Rule cubes for causal investigations

With the complexity of modern vehicles tremendously increasing, quality engineers play a key role within today’s automotive industry. Field data analysis supports corrective actions in development, production and after sales support. We decompose the requirements and show that association rules, being a popular approach to generating explanative models, still exhibit shortcomings. Interactive rule cubes, which have been proposed recently, are a promising alternative. We extend this work by introducing a way of intuitively visualizing and meaningfully ranking them. Moreover, we present methods to interactively factorize a problem and validate hypotheses by ranking patterns based on expectations, and by browsing a cube-based network of related influences. All this is currently in use as an interactive tool for warranty data analysis in the automotive industry. A real-world case study shows how engineers successfully use it in identifying root causes of quality issues.

Semisimple algebras of almost minimal rank over the reals

A famous lower bound for the bilinear complexity of the multiplication in associative algebras is the Alder–Strassen bound. Algebras for which this bound is tight are called algebras of minimal rank. After 25 years of research, these algebras are now well understood. Here we start the investigation of the algebras for which the Alder–Strassen bound is off by one. As a first result, we completely characterize the semisimple algebras over R$\mathbb{R}$ whose bilinear complexity is by one larger than the Alder–Strassen bound. Furthermore, we characterize all algebras A$A$ (with radical) of minimal rank plus one over R$\mathbb{R}$ for which A/radA$A/radA$ has minimal rank plus one. The other possibility is that A/radA$A/radA$ has minimal rank. For this case, we only present a partial result.     

Where are you going? Using human locomotion models for target estimation

To explore virtual environments that are larger than the available physical tracking space by real walking, it is necessary to use so-called redirected walking. Redirection techniques allow the user to explore an unlimited virtual environment in a limited tracking space by introducing a small mismatch between a user’s real and virtual movement, thus preventing the user from colliding with the physical walls of the tracking space. Steering algorithms are used to select the most suitable redirection technique at any given time, depending on the geometry of the real and virtual environment. Together with prediction of a user’s future walking path, these algorithms select the best redirection strategy by an optimal control scheme. In this paper, a new approach for the prediction of a person’s locomotion target is presented. We use various models of human locomotion together with a set of possible targets to create a set of expected paths. These paths are then compared to the real path the user already traveled to calculate the probability of a certain target being the one the user is heading for. A new approach for comparing paths with each other is introduced and is compared to three others. For describing the human’s path to a given target, four different models are used and compared. To gather data for the comparison of the models against the real path, a user study was conducted. Based on the results of the user study, the paper concludes with a discussion on the prediction performance of the different approaches.     

How the Kano model contributes to Kansei engineering in services

Recent studies show that products and services hold great appeal if they are attractively designed to elicit emotional feelings from customers. Kansei engineering (KE) has good potential to provide a competitive advantage to those able to read and translate customer affect and emotion in actual product and services. This study introduces an integrative framework of the Kano model and KE, applied to services. The Kano model was used and inserted into KE to exhibit the relationship between service attribute performance and customer emotional response. Essentially, the Kano model categorises service attribute quality into three major groups (must-be [M], one-dimensional [O] and attractive [A]). The findings of a case study that involved 100 tourists who stayed in luxury 4- and 5-star hotels are presented. As a practical matter, this research provides insight on which service attributes deserve more attention with regard to their significant impact on customer emotional needs. STATEMENT OF RELEVANCE: Apart from cognitive evaluation, emotions and hedonism play a big role in service encounters. Through a focus on delighting qualities of service attributes, this research enables service providers and managers to establish the extent to which they prioritise their improvement efforts and to always satisfy their customer emotions beyond expectation.     

Two‐Level Grids for Ray Tracing on GPUs

We investigate the use of two‐level nested grids as acceleration structure for ray tracing of dynamic scenes. We propose a massively parallel, sort‐based construction algorithm and show that the two‐level grid is one of the structures that is fastest to construct on modern graphics processors. The structure handles non‐uniform primitive distributions more robustly than the uniform grid and its traversal performance is comparable to those of other high quality acceleration structures used for dynamic scenes. We propose a cost model to determine the grid resolution and improve SIMD utilization during ray‐triangle intersection by employing a hybrid packetization strategy. The build times and ray traversal acceleration provide overall rendering performance superior to previous approaches for real time rendering of animated scenes on GPUs.     

An algorithmic framework for network reconstruction

Models of biological systems and phenomena are of high scientific interest and practical relevance, but not always easy to obtain due to their inherent complexity. To gain the required insight, experimental data are provided and need to be interpreted in terms of models that explain the observed phenomena. In systems biology the framework of Petri nets is often used to describe models for the regulatory mechanisms of biological systems. The aim of this paper is to provide, based on results in Marwan et al. (2008) [1] and Durzinsky et al. (2008) [2], an algorithmic framework for the challenging task of generating all possible Petri nets fitting the given experimental data.     

Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.