Characterization of Cementitious Materials Exposed to Freezing and Thawing in Combination with Deicing Salts Using 3D Scans

Surface deterioration of concrete subjected to freezing and thawing in combination with deicing salts is one of the most important factors determining the durability of concrete infrastructure in cold climates. The freeze–thaw deicing salt (FTDS) resistance of cementitious materials can be determined by the capillary suction of de-icing chemicals and freeze–thaw (CDF) test. Specimens are subjected to repeated freeze–thaw cycles with simultaneous addition of deicing salt and the amount of material scaled off near the surface is determined. For concretes with adequate FTDS resistance, this test method works very well. However, specimens with unknown performance often experience increased edge scaling. This leads to a falsification of results and consequently to an underestimation of the actual freeze–thaw resistance. In materials research, however, concretes with high levels of surface deterioration are studied in order to investigate various factors of influence on the freeze–thaw resistance of concretes in a targeted manner. This article presents a novel methodology that delivers new information regarding surface deterioration of CDF samples using high-resolution 3D scan data. Change of volume is used to support deterioration results of the standard CDF methodology. Increase of surface area is used to estimate change in roughness of samples.     

Ultralow Expansion Glass as Material for Advanced Micromechanical Systems

Ultralow expansion (ULE) glasses are of special interest for temperature stabilized systems for example in precision metrology. Nowadays, ULE materials are mainly used in macroscopic and less in micromechanical systems. Reasons for this are a lack of technologies for parallel fabricating high-quality released microstructures with a high accuracy. As a result, there is a high demand in transferring these materials into miniaturized application examples, realistic system modeling, and the investigation of microscopic material properties. Herein, a technological base for fabricating released micromechanical structures and systems with a structure height above 100 μm in ULE 7972 glass is established. Herein, the main fabrication parameters that are important for the system design and contribute thus to the introduction of titanium silicate as material for glass-based micromechanical systems are discussed. To study the mechanical properties in combination with respective simulation models, microcantilevers are used as basic mechanical elements to evaluate technological parameters and other impact factors. The implemented models allow to predict the micromechanical system properties with a deviation of only ±5% and can thus effectively support the micromechanical system design in an early stage of development.     

A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction

We analyze a variational approach to image segmentation that is based on a strictly convex non-quadratic cost functional. The smoothness term combines a standard first-order measure for image regions with a total-variation based measure for signal transitions. Accordingly, the costs associated with discontinuities are given by the length of level lines and local image contrast. For real images, this provides a reasonable approximation of the variational model of Mumford and Shah that has been suggested as a generic approach to image segmentation.The global properties of the convex variational model are favorable to applications: Uniqueness of the solution, continuous dependence of the solution on both data and parameters, consistent and efficient numerical approximation of the solution with the FEM-method.Various global and local properties of the convex variational model are analyzed and illustrated with numerical examples. Apart from the favorable global properties, the approach is shown to provide a sound mathematical model of a useful locally adaptive smoothing process. A comparison is carried out with results of a region-growing technique related to the Mumford-Shah model.     

Controlling patterns of geospatial phenomena

Modeling spatially distributed phenomena in terms of its controlling factors is a recurring problem in geoscience. Most efforts concentrate on predicting the value of response variable in terms of controlling variables either through a physical model or a regression model. However, many geospatial systems comprises complex, nonlinear, and spatially non-uniform relationships, making it difficult to even formulate a viable model. This paper focuses on spatial partitioning of controlling variables that are attributed to a particular range of a response variable. Thus, the presented method surveys spatially distributed relationships between predictors and response. The method is based on association analysis technique of identifying emerging patterns, which are extended in order to be applied more effectively to geospatial data sets. The outcome of the method is a list of spatial footprints, each characterized by a unique “controlling pattern”—a list of specific values of predictors that locally correlate with a specified value of response variable. Mapping the controlling footprints reveals geographic regionalization of relationship between predictors and response. The data mining underpinnings of the method are given and its application to a real world problem is demonstrated using an expository example focusing on determining variety of environmental associations of high vegetation density across the continental United States.     

Information fusion for automotive applications – An overview

This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fuse information with sensor data from other vehicles or from the road infrastructure as well. This enables vehicles to perceive information from regions that are hardly accessible otherwise and represents the basis for cooperative driving maneuvers. While the Bayesian framework builds the basis for information fusion, automobile environments are characterized by their a priori unknown topology, i.e., the number, type, and structure of the perceived objects is highly variable. Multi-object detection and tracking methods are a first step to cope with this challenge. Obviously, the existence or non-existence of an object is of paramount importance for safe driving. Such decisions are highly influenced by the association step that assigns sensor measurements to object tracks. Methods that involve multiple sequences of binary assignments are compared with soft-assignment strategies. Finally, fusion based on finite set statistics that (theoretically) avoid an explicit association are discussed.     

The physiological mirror—a system for unconscious control of a virtual environment through physiological activity

This paper introduces a system for real-time physiological measurement, analysis, and metaphorical visualization within a virtual environment (VE). Our goal is to develop a method that allows humans to unconsciously relate to parts of an environment more strongly than to others, purely induced by their own physiological responses to the virtual reality (VR) displays. In particular, we exploit heart rate, respiration, and galvanic skin response in order to control the behavior of virtual characters in the VE. Such unconscious processes may become a useful tool for storytelling or assist guiding participants through a sequence of tasks in order to make the application more interesting, e.g., in rehabilitation. We claim that anchoring of subjective bodily states to a virtual reality (VR) can enhance a person’s sense of realism of the VR and ultimately create a stronger relationship between humans and the VR.     

Preserving correctness during business process model configuration

A configurable process model captures a family of related process models in a single artifact. Such models are intended to be configured to fit the requirements of specific organizations or projects, leading to individualized process models that are subsequently used for domain analysis or solution design. This article proposes a formal foundation for individualizing configurable process models incrementally, while preserving correctness, both with respect to syntax and behavioral semantics. Specifically, assuming the configurable process model is behaviorally sound, the individualized process models are guaranteed to be sound. The theory is first developed in the context of Petri nets and then extended to a process modeling notation widely used in practice, namely Event-driven Process Chains.     

Study on the replication quality of micro-structures in the injection molding process with dynamical tool tempering systems

The injection molding of micro-structures is a promising mass-production method for a broad range of materials. However, the replication quality of these structures depends significantly on the heat flow during the filling stage. In this paper, the filling and heat transfer of v-groove and random structures below 5 μm is investigated with the help of an AFM (atomic force microscope) and thermo couples. A numerical model is developed to predict the filling of surface structures during the filling and packing stage. The model implies the use of simple fully developed flow models taking the power-law material model into account. This permits investigation into which ways several processing parameters affect the polymer flow in the surface structures. The mold wall temperature, which has significant effects on the polymer flow, is varied by using a variothermal mold temperature control system to validate the model proposed.