIVE Rough set theory (RST) provides powerful methods for reduction of attributes and creation of decision rules, which have successfully been applied in numerous medical applications. The variable precision rough set model (VPRS model), an extension of the original rough set approach, tolerates some degree of misclassification of the training data. The basic idea of the VPRS model is to change the class information of those objects whose class information cannot be induced without contradiction from the available attributes. Thereafter, original methods of RST are applied. An approach of this model is presented that allows uncertain objects to change class information during the process of attribute reduction and rule generation. This method is referred to as variable precision rough set approach with flexible classification of uncertain objects (VPRS(FC) approach) and needs only slight modifications of the original VPRS model. METHODS AND MATERIAL To compare the VPRS model and VPRS(FC) approach both methods are applied to a clinical data set based on electroencephalogram of awake and anesthetized patients. For comparison, a second data set obtained from the UCI machine learning repository is used. It describes the shape of different vehicle types. Further well known feature selection methods were applied to both data sets to compare their results with the results provided by rough set based approaches. RESULTS The VPRS(FC) approach requires higher computational effort, but is able to achieve better reduction of attributes for noisy or inconsistent data and provides smaller rule sets. CONCLUSION The presented approach is a useful method for substantial attribute reduction in noisy and inconsistent data sets.

Multi-objective optimization of generalized reliability design problems using feature models—A concept for early design stages

Reliability optimization problems such as the redundancy allocation problem (RAP) have been of considerable interest in the past. However, due to the restrictions of the design space formulation, they may not be applicable in all practical design problems. A method with high modelling freedom for rapid design screening is desirable, especially in early design stages. This work presents a novel approach to reliability optimization. Feature modelling, a specification method originating from software engineering, is applied for the fast specification and enumeration of complex design spaces. It is shown how feature models can not only describe arbitrary RAPs but also much more complex design problems. The design screening is accomplished by a multi-objective evolutionary algorithm for probabilistic objectives. Comparing averages or medians may hide the true characteristics of this distributions. Therefore the algorithm uses solely the probability of a system dominating another to achieve the Pareto optimal set. We illustrate the approach by specifying a RAP and a more complex design space and screening them with the evolutionary algorithm.

Automatic design of reliable systems consisting of nano-elements

The design of nano structures is considered a challenge for design methods, which have to cope with much more elements than traditional VLSI. Moreover, such elements will be unreliable due to unavoidable physical quantum effects. The inevitable fault tolerance leads to an additional increase of the design space. An extremely high number of nano-devices can be used for various redundancy schemes and many combinations thereof, thus promising an efficient solution to the reliability problem of nano devices. This paper proposes a heuristic design method based on a specific type of genetic algorithms. It has been adapted to the design of fault-tolerant nano systems with respect to the representation of systems as well as the fitness function and the underlying fault model.