Open Access

Forschungsprojekte und ein Werkstoffcoaching für Start-ups sollen innovative KMU fördern. Torsten Bogatzky, operativer Leiter des Instituts für Werkstoffsystemtechnik Thurgau (WITg) in Tägerwilen, und Arnulf Hörtnagl, Leiter Forschungsprojekte, erklären, wohin die Reise geht.

Vortrag beim KLIMOPASS Workshop an der Murg, Rastatt, 30.09.2016

Aktuelle Fachvorträge aus dem internationalen Spektrum des Bauingenieurwesens an der HTWG Konstanz, anlässlich des 100-jährigen Bestehens der Fakultät BI. Themenbereiche: Bauerhaltung und Bausanierung, Geotechnik, Konstruktiver Ingenieurbau, Höhepunkte des Hochbaus im 21. Jahrhundert, Verkehrswesen, Wasserwirtschaft, Wirtschaftsingenieurwesen. Gefördert wurde die Veranstaltung von: ADK Modulraum GmbH ; Architektenkammer BW ; Bauen mit Stahl ; Beton Marketing Süd ; Bund Deutscher Architekten BDA ; Institut Feuerverzinken ; Form TL - Ingenieure für Tragwerk und Leichtbau GmbH ; OTT Ziegel Pfullendorf ; Stahlbauzentrum Schweiz ; Euro Poles Pfleiderer ; Ingenieurk@mmer Baden-Württemberg

Das vorliegende Buch ist entstanden im Rahmen des Forschungsprojekts „Baukultur konkret“ im Forschungsprogramm Experimenteller Wohnungs- und Städtebau (ExWoSt) des Bundesinstituts für Bau-, Stadt- und Raumforschung BBSR im Auftrag des Bundesministeriums für Umwelt, Naturschutz, Bau und Reaktorsicherheit BMUB.

Vor 40 Jahren erschien Frederic Vesters Hauptwerk „Neuland des Denkens“. Der Beitrag beleuchtet die wesentlichen Themen dieses programmatischen Buches im Hinblick auf Vesters Biokybernetik und deren Anwendung auf zahlreiche aktuelle Fragen in der Nachhaltigkeits-Debatte, z.B. Klimawandel-Problematik und Energiewende.

Multi-object tracking filters require a birth density to detect new objects from measurement data. If the initial positions of new objects are unknown, it may be useful to choose an adaptive birth density. In this paper, a circular birth density is proposed, which is placed like a band around the surveillance area. This allows for 360° coverage. The birth density is described in polar coordinates and considers all point-symmetric quantities such as radius, radial velocity and tangential velocity of objects entering the surveillance area. Since it is assumed that these quantities are unknown and may vary between different targets, detected trajectories, and in particular their initial states, are used to estimate the distribution of initial states. The adapted birth density is approximated as a Gaussian mixture, so that it can be used for filters operating on Cartesian coordinates.

This work presents a new concept to implement the elliptic curve point multiplication (PM). This computation is based on a new modular arithmetic over Gaussian integer fields. Gaussian integers are a subset of the complex numbers such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this arithmetic is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of secure hardware implementations, which are robust against attacks. Furthermore, an area-efficient coprocessor design is proposed with an arithmetic unit that enables Montgomery modular arithmetic over Gaussian integers. The proposed architecture and the new arithmetic provide high flexibility, i.e., binary and non-binary key expansions as well as protected and unprotected PM calculations are supported. The proposed coprocessor is a competitive solution for a compact ECC processor suitable for applications in small embedded systems.

One major realm of Condition Based Maintenance is finding features that reflect the current health state of the asset or component under observation. Most of the existing approaches are accompanied with high computational costs during the different feature processing phases making them infeasible in a real-world scenario. In this paper a feature generation method is evaluated compensating for two problems: (1) storing and handling large amounts of data and (2) computational complexity. Both aforementioned problems are existent e.g. when electromagnetic solenoids are artificially aged and health indicators have to be extracted or when multiple identical solenoids have to be monitored. To overcome those problems, Compressed Sensing (CS), a new research field that keeps constantly emerging into new applications, is employed. CS is a data compression technique allowing original signal reconstruction with far fewer samples than Shannon-Nyquist dictates, when some criteria are met. By applying this method to measured solenoid coil current, raw data vectors can be reduced to a way smaller set of samples that yet contain enough information for proper reconstruction. The obtained CS vector is also assumed to contain enough relevant information about solenoid degradation and faults, allowing CS samples to be used as input to fault detection or remaining useful life estimation routines. The paper gives some results demonstrating compression and reconstruction of coil current measurements and outlines the application of CS samples as condition monitoring data by determining deterioration and fault related features. Nevertheless, some unresolved issues regarding information loss during the compression stage, the design of the compression method itself and its influence on diagnostic/prognostic methods exist.

In many industrial applications a workpiece is continuously fed through a heating zone in order to reach a desired temperature to obtain specific material properties. Many examples of such distributed parameter systems exist in heavy industry and also in furniture production such processes can be found. In this paper, a real-time capable model for a heating process with application to industrial furniture production is modeled. As the model is intended to be used in a Model Predictive Control (MPC) application, the main focus is to achieve minimum computational runtime while maintaining a sufficient amount of accuracy. Thus, the governing Partial Differential Equation (PDE) is discretized using finite differences on a grid, specifically tailored to this application. The grid is optimized to yield acceptable accuracy with a minimum number of grid nodes such that a relatively low order model is obtained. Subsequently, an explicit Runge-Kutta ODE (Ordinary Differential Equation) solver of fourth order is compared to the Crank-Nicolson integration scheme presented in Weiss et al. (2022) in terms of runtime and accuracy. Finally, the unknown thermal parameters of the process are estimated using real-world measurement data that was obtained from an experimental setup. The final model yields acceptable accuracy while at the same time shows promising computation time, which enables its use in an MPC controller.