University of Paderborn

About: University of Paderborn is a education organization based out in Paderborn, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Control reconfiguration & Software. The organization has 6684 authors who have published 16929 publications receiving 323154 citations.

Hierarchical Self-Assembly of GaAs=AlGaAs Quantum Dots

Abstract: A novel structure containing self-assembled, unstrained GaAs quantum dots is obtained by combining solid-source molecular beam epitaxy and atomic-layer precise in situ etching. Photo-luminescence (PL) spectroscopy reveals light emission with very narrow inhomogeneous broadening and clearly resolved excited states at high excitation intensity. The dot morphology is determined by scanning probe microscopy and, combined with single band and eight-band k.p theory calculations, is used to interpret PL and single-dot spectra with no adjustable structural parameter.

Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS

Abstract: We use NSGA-III and MOPSO algorithms to solve a multi-objective X-bar control chart design problem.NSGA-III and MOPSO are modified to handle a constrained multi-objective problem with discrete and continuous variables.Four DEA models are proposed to reduce the number of Pareto optimal solutions to a manageable size.TOPSIS is used to prioritize the efficient optimal solutions.Several metrics are used to compare the performance of NSGA-III and MOPSO algorithms. X-bar control charts are widely used to monitor and control business and manufacturing processes. This study considers an X-bar control chart design problem with multiple and often conflicting objectives, including the expected time the process remains in statistical control status, the type-I error, and the detection power. An integrated multi-objective algorithm is proposed for optimizing economical control chart design. We applied multi-objective optimization methods founded on the reference-points-based non-dominated sorting genetic algorithm-II (NSGA-III) and a multi-objective particle swarm optimization (MOPSO) algorithm to efficiently solve the optimization problem. Then, two different multiple criteria decision making (MCDM) methods, including data envelopment analysis (DEA) and the technique for order of preference by similarity to ideal solution (TOPSIS), are used to reduce the number of Pareto optimal solutions to a manageable size. Four DEA methods compare the optimal solutions based on relative efficiency, and then the TOPSIS method ranks the efficient optimal solutions. Several metrics are used to compare the performance of the NSGA-III and MOPSO algorithms. In addition, the DEA and TOPSIS methods are used to compare the performance of NSGA-III and MOPSO. A well-known case study is formulated and solved to demonstrate the applicability and exhibit the efficacy of the proposed optimization algorithm. In addition, several numerical examples are developed to compare the NSGA-III and MOPSO algorithms. Results show that NSGA-III performs better in generating efficient optimal solutions.

The simulation semantics of systemC

Abstract: We present a rigorous but transparent semantics definition of SystemC that covers method, thread, and clocked thread behavior as well as their interaction with the simulation kernel process. The semantics includes watching statements, signal assignment, and wait statements as they are introduced in SystemC V1.O. We present our definition in form of distributed Abstract State Machines (ASMs) rules reflecting the view given in the SystemC User's Manual and the reference implementation. We mainly see our formal semantics as a concise, unambiguous, high-level specification for SystemC-based implementations and for standardization. Additionally, it can be used as a sound basis to investigate SystemC interoperability with Verilog and VHDL.

Effect of microstructure on the fatigue of hot-rolled and cold-drawn NiTi shape memory alloys

Abstract: We present results from a systematic study linking material microstructure to monotonic and fatigue properties of NiTi shape memory alloys We consider Ni-rich materials that are either (1) hot rolled or (2) hot rolled and cold drawn In addition to the two material processing routes, heat treatments are used to systematically alter material microstructure giving rise to a broad range of thermal, monotonic and cyclic properties The strength and hardness of the austenite and martensite phases initially increase with mild heat treatment (300 °C), and subsequently decrease with increased aging temperature above 300 °C This trend is consistent with transmission electron microscopy observed precipitation hardening in the hot-rolled material and precipitation hardening plus recovery and recrystallization in the cold-drawn materials The low-cycle pseudoelastic fatigue properties of the NiTi materials generally improve with increasing material strength, although comparison across the two product forms demonstrates that higher measured flow strength does not assure superior resistance to pseudoelastic cyclic degradation Fatigue crack growth rates in the hot-rolled material are relatively independent of heat treatment and demonstrate similar fatigue crack growth rates to other NiTi product forms; however, the cold-drawn material demonstrates fatigue threshold values some 5 times smaller than the hot-rolled material The difference in the fatigue performance of hot-rolled and cold-drawn NiTi bars is attributed to significant residual stresses in the cold-drawn material, which amplify fatigue susceptibility despite superior measured monotonic properties